Technical
Core Platform
MCP Servers

MCP Servers

Bridging AI and Business Systems

MCP (Model Context Protocol) Servers act as intelligent translators between AI agents and business systems, transforming complex enterprise APIs into AI-understandable interfaces. They provide secure, scalable, and semantically rich connections that enable AI agents to interact naturally with any business system.

Supabase-Powered MCP Architecture 

  graph TB
    subgraph "AI Agent Layer"
        AGENT1[Sales Agent]
        AGENT2[Support Agent]
        AGENT3[Finance Agent]
        AGENT4[Custom Agent]
    end
    
    subgraph "MCP Gateway with Supabase Integration"
        GATEWAY[MCP Gateway]
        ROUTER[Request Router]
        SAUTH[Supabase Auth]
        REALTIME[Real-time Subscriptions]
        CACHE[Response Cache]
    end
    
    subgraph "Supabase Data Layer"
        POSTGRES[(Supabase PostgreSQL)]
        POSTREST[PostgREST API]
        STORAGE[Supabase Storage]
        EDGE[Edge Functions]
        VECTOR[Vector/Embeddings]
    end
    
    subgraph "MCP Server Ecosystem"
        subgraph "ERP Connectors"
            SAP[SAP MCP Server]
            ORACLE[Oracle MCP Server]
            DYNAMICS[Dynamics 365 MCP]
            NETSUITE[NetSuite MCP Server]
        end
        
        subgraph "CRM Connectors"
            SALESFORCE[Salesforce MCP]
            HUBSPOT[HubSpot MCP]
            PIPEDRIVE[Pipedrive MCP]
        end
        
        subgraph "Accounting Systems"
            QUICKBOOKS[QuickBooks MCP]
            XERO[Xero MCP Server]
            SAGE[Sage MCP Server]
        end
        
        subgraph "Supabase-Native"
            SUPABASE_MCP[Supabase MCP Server]
            ANALYTICS[Analytics MCP]
            VECTOR_SEARCH[Vector Search MCP]
        end
    end
    
    subgraph "Business Systems"
        ERP[(ERP Systems)]
        CRM[(CRM Platforms)]
        ACCOUNTING[(Accounting)]
        CUSTOM[(Custom APIs)]
    end
    
    AGENT1 --> GATEWAY
    AGENT2 --> GATEWAY
    AGENT3 --> GATEWAY
    AGENT4 --> GATEWAY
    
    GATEWAY --> ROUTER
    ROUTER --> SAUTH
    SAUTH --> REALTIME
    REALTIME --> CACHE
    
    CACHE --> SAP
    CACHE --> SALESFORCE
    CACHE --> QUICKBOOKS
    CACHE --> SUPABASE_MCP
    
    SUPABASE_MCP --> POSTGRES
    SUPABASE_MCP --> POSTREST
    SUPABASE_MCP --> STORAGE
    SUPABASE_MCP --> EDGE
    VECTOR_SEARCH --> VECTOR
    
    SAP --> ERP
    SALESFORCE --> CRM
    QUICKBOOKS --> ACCOUNTING
    ANALYTICS --> POSTGRES

MCP Protocol Fundamentals

Core Protocol Specification

The Model Context Protocol defines standardized communication patterns between AI agents and business systems:

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// MCP Protocol Definition
interface MCPServer {
  // Server identification
  info: ServerInfo;
  
  // Available functions/tools
  functions: MCPFunction[];
  
  // Data schemas and types
  schemas: Record<string, JSONSchema>;
  
  // Real-time event subscriptions
  subscriptions: EventSubscription[];
}

interface MCPFunction {
  name: string;
  description: string;
  
  // Input schema (what the AI sends)
  input_schema: JSONSchema;
  
  // Output schema (what the system returns)
  output_schema: JSONSchema;
  
  // Examples for AI understanding
  examples: FunctionExample[];
  
  // Semantic annotations for better AI comprehension
  annotations: SemanticAnnotation[];
}

interface FunctionExample {
  description: string;
  input: any;
  output: any;
  context?: string;
}

// Semantic annotations help AI understand business context
interface SemanticAnnotation {
  type: 'business_entity' | 'workflow' | 'permission' | 'data_sensitivity';
  value: string;
  confidence: number;
}

Protocol Message Flow 

  sequenceDiagram
    participant AI as AI Agent
    participant GW as MCP Gateway
    participant MCP as MCP Server
    participant SYS as Business System
    
    AI->>GW: Function Call Request
    GW->>GW: Validate & Authenticate
    GW->>MCP: Forward Request
    MCP->>MCP: Transform to Native API
    MCP->>SYS: Business System Call
    SYS->>MCP: Native Response
    MCP->>MCP: Transform to MCP Format
    MCP->>GW: MCP Response
    GW->>GW: Cache & Log
    GW->>AI: Structured Response

Supabase Native MCP Server

Supabase MCP Server Implementation

The Supabase MCP Server provides native integration with Supabase’s complete backend-as-a-service platform, offering PostgreSQL database access, real-time subscriptions, storage, and Edge Functions.

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# Supabase MCP Server with comprehensive features
from supabase import create_client, Client
import asyncio
from typing import Dict, List, Optional, Any

class SupabaseMCPServer(MCPServer):
    def __init__(self, config: SupabaseConfig):
        self.supabase: Client = create_client(
            config.url,
            config.anon_key
        )
        self.service_client: Client = create_client(
            config.url,
            config.service_role_key
        )
        self.realtime_channels = {}
        self.functions = self._register_functions()
    
    def _register_functions(self):
        return [
            MCPFunction(
                name="query_table",
                description="Query Supabase table with filters, joins, and aggregations",
                input_schema={
                    "type": "object",
                    "properties": {
                        "table": {"type": "string", "description": "Table name to query"},
                        "select": {"type": "string", "default": "*", "description": "Columns to select"},
                        "filters": {"type": "object", "description": "Filter conditions"},
                        "order": {"type": "string", "description": "Order by clause"},
                        "limit": {"type": "integer", "default": 100, "maximum": 1000},
                        "offset": {"type": "integer", "default": 0}
                    },
                    "required": ["table"]
                },
                output_schema={
                    "type": "object",
                    "properties": {
                        "data": {"type": "array", "items": {"type": "object"}},
                        "count": {"type": "integer"},
                        "error": {"type": "string"}
                    }
                },
                examples=[
                    FunctionExample(
                        description="Get active users with their organizations",
                        input={
                            "table": "user_profiles",
                            "select": "*, organizations(name, plan)",
                            "filters": {"status": "active"},
                            "order": "created_at.desc",
                            "limit": 50
                        },
                        output={
                            "data": [
                                {
                                    "id": "user_123",
                                    "email": "user@company.com",
                                    "status": "active",
                                    "organizations": {"name": "Acme Corp", "plan": "professional"}
                                }
                            ],
                            "count": 50
                        }
                    )
                ]
            ),
            
            MCPFunction(
                name="insert_record",
                description="Insert new record into Supabase table",
                input_schema={
                    "type": "object",
                    "properties": {
                        "table": {"type": "string"},
                        "data": {"type": "object", "description": "Record data to insert"},
                        "upsert": {"type": "boolean", "default": False, "description": "Upsert if record exists"}
                    },
                    "required": ["table", "data"]
                },
                output_schema={
                    "type": "object",
                    "properties": {
                        "data": {"type": "object", "description": "Inserted record"},
                        "error": {"type": "string"}
                    }
                }
            ),
            
            MCPFunction(
                name="update_record",
                description="Update existing record in Supabase table",
                input_schema={
                    "type": "object",
                    "properties": {
                        "table": {"type": "string"},
                        "filters": {"type": "object", "description": "Update conditions"},
                        "data": {"type": "object", "description": "Data to update"}
                    },
                    "required": ["table", "filters", "data"]
                }
            ),
            
