What does this project demonstrate?
It demonstrates how an LLM can automatically decide which Python function to call based on a user’s intent, using MCP to describe available tools and Ollama to run the model locally.
@rajeevchandra
基本信息
暂无概览
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-client-server-example": {
"command": "python",
"args": [
"math_server.py"
]
}
}
}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。
It is a demonstration project that shows how a local AI agent can understand user queries and automatically call Python functions using the Model Context Protocol (MCP), a local LLM via Ollama, and a Python MCP client and server.
Install dependencies with pip install "mcp[cli] @ git+https://github.com/awslabs/mcp.git" openai==0.28 httpx, ensure Ollama is installed and running, pull a tool-calling‑capable model (e.g., ollama run llama3), run the MCP server (python math_server.py), then start the client with python ollama_client.py math_server.py. Finally, type queries like “What is 5 + 8?” and the system will respond with the computed result.
add, multiply) become callable toolsIt demonstrates how an LLM can automatically decide which Python function to call based on a user’s intent, using MCP to describe available tools and Ollama to run the model locally.
You need Python, the `mcp[cli]`, `openai==0.28`, and `httpx` packages, plus Ollama installed and running with a model that supports tool calling (e.g., Llama3).
No – everything runs locally. The LLM is served by Ollama on your machine, and the MCP components communicate over local transports.
The example server (`math_server.py`) exposes two tools: `add(a: int, b: int) -> int` and `multiply(a: int, b: int) -> int`.
Yes, as long as the model supports tool‑calling. The README suggests Llama3 via the `ollama run llama3` command; you can replace it with any compatible Ollama model.

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