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Llm MCP Server Template

@coderzzy

基本信息

分类

AI 与智能体

传输方式

stdio

作者

coderzzy

关于 Llm MCP Server Template

LLM-MCP server 开发的模版项目

Llm MCP Server Template 配置

暂无标准配置

该服务器的 README 中没有可解析的 MCP 配置块,请前往代码仓库查看安装说明。

代码仓库

Llm MCP Server Template 工具

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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 llm-mcp-server-template?

llm-mcp-server-template is a template project for developing LLM-MCP (Model Context Protocol) servers. It demonstrates a simple number comparison server and provides example clients that illustrate how various models and invocation methods can interact with MCP servers. It is intended for developers learning to build and integrate MCP tools.

How to use llm-mcp-server-template?

  1. Install uv (package manager) and Python 3.10.
  2. In the server directory, create a virtual environment and install mcp[cli] and httpx.
  3. Use mcp dev math.py to run the server in development mode (supports inspection via mcp-server-inspector).
  4. In the client directory, install openai, openai-agents, and socksio, set OPENAI_API_KEY in .env, then run python openai_client.py.

Key features of llm-mcp-server-template

  • Implements a number‑comparison MCP server (stdin/stdout transport)
  • Includes a working OpenAI client that calls the MCP tool
  • Supports inspection with the official mcp-server-inspector
  • Provides a clear template for local development and debugging
  • Demonstrates the two‑round MCP workflow (tool call + final response)
  • Easily extensible to remote transport (SSE) or other hosting platforms

Use cases of llm-mcp-server-template

  • Learning how to build a custom MCP server from scratch
  • Testing MCP client‑server communication locally before deployment
  • Prototyping a new MCP tool that performs simple logic (e.g., compare numbers)
  • Integrating an MCP server with AI agents on platforms like OpenAI SDK or LangChain
  • Serving as a starter for porting existing command‑line tools to MCP

常见问题

1

What is MCP (Model Context Protocol)?

MCP is a protocol that allows AI models (clients) to call external tools (servers) for capabilities like mathematical computation, file I/O, or network requests. Think of the model as the brain and MCP servers as its tools.

2

What transport options does llm-mcp-server-template support?

The template currently supports local stdio transport (direct client‑server subprocess). It mentions remote aggregated platforms via SSE (e.g., Glama.ai, MCP.so) as a future direction.

3

Does the server require Python 3.10?

Yes. The README instructs to install Python 3.10 via `uv` and to create a virtual environment with that version.

4

How do I test the server without a client?

You can use `mcp-server-inspector` by running `mcp dev math.py` inside the `server` directory. This provides an interactive UI for exploring tools and calling them.

5

Can I use this template with Anthropic SDK or LangChain?

The README lists these as future tasks (TODO): using `anthropic-sdk`, `langchain-mcp-adapter`, and a fully native implementation. The included client currently uses OpenAI’s `openai-agents` SDK.

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