MCP Starter Project

@sharmatriloknath

代码仓库

基本信息

分类

开发工具

传输方式

stdio

作者

sharmatriloknath

关于 MCP Starter Project

How to setup mcp server and mcp client.

#mcp #mcp-server #mcp-client

MCP Starter Project 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp_starter": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

MCP Starter Project 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP Starter Project?

MCP Starter Project is a template for building AI applications with the Model Context Protocol (MCP). It includes a Python server that defines documentation search tools and a TypeScript client that connects to the server and integrates with HuggingFace models. It is designed for developers who want to learn or quickly prototype MCP‑based AI assistants.

How to use MCP Starter Project?

Set up the Python server by creating a virtual environment, installing dependencies, adding a .env file with SERPER_API_KEY, and running python main.py. For the client, install Node.js dependencies, add a .env with HUGGINGFACE_API_KEY, build the TypeScript code, then start it with node build/index.js ../mcp-server/main.py. Use the POST /chat endpoint with a JSON body containing query and sessionId to interact with the system.

Key features of MCP Starter Project

Documentation search tool for LangChain, LlamaIndex, and OpenAI
Session‑based conversation history management
Seamless integration of HuggingFace models with tool calls
Two HTTP endpoints: health check and chat
Robust error handling for API and tool execution

Use cases of MCP Starter Project

Building a documentation‑focused chatbot for popular AI libraries
Prototyping an MCP‑based conversational AI assistant
Learning how to implement MCP servers and clients
Creating a search‑enhanced help desk for developer documentation

常见问题

What are the prerequisites for MCP Starter Project?

You need Python 3.11+, Node.js 18+, a HuggingFace API key, and a Serper API key.

How do I configure environment variables?

Place your Serper API key in `mcp-server/.env` as `SERPER_API_KEY` and your HuggingFace API key in `mcp-clients/.env` as `HUGGINGFACE_API_KEY`.

What tools does the MCP server expose?

The server exposes a documentation search tool that can query LangChain, LlamaIndex, and OpenAI documentation.

How do I start the application?

First run `python main.py` from the `mcp-server` directory, then in another terminal run `node build/index.js ../mcp-server/main.py` from the `mcp-clients` directory.

What API endpoints are available?

The client serves `GET /health` for health checks and `POST /chat` for sending queries. The chat endpoint expects a JSON body with `query` and `sessionId` fields.

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