MCP-Server 项目文档

What is MCP-Server 项目文档?

MCP-Server 项目文档 is a server and client implementation of the Model Context Protocol (MCP) that enables large language models (LLMs) to call external tools via a structured protocol. It provides a TypeScript client that connects to an LLM API (defaulting to Deepseek) and an MCP server that registers and executes tools such as weather forecast and GitHub user info lookup. The project is designed for developers who want to extend LLM capabilities with external tool integration and observe the full reasoning process.

How to use MCP-Server 项目文档?

Start the MCP server, then launch the client by providing the server script path as a command-line argument. Interact via the client’s command-line interface or send POST requests to the /sse endpoint on http://localhost:3000/sse with a JSON body containing a query field. Required environment variables are LLM_API_KEY (Deepseek key) and GAODE_KEY (Gaode Map key for weather queries).

Key features of MCP-Server 项目文档

• MCP server with tool registration and execution (weather, GitHub)
• TypeScript MCP client communicating via stdio
• Default integration with Deepseek LLM via OpenAI-compatible API
• Express-based Web API with streaming and process visualization
• White-box display of intent recognition, tool selection, and answer generation

Use cases of MCP-Server 项目文档

• Query current weather for any city using Gaode Map data
• Fetch GitHub user profile information
• Demonstrate how LLMs can invoke external tools through a structured protocol
• Build a front-end application that shows the step‑by‑step reasoning behind LLM‑tool interactions

FAQ from MCP-Server 项目文档

What is MCP-Server 项目文档 and how does it differ from other MCP implementations?

It is a complete MCP server‑client pair written in TypeScript, currently shipping two built‑in tools (weather and GitHub) and featuring a white‑box visualization of the model’s reasoning and tool‑calling steps.

What runtime and dependencies are required?

Node.js, TypeScript, Express, the @modelcontextprotocol/sdk package, and API keys for Deepseek and Gaode Maps. The client communicates with Deepseek via an OpenAI‑compatible API.

Where does user data go?

User queries are sent to the Deepseek LLM; if a tool is needed, the client sends a request to the local MCP server, which then calls external APIs (Gaode Maps for weather, GitHub for user info). Results are returned to the LLM, which generates the final answer.

What are the known limitations?

Only two tools are currently implemented (weather and GitHub user lookup). The system depends on the availability and correctness of the Deepseek API and the external service APIs.

What transport and authentication does MCP-Server 项目文档 use?

The MCP server and client communicate over standard input/output (stdio). The Web API uses HTTP. Authentication is handled via environment‑variable API keys (Deepseek and Gaode).