            MCPFunction(
                name="subscribe_to_changes",
                description="Subscribe to real-time changes on a table",
                input_schema={
                    "type": "object",
                    "properties": {
                        "table": {"type": "string"},
                        "events": {"type": "array", "items": {"type": "string", "enum": ["INSERT", "UPDATE", "DELETE"]}, "default": ["*"]},
                        "filter": {"type": "string", "description": "SQL filter for subscription"},
                        "callback_url": {"type": "string", "format": "uri", "description": "Webhook URL for real-time events"}
                    },
                    "required": ["table", "callback_url"]
                }
            ),
            
            MCPFunction(
                name="call_edge_function",
                description="Invoke Supabase Edge Function",
                input_schema={
                    "type": "object",
                    "properties": {
                        "function_name": {"type": "string"},
                        "payload": {"type": "object", "description": "Function payload"},
                        "headers": {"type": "object", "description": "Custom headers"}
                    },
                    "required": ["function_name"]
                }
            ),
            
            MCPFunction(
                name="vector_similarity_search",
                description="Perform vector similarity search using pgvector",
                input_schema={
                    "type": "object",
                    "properties": {
                        "table": {"type": "string"},
                        "vector_column": {"type": "string", "default": "embedding"},
                        "query_vector": {"type": "array", "items": {"type": "number"}},
                        "similarity_threshold": {"type": "number", "default": 0.7, "minimum": 0, "maximum": 1},
                        "limit": {"type": "integer", "default": 10, "maximum": 100},
                        "metadata_filters": {"type": "object", "description": "Additional metadata filters"}
                    },
                    "required": ["table", "query_vector"]
                },
                output_schema={
                    "type": "object",
                    "properties": {
                        "matches": {
                            "type": "array",
                            "items": {
                                "type": "object",
                                "properties": {
                                    "id": {"type": "string"},
                                    "similarity": {"type": "number"},
                                    "metadata": {"type": "object"}
                                }
                            }
                        }
                    }
                }
            ),
            
            MCPFunction(
                name="upload_file",
                description="Upload file to Supabase Storage",
                input_schema={
                    "type": "object",
                    "properties": {
                        "bucket": {"type": "string"},
                        "path": {"type": "string", "description": "File path in bucket"},
                        "file_data": {"type": "string", "description": "Base64 encoded file data"},
                        "content_type": {"type": "string"},
                        "upsert": {"type": "boolean", "default": False}
                    },
                    "required": ["bucket", "path", "file_data"]
                }
            )
        ]
    
    async def execute_function(self, function_name: str, parameters: dict):
        """Execute MCP function with Supabase integration"""
        
        try:
            if function_name == "query_table":
                return await self._query_table(parameters)
            elif function_name == "insert_record":
                return await self._insert_record(parameters)
            elif function_name == "update_record":
                return await self._update_record(parameters)
            elif function_name == "subscribe_to_changes":
                return await self._subscribe_to_changes(parameters)
            elif function_name == "call_edge_function":
                return await self._call_edge_function(parameters)
            elif function_name == "vector_similarity_search":
                return await self._vector_similarity_search(parameters)
            elif function_name == "upload_file":
                return await self._upload_file(parameters)
            else:
                raise MCPFunctionNotFoundError(f"Function {function_name} not found")
                
        except Exception as e:
            raise MCPExecutionError(f"Supabase operation failed: {str(e)}")
    
    async def _query_table(self, params):
        """Query Supabase table with advanced features"""
        
        query = self.supabase.table(params['table'])
        
        # Apply select
        if 'select' in params and params['select'] != '*':
            query = query.select(params['select'])
        else:
            query = query.select('*')
        
        # Apply filters
        if 'filters' in params:
            for key, value in params['filters'].items():
                if isinstance(value, dict):
                    # Handle complex filters like {"gte": 100} or {"in": [1,2,3]}
                    for op, val in value.items():
                        if op == 'eq':
                            query = query.eq(key, val)
                        elif op == 'neq':
                            query = query.neq(key, val)
                        elif op == 'gt':
                            query = query.gt(key, val)
                        elif op == 'gte':
                            query = query.gte(key, val)
                        elif op == 'lt':
                            query = query.lt(key, val)
                        elif op == 'lte':
                            query = query.lte(key, val)
                        elif op == 'in':
                            query = query.in_(key, val)
                        elif op == 'like':
                            query = query.like(key, val)
                        elif op == 'ilike':
                            query = query.ilike(key, val)
                else:
                    # Simple equality filter
                    query = query.eq(key, value)
        
        # Apply ordering
        if 'order' in params:
            order_parts = params['order'].split('.')
            column = order_parts[0]
            direction = order_parts[1] if len(order_parts) > 1 else 'asc'
            
            if direction.lower() == 'desc':
                query = query.order(column, desc=True)
            else:
                query = query.order(column, desc=False)
        
        # Apply limit and offset
        if 'limit' in params:
            query = query.limit(params['limit'])
        if 'offset' in params:
            query = query.offset(params['offset'])
        
        # Execute query
        response = query.execute()
        
        return {
            "data": response.data,
            "count": len(response.data) if response.data else 0
        }
    
    async def _insert_record(self, params):
        """Insert record into Supabase table"""
        
        table = self.supabase.table(params['table'])
        
        if params.get('upsert', False):
            response = table.upsert(params['data']).execute()
        else:
            response = table.insert(params['data']).execute()
        
        return {
            "data": response.data[0] if response.data else None
        }
    
    async def _update_record(self, params):
        """Update record in Supabase table"""
        
        query = self.supabase.table(params['table'])
        
        # Apply filters for update condition
        for key, value in params['filters'].items():
            query = query.eq(key, value)
        
        response = query.update(params['data']).execute()
        
        return {
            "data": response.data,
            "count": len(response.data) if response.data else 0
        }
    
    async def _subscribe_to_changes(self, params):
        """Setup real-time subscription to table changes"""
        
        channel_name = f"table-{params['table']}-{hash(str(params))}"
        
        # Create realtime channel
        channel = self.supabase.channel(channel_name)
        
        # Setup postgres changes subscription
        postgres_changes_config = {
            "event": params.get('events', ['*']),
            "schema": "public",
            "table": params['table']
        }
        
        if 'filter' in params:
            postgres_changes_config['filter'] = params['filter']
        
        def handle_postgres_changes(payload):
            """Handle real-time changes and forward to callback URL"""
            asyncio.create_task(self._forward_realtime_event(
                params['callback_url'], 
                payload
            ))
        
        channel.on_postgres_changes(
            **postgres_changes_config,
            callback=handle_postgres_changes
        )
        
        # Subscribe to channel
        channel.subscribe()
        
        # Store channel reference
        self.realtime_channels[channel_name] = channel
        
        return {
            "subscription_id": channel_name,
            "status": "subscribed",
            "message": f"Subscribed to changes on table '{params['table']}'"
        }
    
    async def _call_edge_function(self, params):
        """Call Supabase Edge Function"""
        
        headers = params.get('headers', {})
        payload = params.get('payload')
        
        response = self.supabase.functions.invoke(
            params['function_name'],
            invoke_options={
                "body": payload,
                "headers": headers
            }
        )
        
        return response
    
    async def _vector_similarity_search(self, params):
        """Perform vector similarity search using pgvector"""
        
        # Build RPC call for vector similarity search
        rpc_params = {
            "query_embedding": params['query_vector'],
            "match_threshold": params.get('similarity_threshold', 0.7),
            "match_count": params.get('limit', 10)
        }
        
        # Add metadata filters if provided
        if 'metadata_filters' in params:
            rpc_params['metadata_filter'] = params['metadata_filters']
        
