Introducing Model Context Protocol (MCP): Architecture, Server/Client Development, and Practical Applications

The Model Context Protocol (MCP) is presented as a standardised interface for AI models to invoke external tools, likened to an "AI docking station" that dynamically connects knowledge bases, computation modules, and specialised models, thereby eliminating fragmented function‑call formats across providers.

A comparison table highlights differences in call structures, parameter formats, and special fields among OpenAI, Claude, Gemini, and LLaMA, illustrating the need for a unified protocol.

Server Development – The guide shows how to set up an MCP server using the community TypeScript SDK, including project initialization, dependency installation, and creating a McpServer instance. It demonstrates registering a text‑sending tool for WeChat, defining resources, tools, and prompts, and provides full source snippets for each step.

Client Development – A Python‑based MCP client is described, supporting both stdio and Server‑Sent Events (SSE) connections. The client class manages sessions, connects to servers, lists available prompts/tools/resources, and includes cleanup logic. Example code shows how to initialise the client, connect, and invoke server methods.

Architecture – MCP follows a C/S model with Host, Client, and Server roles. Hosts (e.g., Cursor) receive user queries, Clients maintain 1:1 connections to Servers, and Servers execute concrete operations such as file scanning or messaging.

Communication – All interactions between client and server use JSON‑RPC 2.0, offering simplicity, lightweight payloads, statelessness, language neutrality, and batch processing capabilities.

Tool Selection & Execution – The article explains how LLMs decide which registered tools to call by constructing a system prompt that lists tool descriptions and enforces a strict JSON output format. It provides a Python example where the model’s response is parsed, the appropriate server is located, and the tool is executed, with error handling and result transformation.

Debugging & Testing – The MCP Inspector tool (v0.7.0) is recommended for debugging servers, with screenshots illustrating tool, resource, and prompt inspection.

Summary – By exposing a catalogue of tools via MCP servers, hosts can delegate tasks to specialised services, receive JSON‑RPC responses, and convert them into natural‑language replies, achieving decoupled decision‑making and execution in AI‑augmented applications.