Expose any MCP server's tools as OpenAI function-calling tools, and execute OpenAI tool calls back against it.
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<div align="center"> # 🌉 mcp-openai-bridge **Give any OpenAI-function-calling agent access to the entire MCP tool ecosystem — no SDK rewrite, no per-tool adapter.** [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) [](#privacy--security) [](#versioning) </div> <div align="center"> [Problem](#the-problem) · [What it does](#what-it-does) · [Use cases](#use-cases) · [Example](#end-to-end-example) · [Architecture](#architecture) · [Performance](#performance) · [Install](#installation) · [Security](#privacy--security) </div> --- ## The problem Two tool-calling formats now dominate agent development: - **MCP** (Model Context Protocol) — the format most Claude-first tools, IDEs, and local dev tooling speak. - **OpenAI function-calling** — the `tools=[{"type": "function", ...}]` JSON-Schema format used by the OpenAI API and every SDK/framework that mirrors it (LangChain, most self-rolled agent loops, etc.). If your agent only speaks OpenAI function-calling, the fast-growing MCP server ecosystem (filesystem tools, database connectors, SaaS integrations, dev tools) is invisible to it — unless someone hand-writes a per-server adapter, or adopts a full proxy that speaks MCP on one side and OpenAI-compatible chat completions on the other. **`mcp-openai-bridge` is a narrower shim.** Point it at any MCP server, get back a local HTTP endpoint that speaks pure OpenAI function-calling — just tool listing and tool execution, nothing else. ```mermaid flowchart LR A["OpenAI-format agent\n(tools=[...] / tool_calls)"] -- "GET /v1/tools" --> B["mcp-openai-bridge"] B -- "OpenAI tools array" --> A A -- "POST /v1/execute\n{name, arguments}" --> B …
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