        # Call stored procedure for vector search
        response = self.supabase.rpc(
            'match_documents',  # Assumes a stored procedure exists
            rpc_params
        ).execute()
        
        return {
            "matches": response.data or []
        }
    
    async def _upload_file(self, params):
        """Upload file to Supabase Storage"""
        
        import base64
        
        # Decode base64 file data
        file_bytes = base64.b64decode(params['file_data'])
        
        # Upload to storage
        response = self.supabase.storage.from_(params['bucket']).upload(
            path=params['path'],
            file=file_bytes,
            file_options={
                "content-type": params.get('content_type', 'application/octet-stream'),
                "upsert": params.get('upsert', False)
            }
        )
        
        if response.get('error'):
            raise Exception(response['error']['message'])
        
        # Get public URL
        public_url = self.supabase.storage.from_(params['bucket']).get_public_url(params['path'])
        
        return {
            "path": response['path'],
            "public_url": public_url,
            "size": len(file_bytes)
        }
    
    async def _forward_realtime_event(self, callback_url: str, payload: dict):
        """Forward real-time event to callback URL"""
        
        import aiohttp
        
        try:
            async with aiohttp.ClientSession() as session:
                await session.post(
                    callback_url,
                    json={
                        "event": "supabase_realtime",
                        "table": payload.get('table'),
                        "eventType": payload.get('eventType'),
                        "new": payload.get('new'),
                        "old": payload.get('old'),
                        "timestamp": payload.get('ts')
                    },
                    headers={"content-type": "application/json"}
                )
        except Exception as e:
            print(f"Failed to forward realtime event: {str(e)}")

Supabase Real-time Subscriptions

Real-time capabilities are a core strength of Supabase, enabling AI agents to respond immediately to data changes.

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# Real-time subscription examples for AI agents
class SupabaseRealtimeIntegration:
    def __init__(self, supabase_client):
        self.supabase = supabase_client
        self.active_subscriptions = {}
    
    async def setup_agent_subscriptions(self, agent_id: str):
        """Setup real-time subscriptions for an AI agent"""
        
        subscriptions = [
            # Monitor user activities for sales agents
            {
                "table": "user_activities",
                "events": ["INSERT"],
                "filter": f"agent_id=eq.{agent_id}",
                "handler": self._handle_user_activity
            },
            
            # Monitor support tickets for support agents  
            {
                "table": "support_tickets",
                "events": ["INSERT", "UPDATE"],
                "filter": f"assigned_agent_id=eq.{agent_id}",
                "handler": self._handle_ticket_update
            },
            
            # Monitor system alerts
            {
                "table": "system_alerts",
                "events": ["INSERT"],
                "filter": "severity=eq.high",
                "handler": self._handle_system_alert
            }
        ]
        
        for sub in subscriptions:
            await self._create_subscription(agent_id, sub)
    
    async def _create_subscription(self, agent_id: str, config: dict):
        """Create a real-time subscription"""
        
        channel_name = f"agent-{agent_id}-{config['table']}"
        channel = self.supabase.channel(channel_name)
        
        channel.on_postgres_changes(
            event=config['events'],
            schema="public",
            table=config['table'],
            filter=config.get('filter'),
            callback=lambda payload: asyncio.create_task(
                config['handler'](agent_id, payload)
            )
        )
        
        channel.subscribe()
        self.active_subscriptions[channel_name] = channel
        
        return channel_name
    
    async def _handle_user_activity(self, agent_id: str, payload: dict):
        """Handle user activity events for sales agents"""
        
        activity = payload.get('new', {})
        
        # Trigger agent workflow based on activity type
        if activity.get('type') == 'page_view':
            await self._trigger_agent_workflow(agent_id, 'page_view_detected', activity)
        elif activity.get('type') == 'form_submission':
            await self._trigger_agent_workflow(agent_id, 'lead_conversion', activity)
        elif activity.get('type') == 'email_open':
            await self._trigger_agent_workflow(agent_id, 'email_engagement', activity)
    
    async def _handle_ticket_update(self, agent_id: str, payload: dict):
        """Handle support ticket updates"""
        
        ticket = payload.get('new', {})
        old_ticket = payload.get('old', {})
        
        # Check for priority changes
        if ticket.get('priority') != old_ticket.get('priority'):
            await self._trigger_agent_workflow(
                agent_id, 
                'priority_changed', 
                {
                    'ticket_id': ticket.get('id'),
                    'old_priority': old_ticket.get('priority'),
                    'new_priority': ticket.get('priority')
                }
            )
        
        # Check for new messages
        if payload.get('eventType') == 'INSERT':
            await self._trigger_agent_workflow(
                agent_id,
                'new_ticket_assigned',
                ticket
            )
    
    async def _handle_system_alert(self, agent_id: str, payload: dict):
        """Handle system alerts"""
        
        alert = payload.get('new', {})
        
        await self._trigger_agent_workflow(
            agent_id,
            'system_alert',
            {
                'alert_type': alert.get('type'),
                'severity': alert.get('severity'),
                'message': alert.get('message'),
                'requires_action': alert.get('requires_immediate_action', False)
            }
        )
    
    async def _trigger_agent_workflow(self, agent_id: str, event_type: str, data: dict):
        """Trigger AI agent workflow based on real-time events"""
        
        # This would integrate with your agent execution system
        workflow_payload = {
            "agent_id": agent_id,
            "trigger": event_type,
            "data": data,
            "timestamp": "now()",
            "source": "supabase_realtime"
        }
        
        # Queue workflow execution
        await self._queue_agent_workflow(workflow_payload)
    
    async def _queue_agent_workflow(self, payload: dict):
        """Queue agent workflow for execution"""
        
        # Insert into agent_workflows table for processing
        response = self.supabase.table('agent_workflows').insert({
            "agent_id": payload["agent_id"],
            "trigger_type": payload["trigger"],
            "payload": payload["data"],
            "status": "queued",
            "created_at": "now()"
        }).execute()
        
        return response

Enterprise ERP Connectors

SAP Integration 

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# SAP MCP Server Implementation
class SAPMCPServer(MCPServer):
    def __init__(self, config: SAPConfig):
        self.sap_client = SAPClient(
            host=config.host,
            client=config.client,
            username=config.username,
            password=config.password
        )
        self.functions = self._register_functions()
    
    def _register_functions(self):
        return [
            MCPFunction(
                name="get_customer_info",
                description="Retrieve customer information from SAP",
                input_schema={
                    "type": "object",
                    "properties": {
                        "customer_id": {"type": "string", "description": "SAP customer number"},
                        "fields": {"type": "array", "items": {"type": "string"}, "default": ["*"]}
                    },
                    "required": ["customer_id"]
                },
                output_schema={
                    "type": "object",
                    "properties": {
                        "customer_id": {"type": "string"},
                        "name": {"type": "string"},
                        "address": {"type": "object"},
                        "credit_limit": {"type": "number"},
                        "payment_terms": {"type": "string"},
                        "last_order_date": {"type": "string", "format": "date"}
                    }
                },
                examples=[
                    FunctionExample(
                        description="Get basic customer information",
                        input={"customer_id": "CUST001"},
                        output={
                            "customer_id": "CUST001",
                            "name": "Acme Corporation", 
                            "credit_limit": 100000,
                            "payment_terms": "NET30"
                        }
                    )
                ],
                annotations=[
                    SemanticAnnotation(
                        type="business_entity",
                        value="customer",
                        confidence=0.95
                    ),
                    SemanticAnnotation(
                        type="data_sensitivity", 
                        value="confidential",
                        confidence=0.9
                    )
                ]
            ),
            
