What is MCP (Model Context Protocol)?

Understanding the Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an innovative framework designed to connect LLM applications with external integrations, facilitating seamless communication and enhancing the capabilities of language models.

Core Architecture

MCP is based on a client-server architecture comprising:

  • Hosts: LLM applications that initiate connections (like Claude Desktop or IDEs).
  • Clients: Maintain individual connections with servers within the host application.
  • Servers: Provide context, tools, and prompts to clients.

Components of MCP

  1. Protocol Layer: Handles message framing and communication patterns.
  2. Transport Layer: Manages communication, supporting stdio transport for local processes and HTTP with SSE for remote operations.
  3. Message Types: Include requests, responses, errors, and notifications.

Message Types

  1. Requests: Await a response:

    interface Request {
  method: string;
  params?: { ... };
}

  2. Results: Represent successful responses:

    interface Result {
  [key: string]: unknown;
}

  3. Errors: Indicate a failure in a request:

    interface Error {
  code: number;
  message: string;
  data?: unknown;
}

  4. Notifications: One-way messages not expecting a response:

    interface Notification {
  method: string;
  params?: { ... };
}

Lifecycle Phases

  1. Initialization: Establishes protocol version compatibility and exchange of capabilities.

    sequenceDiagram
    participant Client
    participant Server

    Client->>Server: initialize request
    Server->>Client: initialize response
    Client->>Server: initialized notification

    Note over Client,Server: Ready for message exchange

  2. Operation: Involves regular exchanges, including requests, responses, and notifications.

  3. Termination: Encompasses clean shutdowns via various means.

Error Handling

Defined error codes ensure robust management:

enum ErrorCode {
  ParseError = -32700,
  InvalidRequest = -32600,
  MethodNotFound = -32601,
  InvalidParams = -32602,
  InternalError = -32603
}

Applications can extend with custom error codes above -32000.

Implementation Example

Here's a basic outline for implementing an MCP server:

TypeScript

import { Server } from "@modelcontextprotocol/sdk/server/index.js";

const server = new Server({
  name: "example-server",
  version: "1.0.0"
}, {
  capabilities: {
    resources: {}
  }
});

server.setRequestHandler(ListResourcesRequestSchema, async () => {
  return {
    resources: [
      {
        uri: "example://resource",
        name: "Example Resource"
      }
    ]
  };
});

const transport = new StdioServerTransport();
await server.connect(transport);

Python

import asyncio
from mcp.server import Server
from mcp.server.stdio import stdio_server

app = Server("example-server")

@app.list_resources()
async def list_resources():
    return [
        {
            "uri": "example://resource",
            "name": "Example Resource"
        }
    ]

async def main():
    async with stdio_server() as streams:
        await app.run(
            streams[0],
            streams[1],
            app.create_initialization_options()
        )

if __name__ == "__main__":
    asyncio.run(main())

Best Practices

  • Transport Selection: Use stdio for local and SSE for HTTP-compatible remote communications.
  • Message Handling: Implement efficient request processing and error management.
  • Security Considerations: Use TLS for remote connections, sanitize inputs, implement route limits, and handle errors prudently.

Security and Error Management

  1. Transport Security: Implement TLS and validate connections.
  2. Message Validation: Ensure all messages and inputs are sanitized and validated.
  3. Resource Protection: Enforce access controls, rate limits, and secure cleanup processes.

Debugging and Monitoring

Implement logging and diagnostics to monitor and enhance performance, some of these include:

  1. Logging: Record events, message flow, and errors.
  2. Diagnostics: Implement health checks and monitor state.
  3. Testing: Validate error-handling and load impact.

MCP enables the extension of LLM capabilities by defining clear protocols and structures for integrating with external systems, making it an essential component for developers focusing on AI-enhanced applications.