Describe the MCP server you want, point at an OpenAPI spec, and get a small curated FastMCP server (a few workflow-level tools, not hundreds of endpoint clones) plus a built-in eval harness that measures it.
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# prompt-to-mcp [](https://github.com/KnaniOussama/prompt-to-mcp/actions/workflows/test.yml) [](https://pypi.org/project/prompt-to-mcp/) [](https://pypi.org/project/prompt-to-mcp/) [](LICENSE) Describe the MCP server you want. Point at an OpenAPI spec. Get a small, curated FastMCP server as editable code. ```bash pip install prompt-to-mcp # or run it without installing: uvx prompt-to-mcp ``` Big APIs make bad MCP servers when converted one tool per endpoint: Stripe's spec becomes 587 tools and ~463,000 tokens of definitions, more than 14 times a 32k context window. prompt-to-mcp compiles the server you describe instead, with just the tools that serve your use case, and ships an eval harness that measures the result. ## Contents - [Quick start](#quick-start) - [Example: 587 Stripe operations, 6 tools](#example-587-stripe-operations-6-tools) - [How it works](#how-it-works) - [Use the generated server](#use-the-generated-server) - [Models and providers](#models-and-providers) - [Evidence](#evidence) - [Coverage tradeoff](#coverage-tradeoff) - [CLI reference](#cli-reference) - [Development](#development) - [License](#license) ## Quick start You need Python 3.12+ and one LLM. A local Ollama model works; so does any key that speaks the Anthropic or OpenAI protocol. Nothing to clone. ```bash # grab a small example spec curl -o petstore.yaml https://raw.githubusercontent.com/KnaniOussama/prompt-to-mcp/master/tests/fixtures/petstore.yaml # 1. see what a naive 1:1 conversion would cost (free, no LLM involved) uvx prompt-to-mcp inspect petstore.yaml # 2. describe the server you want (the one step that calls an LLM) uvx prompt-to-mcp plan petstore.yaml "a server for looking up pets and thei…
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