            MCPFunction(
                name="create_sales_order",
                description="Create a new sales order in SAP",
                input_schema={
                    "type": "object",
                    "properties": {
                        "customer_id": {"type": "string"},
                        "items": {
                            "type": "array",
                            "items": {
                                "type": "object",
                                "properties": {
                                    "material": {"type": "string"},
                                    "quantity": {"type": "number"},
                                    "price": {"type": "number"}
                                }
                            }
                        },
                        "delivery_date": {"type": "string", "format": "date"}
                    },
                    "required": ["customer_id", "items"]
                },
                output_schema={
                    "type": "object", 
                    "properties": {
                        "order_number": {"type": "string"},
                        "total_amount": {"type": "number"},
                        "status": {"type": "string"},
                        "estimated_delivery": {"type": "string", "format": "date"}
                    }
                },
                examples=[
                    FunctionExample(
                        description="Create order for office supplies",
                        input={
                            "customer_id": "CUST001",
                            "items": [
                                {"material": "PAPER_A4", "quantity": 100, "price": 5.99}
                            ]
                        },
                        output={
                            "order_number": "SO123456",
                            "total_amount": 599.00,
                            "status": "confirmed",
                            "estimated_delivery": "2025-09-01"
                        }
                    )
                ]
            )
        ]
    
    async def execute_function(self, function_name: str, parameters: dict):
        """Execute MCP function and return structured result"""
        
        if function_name == "get_customer_info":
            return await self._get_customer_info(parameters)
        elif function_name == "create_sales_order":
            return await self._create_sales_order(parameters)
        else:
            raise MCPFunctionNotFoundError(f"Function {function_name} not found")
    
    async def _get_customer_info(self, params):
        """Retrieve customer info from SAP"""
        try:
            # Call SAP RFC function
            result = await self.sap_client.call_function(
                'BAPI_CUSTOMER_GETDETAIL2',
                CUSTOMERNO=params['customer_id']
            )
            
            # Transform SAP response to MCP format
            return {
                "customer_id": result['CUSTOMERNO'],
                "name": result['CUSTOMERDETAIL']['NAME'],
                "address": {
                    "street": result['CUSTOMERDETAIL']['STREET'],
                    "city": result['CUSTOMERDETAIL']['CITY'],
                    "postal_code": result['CUSTOMERDETAIL']['POSTL_COD1'],
                    "country": result['CUSTOMERDETAIL']['COUNTRY']
                },
                "credit_limit": float(result['CUSTOMERDETAIL']['CREDIT_LIM']),
                "payment_terms": result['CUSTOMERDETAIL']['PMNTTRMS']
            }
            
        except SAPException as e:
            raise MCPExecutionError(f"SAP error: {e}")

Oracle ERP Integration 

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# Oracle ERP MCP Server
class OracleERPMCPServer(MCPServer):
    def __init__(self, config: OracleConfig):
        self.oracle_client = OracleClient(
            host=config.host,
            port=config.port,
            service_name=config.service_name,
            username=config.username,
            password=config.password
        )
        
    def _register_functions(self):
        return [
            MCPFunction(
                name="get_gl_balance",
                description="Get General Ledger account balance",
                input_schema={
                    "type": "object",
                    "properties": {
                        "account_code": {"type": "string"},
                        "period": {"type": "string", "format": "YYYY-MM"},
                        "currency": {"type": "string", "default": "USD"}
                    },
                    "required": ["account_code", "period"]
                },
                output_schema={
                    "type": "object",
                    "properties": {
                        "account_code": {"type": "string"},
                        "account_name": {"type": "string"},
                        "balance": {"type": "number"},
                        "currency": {"type": "string"},
                        "period": {"type": "string"},
                        "last_updated": {"type": "string", "format": "date-time"}
                    }
                }
            ),
            
            MCPFunction(
                name="create_purchase_requisition",
                description="Create purchase requisition in Oracle",
                input_schema={
                    "type": "object",
                    "properties": {
                        "items": {
                            "type": "array",
                            "items": {
                                "type": "object",
                                "properties": {
                                    "item_code": {"type": "string"},
                                    "description": {"type": "string"},
                                    "quantity": {"type": "number"},
                                    "unit_price": {"type": "number"},
                                    "department": {"type": "string"}
                                },
                                "required": ["item_code", "quantity", "department"]
                            }
                        },
                        "justification": {"type": "string"},
                        "requested_by": {"type": "string"},
                        "needed_by": {"type": "string", "format": "date"}
                    },
                    "required": ["items", "requested_by"]
                }
            )
        ]
    
    async def _get_gl_balance(self, params):
        """Query Oracle GL for account balance"""
        query = """
        SELECT 
            gcc.segment1 as account_code,
            gcc.description as account_name,
            SUM(gjl.accounted_dr - gjl.accounted_cr) as balance,
            gjh.currency_code,
            TO_CHAR(gjh.default_effective_date, 'YYYY-MM') as period
        FROM 
            gl_je_headers gjh,
            gl_je_lines gjl,
            gl_code_combinations gcc
        WHERE 
            gjh.je_header_id = gjl.je_header_id
            AND gjl.code_combination_id = gcc.code_combination_id
            AND gcc.segment1 = :account_code
            AND TO_CHAR(gjh.default_effective_date, 'YYYY-MM') = :period
        GROUP BY 
            gcc.segment1, gcc.description, gjh.currency_code, 
            TO_CHAR(gjh.default_effective_date, 'YYYY-MM')
        """
        
        result = await self.oracle_client.execute_query(
            query,
            account_code=params['account_code'],
            period=params['period']
        )
        
        if result:
            return {
                "account_code": result[0]['ACCOUNT_CODE'],
                "account_name": result[0]['ACCOUNT_NAME'], 
                "balance": float(result[0]['BALANCE']),
                "currency": params.get('currency', 'USD'),
                "period": params['period'],
                "last_updated": datetime.now().isoformat()
            }
        else:
            raise MCPExecutionError("Account not found or no data for period")

Microsoft Dynamics 365 Integration 

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# Dynamics 365 MCP Server Configuration
dynamics365:
  server_info:
    name: "Microsoft Dynamics 365 MCP Server"
    version: "1.0.0"
    description: "Connect AI agents to Dynamics 365 Business Central and Finance & Operations"
    
  connection:
    tenant_id: "${DYNAMICS_TENANT_ID}"
    client_id: "${DYNAMICS_CLIENT_ID}"
    client_secret: "${DYNAMICS_CLIENT_SECRET}"
    resource_url: "https://your-tenant.crm.dynamics.com"
    
  functions:
    - name: "get_contact"
      endpoint: "contacts"
      method: "GET"
      description: "Retrieve contact information from Dynamics 365"
      
    - name: "create_opportunity"
      endpoint: "opportunities"
      method: "POST"
      description: "Create new sales opportunity"
      
    - name: "get_account_summary"
      endpoint: "accounts"
      method: "GET"
      description: "Get account summary with related opportunities and activities"
      
  authentication:
    type: "oauth2"
    refresh_token_automatically: true
    
  rate_limiting:
    requests_per_minute: 100
    burst_limit: 20

CRM Platform Connectors

Salesforce Integration 

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# Salesforce MCP Server with advanced features
class SalesforceMCPServer(MCPServer):
    def __init__(self, config: SalesforceConfig):
        self.sf_client = SalesforceClient(
            username=config.username,
            password=config.password,
            security_token=config.security_token,
            domain=config.domain,
            version=config.api_version or "54.0"
        )
        self.metadata_cache = {}
        
    def _register_functions(self):
        return [
            MCPFunction(
                name="query_records",
                description="Query Salesforce records using SOQL",
                input_schema={
                    "type": "object",
                    "properties": {
                        "object_type": {"type": "string", "description": "Salesforce object API name"},
                        "fields": {"type": "array", "items": {"type": "string"}},
                        "conditions": {"type": "string", "description": "WHERE clause conditions"},
                        "limit": {"type": "integer", "default": 100, "maximum": 2000}
                    },
                    "required": ["object_type"]
                },
                output_schema={
                    "type": "object",
                    "properties": {
                        "total_size": {"type": "integer"},
                        "records": {"type": "array", "items": {"type": "object"}},
                        "done": {"type": "boolean"}
                    }
                },
                examples=[
                    FunctionExample(
                        description="Find all open opportunities for a specific account",
                        input={
                            "object_type": "Opportunity",
                            "fields": ["Name", "Amount", "CloseDate", "StageName"],
                            "conditions": "AccountId = '001XX000003DHPi' AND IsClosed = false"
                        },
                        output={
                            "total_size": 5,
                            "records": [
                                {
                                    "Name": "Enterprise License Renewal",
                                    "Amount": 150000,
                                    "CloseDate": "2025-09-30",
                                    "StageName": "Proposal/Price Quote"
                                }
                            ],
                            "done": True
                        }
                    )
                ]
            ),
            
