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MCP protocol specification overview

Q: MCP protocol specification overview

The MCP (Model Context Protocol) is a standardized protocol by Anthropic for connecting AI agents to external tools and resources via a structured message exchange. It uses JSON-based messages over stdio or SSE transport, enabling seamless integration of AI models with APIs, databases, or custom tools.

Quick answer

The MCP (Model Context Protocol) is a standardized protocol by Anthropic for connecting AI agents to external tools and resources via a structured message exchange. It uses JSON-based messages over stdio or SSE transport, enabling seamless integration of AI models with APIs, databases, or custom tools.

PREREQUISITES

Python 3.8+
pip install mcp
Anthropic API key (free tier works)
Basic knowledge of JSON and Python

Setup

Install the official mcp Python package and set your ANTHROPIC_API_KEY environment variable to authenticate with the MCP server.

bash

pip install mcp

Step by step

This example demonstrates a simple MCP server using the mcp Python SDK that listens on stdio and responds to a basic request.

python

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

# Define a simple handler function
async def handler(request):
    # Echo back the received request content
    return {"response": f"Received: {request.get('content', '')}"}

# Create MCP server instance with the handler
server = Server(handler=handler)

# Run the server using stdio transport
stdio_server(server)

Common variations

Use SSE transport instead of stdio for web-based integration.
Implement multiple handlers to connect different tools or APIs.
Integrate with Anthropic models by forwarding requests to claude-3-5-sonnet-20241022.

python

import os
import asyncio
from mcp.server import Server
from mcp.server.sse import sse_server

async def handler(request):
    # Example: call Anthropic Claude model here
    from openai import OpenAI
    client = OpenAI(api_key=os.environ["ANTHROPIC_API_KEY"])
    response = client.chat.completions.create(
        model="claude-3-5-sonnet-20241022",
        system="You are an MCP tool responder.",
        messages=[{"role": "user", "content": request.get('content', '')}]
    )
    return {"response": response.choices[0].message.content}

server = Server(handler=handler)

# Run MCP server with SSE transport
asyncio.run(sse_server(server, host="0.0.0.0", port=8080))

Troubleshooting

If the server does not start, verify your Python version is 3.8 or higher.
Check that ANTHROPIC_API_KEY is set correctly in your environment.
For stdio transport, ensure your environment supports asynchronous stdio operations.
Use logging to debug message flow and handler responses.

✅

Key Takeaways

Use the official mcp Python SDK to implement MCP servers easily with async handlers.
MCP supports stdio and SSE transports for flexible AI agent integration.
Forward MCP requests to Anthropic models like claude-3-5-sonnet-20241022 for powerful tool responses.

Verified 2026-04 · claude-3-5-sonnet-20241022

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