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<div align="center"> <h1>𝔐 mpc-openai ✧</h1> <p><em>MCP Client with OpenAI compatible API</em></p> </div> > Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. > > — [https://modelcontextprotocol.io](https://modelcontextprotocol.io) ______________________________________________________________________ > [!WARNING] > This project is still in the early stages of development. Support is not planned. This is a MCP **client** (not a server). It is meant to be used as a library for building LLMs UI that support MCP through an OpenAI compatible API. This opens the door to locally runnable inference engines ([vLLM](https://github.com/vllm-project/vllm), [Ollama](https://github.com/ollama/ollama), [TGI](https://github.com/huggingface/text-generation-inference), [llama.cpp](https://github.com/ggerganov/llama.cpp), [LMStudio](https://github.com/lmstudio-ai), ...) that support providing support for the OpenAI API ([text generation](https://platform.openai.com/docs/guides/text-generation), [function calling](https://platform.openai.com/docs/guides/function-calling), etc.). ## Usage It is highly recommended to use [uv](https://docs.astral.sh/uv/) in your project based on mpc-openai: - It manages python installation and virtual environment. - It is an executable that can run self-contained python scripts (in our case MCP server) - It is used for CI workflows. Add mcp-openai to your project dependencies with: ``` uv add mcp-openai ``` or use classic pip install. ### Create a MCP client Now you can create a MCP client by specifying your custom configuration. ```python from mcp_openai import MCPClient from mcp_openai import config mcp_client_config = config.MCPClientConfig( mcpServers={ "the-name-of-the-server": config.MCPServerConfig( command="uv", args=["run", "path/to/server/scripts.py/or/github/…
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