            MCPFunction(
                name="create_record",
                description="Create a new record in Salesforce",
                input_schema={
                    "type": "object",
                    "properties": {
                        "object_type": {"type": "string"},
                        "fields": {"type": "object", "description": "Field name-value pairs"}
                    },
                    "required": ["object_type", "fields"]
                },
                output_schema={
                    "type": "object",
                    "properties": {
                        "id": {"type": "string", "description": "Salesforce record ID"},
                        "success": {"type": "boolean"},
                        "errors": {"type": "array", "items": {"type": "string"}}
                    }
                }
            ),
            
            MCPFunction(
                name="intelligent_lead_scoring",
                description="AI-enhanced lead scoring using Salesforce data",
                input_schema={
                    "type": "object",
                    "properties": {
                        "lead_id": {"type": "string"},
                        "additional_context": {"type": "object"}
                    },
                    "required": ["lead_id"]
                },
                output_schema={
                    "type": "object",
                    "properties": {
                        "score": {"type": "integer", "minimum": 0, "maximum": 100},
                        "factors": {"type": "array", "items": {"type": "object"}},
                        "recommendations": {"type": "array", "items": {"type": "string"}},
                        "confidence": {"type": "number", "minimum": 0, "maximum": 1}
                    }
                }
            )
        ]
    
    async def _intelligent_lead_scoring(self, params):
        """Advanced lead scoring with AI enhancement"""
        
        # Get lead data from Salesforce
        lead_data = await self.sf_client.get_record('Lead', params['lead_id'])
        
        # Gather related data
        related_data = await self._gather_lead_context(lead_data)
        
        # Apply ML scoring model
        scoring_features = self._extract_scoring_features(lead_data, related_data)
        ml_score = await self._calculate_ml_score(scoring_features)
        
        # Generate explainable factors
        factors = await self._generate_scoring_factors(scoring_features, ml_score)
        
        # Create actionable recommendations
        recommendations = await self._generate_recommendations(lead_data, ml_score, factors)
        
        return {
            "score": int(ml_score.score),
            "factors": factors,
            "recommendations": recommendations,
            "confidence": ml_score.confidence
        }
    
    async def _gather_lead_context(self, lead_data):
        """Gather contextual data for lead scoring"""
        
        context = {}
        
        # Company information
        if lead_data.get('Company'):
            context['company'] = await self._enrich_company_data(lead_data['Company'])
        
        # Industry and market data
        if lead_data.get('Industry'):
            context['industry_trends'] = await self._get_industry_trends(lead_data['Industry'])
        
        # Similar customer analysis
        context['similar_customers'] = await self._find_similar_customers(lead_data)
        
        # Engagement history
        context['activities'] = await self._get_lead_activities(lead_data['Id'])
        
        return context
    
    def _extract_scoring_features(self, lead_data, context):
        """Extract features for ML scoring"""
        
        return {
            # Lead characteristics
            'title_seniority': self._categorize_title_seniority(lead_data.get('Title', '')),
            'company_size': self._estimate_company_size(context.get('company', {})),
            'industry_growth': context.get('industry_trends', {}).get('growth_rate', 0),
            
            # Engagement features
            'email_opens': context.get('activities', {}).get('email_opens', 0),
            'website_visits': context.get('activities', {}).get('website_visits', 0),
            'content_downloads': context.get('activities', {}).get('content_downloads', 0),
            
            # Behavioral features
            'response_time': context.get('activities', {}).get('avg_response_time', 0),
            'engagement_score': self._calculate_engagement_score(context.get('activities', {})),
            
            # Company features
            'company_revenue': context.get('company', {}).get('estimated_revenue', 0),
            'company_growth': context.get('company', {}).get('growth_rate', 0),
            'technology_stack': context.get('company', {}).get('technologies', []),
            
            # Similarity features
            'similar_customer_count': len(context.get('similar_customers', [])),
            'similar_customer_success_rate': self._calculate_success_rate(context.get('similar_customers', []))
        }

HubSpot Integration 

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# HubSpot MCP Server with workflow automation
class HubSpotMCPServer(MCPServer):
    def __init__(self, config: HubSpotConfig):
        self.hubspot_client = HubSpotClient(api_key=config.api_key)
        
    def _register_functions(self):
        return [
            MCPFunction(
                name="get_contact_timeline",
                description="Get complete timeline of contact interactions",
                input_schema={
                    "type": "object",
                    "properties": {
                        "contact_id": {"type": "string"},
                        "include_activities": {"type": "boolean", "default": True},
                        "date_range": {
                            "type": "object",
                            "properties": {
                                "start_date": {"type": "string", "format": "date"},
                                "end_date": {"type": "string", "format": "date"}
                            }
                        }
                    },
                    "required": ["contact_id"]
                }
            ),
            
            MCPFunction(
                name="trigger_workflow",
                description="Trigger HubSpot workflow for a contact or company",
                input_schema={
                    "type": "object",
                    "properties": {
                        "workflow_id": {"type": "string"},
                        "object_type": {"type": "string", "enum": ["contact", "company", "deal"]},
                        "object_id": {"type": "string"}
                    },
                    "required": ["workflow_id", "object_type", "object_id"]
                }
            ),
            
            MCPFunction(
                name="analyze_campaign_performance",
                description="Analyze email campaign performance with AI insights",
                input_schema={
                    "type": "object",
                    "properties": {
                        "campaign_id": {"type": "string"},
                        "comparison_period": {"type": "string", "enum": ["previous_campaign", "previous_month", "industry_benchmark"]}
                    },
                    "required": ["campaign_id"]
                }
            )
        ]
    
    async def _analyze_campaign_performance(self, params):
        """AI-powered campaign performance analysis"""
        
        # Get campaign data
        campaign = await self.hubspot_client.get_campaign(params['campaign_id'])
        campaign_stats = await self.hubspot_client.get_campaign_stats(params['campaign_id'])
        
        # Get comparison data
        comparison_data = await self._get_comparison_data(params['campaign_id'], params.get('comparison_period'))
        
        # AI analysis
        insights = await self._generate_campaign_insights(campaign, campaign_stats, comparison_data)
        
        # Generate recommendations
        recommendations = await self._generate_campaign_recommendations(insights)
        
        return {
            "campaign_performance": {
                "open_rate": campaign_stats.get('open_rate', 0),
                "click_rate": campaign_stats.get('click_rate', 0),
                "conversion_rate": campaign_stats.get('conversion_rate', 0),
                "unsubscribe_rate": campaign_stats.get('unsubscribe_rate', 0)
            },
            "comparison": comparison_data,
            "insights": insights,
            "recommendations": recommendations,
            "performance_score": self._calculate_performance_score(campaign_stats, comparison_data)
        }

