llm-mcp

# llm-mcp [](https://img.shields.io/github/v/release/imaurer/llm-mcp) [](https://github.com/imaurer/llm-mcp/actions/workflows/main.yml?query=branch%3Amain) [](https://codecov.io/gh/imaurer/llm-mcp) [](https://img.shields.io/github/license/imaurer/llm-mcp) [ `llm`](https://llm.datasette.io/) [plugin](https://llm.datasette.io/en/stable/plugins/directory.html) for creating MCP clients and servers. ## Installation ```bash pip install llm-mcp ``` ## MCP Servers This package provides a bridge between MCP servers and the `llm` package, allowing you to use MCP tools with LLM models. The bridge supports both stdio and HTTP-based MCP servers and provides a synchronous interface that's safe to use in any context, including Jupyter notebooks, FastAPI applications, and more. ### Command Line Usage (todo) ### Programmatic Usage For programmatic usage, you can wrap MCP tools as `llm.Tool` objects: ```python import os from llm import get_model from llm_mcp import wrap_stdio, stdio # convert stdio tool to llm tools tools = wrap_stdio(stdio.ServerParameters( command="npx", args=["-y", "@wonderwhy-er/desktop-commander"], )) # wrap_mcp(command string) equivalent to wrap_stdio(stdio.ServerParameters) # from llm_mcp import wrap_mcp # tools = wrap_mcp("npx -y @wonderwhy-er/desktop-commander") # Use the tools with a model model = get_model("gpt-4.1-nano") response = model.chain( f"Display the text found in the secret.txt file in {os.getcwd()}", tools=tools, ) print(response.text()) ``` **Output:** > The text found in the secret.txt file is: "Why don't pelicans like to tip > waiters?" ### HTTP Servers For HTTP-based MCP servers, use the HTTP …