Understanding and Implementing the Model Context Protocol (MCP) for AI Tool Integration

Model Context Protocol (MCP) is a standardised AI model context protocol introduced by Anthropic on 2024‑11‑25, acting as an “AI docking station” that enables dynamic integration of resources, tools, and prompts for large language models.

The article explains why MCP is needed to solve fragmented function‑calling across OpenAI, Claude, Gemini and LLaMA, comparing their call structures and parameters in a table.

It then details the architecture of MCP, describing the Host‑Client‑Server C/S model, the role of MCP Server (resources, tools, prompts) and how developers can register tools such as a WeChat text‑message sender using the @modelcontextprotocol/sdk TypeScript SDK.

Code examples show project setup (npm init, dependencies), creating an McpServer instance, defining a tool with Zod schemas, registering resources, prompts, and implementing a client in Python that connects via SSE or stdio, lists available tools, and executes them based on JSON‑RPC 2.0 messages.

Finally, the article discusses debugging with the MCP Inspector, the JSON‑RPC communication format, and how the model decides which tool to invoke, completing the end‑to‑end workflow from user query to tool execution and natural‑language response.