Accounting System Connectors

QuickBooks Integration 

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# QuickBooks MCP Server with financial intelligence
class QuickBooksMCPServer(MCPServer):
    def __init__(self, config: QuickBooksConfig):
        self.qb_client = QuickBooksClient(
            client_id=config.client_id,
            client_secret=config.client_secret,
            access_token=config.access_token,
            refresh_token=config.refresh_token,
            company_id=config.company_id
        )
        
    def _register_functions(self):
        return [
            MCPFunction(
                name="get_financial_summary", 
                description="Get comprehensive financial summary for a period",
                input_schema={
                    "type": "object",
                    "properties": {
                        "period": {"type": "string", "enum": ["current_month", "current_quarter", "current_year", "custom"]},
                        "start_date": {"type": "string", "format": "date"},
                        "end_date": {"type": "string", "format": "date"},
                        "include_comparisons": {"type": "boolean", "default": True}
                    },
                    "required": ["period"]
                },
                output_schema={
                    "type": "object",
                    "properties": {
                        "revenue": {"type": "number"},
                        "expenses": {"type": "number"},
                        "net_income": {"type": "number"},
                        "cash_flow": {"type": "object"},
                        "accounts_receivable": {"type": "number"},
                        "accounts_payable": {"type": "number"},
                        "comparisons": {"type": "object"}
                    }
                }
            ),
            
            MCPFunction(
                name="create_invoice",
                description="Create invoice with intelligent recommendations",
                input_schema={
                    "type": "object",
                    "properties": {
                        "customer_id": {"type": "string"},
                        "line_items": {
                            "type": "array",
                            "items": {
                                "type": "object",
                                "properties": {
                                    "description": {"type": "string"},
                                    "quantity": {"type": "number"},
                                    "rate": {"type": "number"},
                                    "item_ref": {"type": "string"}
                                }
                            }
                        },
                        "due_date": {"type": "string", "format": "date"},
                        "payment_terms": {"type": "string"}
                    },
                    "required": ["customer_id", "line_items"]
                }
            ),
            
            MCPFunction(
                name="cash_flow_forecast",
                description="AI-powered cash flow forecasting",
                input_schema={
                    "type": "object",
                    "properties": {
                        "forecast_months": {"type": "integer", "default": 6, "maximum": 12},
                        "scenario": {"type": "string", "enum": ["conservative", "realistic", "optimistic"]},
                        "include_pending_invoices": {"type": "boolean", "default": True}
                    }
                }
            )
        ]
    
    async def _cash_flow_forecast(self, params):
        """AI-powered cash flow forecasting"""
        
        # Get historical financial data
        historical_data = await self._get_historical_cash_flow(months=24)
        
        # Get pending invoices and bills
        pending_receivables = await self.qb_client.get_pending_invoices()
        pending_payables = await self.qb_client.get_pending_bills()
        
        # Get recurring transactions
        recurring_income = await self.qb_client.get_recurring_transactions('income')
        recurring_expenses = await self.qb_client.get_recurring_transactions('expense')
        
        # Apply ML forecasting model
        forecast = await self._generate_ml_forecast(
            historical_data=historical_data,
            pending_receivables=pending_receivables,
            pending_payables=pending_payables,
            recurring_transactions={
                'income': recurring_income,
                'expenses': recurring_expenses
            },
            forecast_months=params.get('forecast_months', 6),
            scenario=params.get('scenario', 'realistic')
        )
        
        # Generate insights and recommendations
        insights = await self._analyze_forecast_trends(forecast)
        recommendations = await self._generate_cash_flow_recommendations(forecast, insights)
        
        return {
            "forecast_period": f"{params.get('forecast_months', 6)} months",
            "scenario": params.get('scenario', 'realistic'),
            "monthly_projections": forecast.monthly_projections,
            "key_metrics": {
                "projected_cash_position": forecast.final_cash_position,
                "lowest_cash_month": forecast.lowest_cash_month,
                "cash_runway_months": forecast.cash_runway_months
            },
            "insights": insights,
            "recommendations": recommendations,
            "confidence_level": forecast.confidence_score
        }

Xero Integration 

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# Xero MCP Server with advanced reporting
class XeroMCPServer(MCPServer):
    def __init__(self, config: XeroConfig):
        self.xero_client = XeroClient(
            client_id=config.client_id,
            client_secret=config.client_secret,
            tenant_id=config.tenant_id
        )
        
    def _register_functions(self):
        return [
            MCPFunction(
                name="generate_management_report",
                description="Generate comprehensive management report with KPIs",
                input_schema={
                    "type": "object",
                    "properties": {
                        "report_type": {"type": "string", "enum": ["monthly", "quarterly", "annual"]},
                        "period_end": {"type": "string", "format": "date"},
                        "include_budget_variance": {"type": "boolean", "default": True},
                        "include_kpi_analysis": {"type": "boolean", "default": True}
                    },
                    "required": ["report_type", "period_end"]
                }
            ),
            
            MCPFunction(
                name="expense_categorization", 
                description="AI-powered expense categorization and analysis",
                input_schema={
                    "type": "object",
                    "properties": {
                        "date_range": {
                            "type": "object",
                            "properties": {
                                "start_date": {"type": "string", "format": "date"},
                                "end_date": {"type": "string", "format": "date"}
                            },
                            "required": ["start_date", "end_date"]
                        },
                        "analyze_anomalies": {"type": "boolean", "default": True},
                        "suggest_optimizations": {"type": "boolean", "default": True}
                    },
                    "required": ["date_range"]
                }
            )
        ]
    
    async def _expense_categorization(self, params):
        """AI-powered expense analysis and optimization"""
        
        # Get expense data
        expenses = await self.xero_client.get_bank_transactions(
            where=f"Type=='SPEND' AND Date>=datetime'{params['date_range']['start_date']}' AND Date<=datetime'{params['date_range']['end_date']}'"
        )
        
        # Categorize expenses using AI
        categorized_expenses = await self._ai_categorize_expenses(expenses)
        
        # Detect anomalies if requested
        anomalies = []
        if params.get('analyze_anomalies', True):
            anomalies = await self._detect_expense_anomalies(categorized_expenses)
        
        # Generate optimization suggestions
        optimizations = []
        if params.get('suggest_optimizations', True):
            optimizations = await self._suggest_expense_optimizations(categorized_expenses)
        
        # Calculate spending patterns
        patterns = await self._analyze_spending_patterns(categorized_expenses)
        
        return {
            "total_expenses": sum(exp.amount for exp in categorized_expenses),
            "category_breakdown": self._calculate_category_breakdown(categorized_expenses),
            "spending_patterns": patterns,
            "anomalies": anomalies,
            "optimization_opportunities": optimizations,
            "ai_insights": await self._generate_expense_insights(categorized_expenses)
        }

Custom API & System Connectors

Generic REST API MCP Server 

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# Generic REST API MCP Server with intelligent adaptation
class GenericAPIMCPServer(MCPServer):
    def __init__(self, config: APIConfig):
        self.base_url = config.base_url
        self.auth = config.authentication
        self.rate_limiter = RateLimiter(config.rate_limits)
        self.schema_discoverer = APISchemaDiscoverer()
        
    async def discover_api_schema(self):
        """Automatically discover API schema and generate functions"""
        
        # Try common API discovery methods
        schema = None
        
        # 1. OpenAPI/Swagger specification
        try:
            schema = await self._discover_openapi_schema()
        except Exception:
            pass
        
        # 2. GraphQL introspection
        if not schema:
            try:
                schema = await self._discover_graphql_schema()
            except Exception:
                pass
        
        # 3. AI-powered endpoint discovery
        if not schema:
            schema = await self._ai_discover_endpoints()
        
        # Generate MCP functions from schema
        if schema:
            self.functions = await self._generate_functions_from_schema(schema)
        
        return schema
    
    async def _ai_discover_endpoints(self):
        """Use AI to discover API endpoints and their schemas"""
        
        # Get common endpoint patterns
        test_endpoints = [
            '/api/users', '/api/v1/users', '/users',
            '/api/orders', '/api/v1/orders', '/orders',
            '/api/products', '/api/v1/products', '/products',
            '/api/health', '/health', '/status'
        ]
        
        discovered_endpoints = []
        
        for endpoint in test_endpoints:
            try:
                # Test endpoint with GET request
                response = await self._safe_api_call('GET', endpoint)
                if response.status_code == 200:
                    
                    # Analyze response structure
                    schema = await self._infer_schema_from_response(response.json())
                    
                    discovered_endpoints.append({
                        'path': endpoint,
                        'method': 'GET',
                        'response_schema': schema,
                        'description': await self._ai_generate_description(endpoint, schema)
                    })
                    
            except Exception:
                continue
        
        return {
            'endpoints': discovered_endpoints,
            'base_url': self.base_url,
            'discovery_method': 'ai_exploration'
        }
    
    async def _generate_functions_from_schema(self, schema):
        """Generate MCP functions from discovered API schema"""
        
        functions = []
        
        for endpoint in schema['endpoints']:
            function_name = self._generate_function_name(endpoint['path'], endpoint['method'])
            
            function = MCPFunction(
                name=function_name,
                description=endpoint.get('description', f"{endpoint['method']} {endpoint['path']}"),
                input_schema=self._generate_input_schema(endpoint),
                output_schema=endpoint.get('response_schema', {"type": "object"}),
                examples=await self._generate_function_examples(endpoint)
            )
            
            functions.append(function)
        
        return functions
    
    def _generate_function_name(self, path, method):
        """Generate descriptive function name from API path and method"""
        
        # Clean up path
        clean_path = path.strip('/').replace('/', '_').replace('-', '_')
        
        # Add method prefix
        method_prefixes = {
            'GET': 'get',
            'POST': 'create',
            'PUT': 'update', 
            'DELETE': 'delete',
            'PATCH': 'modify'
        }
        
        prefix = method_prefixes.get(method, method.lower())
        
        return f"{prefix}_{clean_path}".replace('api_', '').replace('v1_', '')

# Database MCP Server
class DatabaseMCPServer(MCPServer):
    def __init__(self, config: DatabaseConfig):
        self.db_client = DatabaseClient(
            host=config.host,
            database=config.database,
            username=config.username,
            password=config.password,
            port=config.port
        )
        self.query_optimizer = QueryOptimizer()
        self.security_validator = QuerySecurityValidator()
        
    def _register_functions(self):
        return [
            MCPFunction(
                name="natural_language_query",
                description="Query database using natural language",
                input_schema={
                    "type": "object",
                    "properties": {
                        "question": {"type": "string", "description": "Natural language question about the data"},
                        "max_results": {"type": "integer", "default": 100},
                        "include_explanation": {"type": "boolean", "default": True}
                    },
                    "required": ["question"]
                },
                output_schema={
                    "type": "object",
                    "properties": {
                        "results": {"type": "array"},
                        "sql_query": {"type": "string"},
                        "explanation": {"type": "string"},
                        "result_count": {"type": "integer"}
                    }
                },
                examples=[
                    FunctionExample(
                        description="Find top customers by revenue this year",
                        input={"question": "Who are our top 10 customers by revenue this year?"},
                        output={
                            "results": [{"customer_name": "Acme Corp", "revenue": 150000}],
                            "sql_query": "SELECT customer_name, SUM(order_total) as revenue FROM...",
                            "explanation": "This query joins the customers and orders tables...",
                            "result_count": 10
                        }
                    )
                ]
            ),
            
            MCPFunction(
                name="execute_safe_query",
                description="Execute SQL query with security validation",
                input_schema={
                    "type": "object",
                    "properties": {
                        "sql_query": {"type": "string"},
                        "parameters": {"type": "object"},
                        "max_results": {"type": "integer", "default": 1000}
                    },
                    "required": ["sql_query"]
                }
            )
        ]
    
    async def _natural_language_query(self, params):
        """Convert natural language to SQL and execute"""
        
        # Get database schema context
        schema_context = await self.db_client.get_schema_metadata()
        
        # Convert natural language to SQL using AI
        sql_query = await self._nl_to_sql(params['question'], schema_context)
        
        # Validate query security
        security_check = await self.security_validator.validate_query(sql_query)
        if not security_check.safe:
            raise MCPSecurityError(f"Query rejected: {security_check.reason}")
        
        # Optimize query performance
        optimized_query = await self.query_optimizer.optimize(sql_query)
        
        # Execute query
        results = await self.db_client.execute_query(
            optimized_query,
            max_results=params.get('max_results', 100)
        )
        
        # Generate explanation if requested
        explanation = ""
        if params.get('include_explanation', True):
            explanation = await self._generate_query_explanation(
                sql_query, schema_context, results
            )
        
        return {
            "results": results,
            "sql_query": optimized_query,
            "explanation": explanation,
            "result_count": len(results)
        }

MCP Server Management & Orchestration

Server Registry & Discovery 

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# MCP Server registry and discovery service
class MCPServerRegistry:
    def __init__(self):
        self.servers = {}
        self.health_monitor = HealthMonitor()
        self.load_balancer = LoadBalancer()
        
    async def register_server(self, server_info: MCPServerInfo):
        """Register new MCP server"""
        
        # Validate server
        validation = await self._validate_server(server_info)
        if not validation.valid:
            raise RegistrationError(validation.errors)
        
        # Health check
        health = await self.health_monitor.check_server_health(server_info.endpoint)
        if not health.healthy:
            raise RegistrationError("Server failed health check")
        
        # Add to registry
        self.servers[server_info.id] = RegisteredServer(
            info=server_info,
            registered_at=datetime.utcnow(),
            last_health_check=health,
            status='active'
        )
        
        # Setup monitoring
        await self.health_monitor.start_monitoring(server_info)
        
        return f"Server {server_info.name} registered successfully"
    
    async def discover_servers_for_task(self, task_description: str):
        """Find MCP servers capable of handling a specific task"""
        
        # Get task requirements using AI
        requirements = await self._analyze_task_requirements(task_description)
        
        # Find matching servers
        matching_servers = []
        
        for server_id, server in self.servers.items():
            if server.status != 'active':
                continue
                
            # Check capability match
            capability_score = await self._calculate_capability_score(
                server.info, requirements
            )
            
            if capability_score > 0.7:  # 70% match threshold
                matching_servers.append(ServerMatch(
                    server=server,
                    score=capability_score,
                    matching_functions=await self._identify_matching_functions(
                        server.info.functions, requirements
                    )
                ))
        
        # Sort by capability score
        matching_servers.sort(key=lambda x: x.score, reverse=True)
        
        return matching_servers
    
    async def _analyze_task_requirements(self, task_description):
        """Use AI to analyze what capabilities are needed for a task"""
        
        # AI prompt to extract requirements
        analysis_prompt = f"""
        Analyze this task and identify what business systems and capabilities are needed:
        
        Task: {task_description}
        
        Extract:
        1. Business systems (ERP, CRM, Accounting, etc.)
        2. Required operations (read, write, query, etc.)
        3. Data types involved
        4. Integration complexity level
        """
        
        # Use LLM to analyze
        analysis = await self.llm.analyze(analysis_prompt)
        
        return TaskRequirements(
            business_systems=analysis.get('business_systems', []),
            operations=analysis.get('operations', []),
            data_types=analysis.get('data_types', []),
            complexity=analysis.get('complexity', 'medium')
        )

# Intelligent request routing
class MCPRequestRouter:
    def __init__(self):
        self.registry = MCPServerRegistry()
        self.performance_tracker = PerformanceTracker()
        self.circuit_breaker = CircuitBreaker()
        
    async def route_request(self, request: MCPRequest):
        """Intelligently route MCP request to optimal server"""
        
        # Find capable servers
        capable_servers = await self.registry.discover_servers_for_task(
            request.function_name
        )
        
        if not capable_servers:
            raise NoCapableServerError("No servers can handle this request")
        
        # Score servers based on multiple factors
        server_scores = []
        for server_match in capable_servers:
            server = server_match.server
            
            # Get performance metrics
            perf_metrics = await self.performance_tracker.get_metrics(server.info.id)
            
            # Check circuit breaker status
            circuit_status = await self.circuit_breaker.get_status(server.info.id)
            
            # Calculate routing score
            score = self._calculate_routing_score(
                capability_score=server_match.score,
                performance_metrics=perf_metrics,
                circuit_status=circuit_status,
                current_load=server.current_load
            )
            
            server_scores.append(ServerScore(
                server=server,
                score=score,
                factors={
                    'capability': server_match.score,
                    'performance': perf_metrics.score,
                    'availability': 1.0 if circuit_status.closed else 0.0,
                    'load': 1.0 - (server.current_load / server.max_load)
                }
            ))
        
        # Select best server
        best_server = max(server_scores, key=lambda x: x.score)
        
        # Route request
        try:
            response = await self._execute_request(best_server.server, request)
            
            # Record success metrics
            await self.performance_tracker.record_success(
                best_server.server.info.id,
                response.execution_time
            )
            
            return response
            
        except Exception as e:
            # Record failure and potentially trigger circuit breaker
            await self.performance_tracker.record_failure(
                best_server.server.info.id,
                str(e)
            )
            
            # Try fallback servers
            return await self._try_fallback_servers(request, server_scores[1:])

Security & Compliance

Authentication & Authorization 

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# MCP Server security configuration
security:
  authentication:
    methods:
      - oauth2
      - api_key
      - mutual_tls
      - saml
    
    token_validation:
      validate_expiry: true
      validate_audience: true
      validate_issuer: true
      
  authorization:
    model: "rbac"
    
    roles:
      - name: "agent_executor"
        permissions:
          - "mcp:call_function"
          - "mcp:read_schema"
        
      - name: "system_admin"
        permissions:
          - "mcp:*"
          - "server:manage"
          - "logs:read"
    
    function_permissions:
      "get_customer_info":
        required_scopes: ["customer:read"]
        data_classification: "confidential"
        
      "create_invoice":
        required_scopes: ["invoice:create", "financial:write"]
        data_classification: "restricted"
        approval_required: true
  
  encryption:
    in_transit: "tls_1.3"
    at_rest: "aes_256_gcm"
    key_rotation: "automatic"
    
  audit_logging:
    enabled: true
    log_level: "detailed"
    retention_days: 2555  # 7 years
    pii_masking: true

Data Privacy & Governance 

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# Data privacy and governance for MCP servers
class MCPDataGovernance:
    def __init__(self):
        self.data_classifier = DataClassifier()
        self.privacy_engine = PrivacyEngine()
        self.audit_logger = AuditLogger()
        
    async def validate_data_access(self, request: MCPRequest):
        """Validate data access permissions and privacy requirements"""
        
        # Classify data sensitivity
        data_classification = await self.data_classifier.classify_request(request)
        
        # Check privacy regulations compliance
        privacy_check = await self.privacy_engine.validate_compliance(
            request, data_classification
        )
        
        if not privacy_check.compliant:
            raise PrivacyViolationError(privacy_check.violations)
        
        # Log access for audit
        await self.audit_logger.log_data_access(
            request=request,
            classification=data_classification,
            user=request.user,
            timestamp=datetime.utcnow()
        )
        
        return privacy_check
    
    async def apply_data_masking(self, response_data, user_permissions):
        """Apply data masking based on user permissions"""
        
        masked_data = {}
        
        for field, value in response_data.items():
            field_sensitivity = await self.data_classifier.classify_field(field, value)
            
            if self._user_can_access_field(user_permissions, field_sensitivity):
                masked_data[field] = value
            else:
                # Apply appropriate masking
                if field_sensitivity.contains_pii:
                    masked_data[field] = self._mask_pii(value, field_sensitivity.pii_type)
                elif field_sensitivity.financial:
                    masked_data[field] = self._mask_financial_data(value)
                else:
                    masked_data[field] = "[REDACTED]"
        
        return masked_data

Performance & Monitoring

MCP Server Performance Monitoring 

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# Comprehensive MCP server monitoring
class MCPServerMonitor:
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.performance_analyzer = PerformanceAnalyzer()
        self.alerting = AlertingSystem()
        
    async def monitor_server_performance(self, server_id: str):
        """Monitor individual MCP server performance"""
        
        # Collect real-time metrics
        metrics = await self.metrics_collector.collect_server_metrics(server_id)
        
        # Analyze performance trends
        analysis = await self.performance_analyzer.analyze_trends(server_id, metrics)
        
        # Check for performance issues
        issues = await self._detect_performance_issues(metrics, analysis)
        
        # Generate alerts if needed
        for issue in issues:
            if issue.severity >= AlertSeverity.HIGH:
                await self.alerting.send_alert(issue)
        
        return ServerPerformanceReport(
            server_id=server_id,
            metrics=metrics,
            analysis=analysis,
            issues=issues,
            recommendations=await self._generate_performance_recommendations(analysis)
        )
    
    async def _detect_performance_issues(self, metrics, analysis):
        """Detect various performance issues"""
        
        issues = []
        
        # High response time
        if metrics.avg_response_time > 5000:  # 5 seconds
            issues.append(PerformanceIssue(
                type="high_response_time",
                severity=AlertSeverity.HIGH,
                description=f"Average response time is {metrics.avg_response_time}ms",
                impact="User experience degradation"
            ))
        
        # High error rate
        if metrics.error_rate > 0.05:  # 5%
            issues.append(PerformanceIssue(
                type="high_error_rate",
                severity=AlertSeverity.CRITICAL,
                description=f"Error rate is {metrics.error_rate*100:.1f}%",
                impact="Service reliability issues"
            ))
        
        # Memory leak detection
        if analysis.memory_trend.slope > 0.1:  # Growing memory usage
            issues.append(PerformanceIssue(
                type="memory_leak",
                severity=AlertSeverity.MEDIUM,
                description="Memory usage is consistently increasing",
                impact="Potential server instability"
            ))
        
        return issues

Getting Started

MCP Server Development Kit 

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# Install MCP Server SDK
npm install -g @aimatrix/mcp-sdk
# or
pip install aimatrix-mcp-sdk

# Create new MCP server
aimatrix mcp create \
  --name=my-erp-connector \
  --template=generic-api \
  --language=python

# Test MCP server
aimatrix mcp test \
  --server=./my-erp-connector \
  --test-suite=comprehensive

# Deploy MCP server
aimatrix mcp deploy \
  --server=./my-erp-connector \
  --environment=production \
  --monitoring=enabled

Configuration Example 

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# mcp-server-config.yaml
server:
  name: "Enterprise ERP Connector"
  version: "1.0.0"
  description: "Connect AI agents to enterprise ERP system"
  
  connection:
    type: "rest_api"
    base_url: "https://erp.company.com/api/v1"
    authentication:
      type: "oauth2"
      client_id: "${ERP_CLIENT_ID}"
      client_secret: "${ERP_CLIENT_SECRET}"
      token_endpoint: "https://erp.company.com/oauth/token"
    
  functions:
    - name: "get_customer"
      endpoint: "/customers/{id}"
      method: "GET"
      description: "Retrieve customer information"
      
    - name: "create_order"
      endpoint: "/orders"
      method: "POST"
      description: "Create new sales order"
      
  security:
    data_classification: "confidential"
    audit_logging: true
    rate_limiting:
      requests_per_minute: 100
      
  monitoring:
    health_check_endpoint: "/health"
    metrics_enabled: true
    performance_tracking: true

Note

Enterprise Integration: MCP Servers provide the critical bridge between AI intelligence and existing business systems, enabling seamless digital transformation.

Tip

Start Simple: Begin with read-only functions for existing systems, then gradually add write capabilities as confidence and trust build.

MCP Servers - The intelligent connectors that make every business system AI-ready

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