I've been dealing with the MCP side for a while, and I wanted to share what finally came up: mcpify.
A new tool called mcpify simplifies the process of making OpenAPI REST APIs usable by AI agents. It allows developers to integrate APIs with a single command, eliminating the need to build an MCP server from scratch. A notable feature is its "--lazy" mode, which dynamically calls only necessary tools for large APIs, significantly reducing the tool listing size. For instance, in an api.weather.gov test, the tool listing decreased from 38,882 to 1,741 characters. The project is available on GitHub.
The basic idea is simple: if you have an OpenAPI REST API, it makes it useable by AI agents with a single command without writing a MCP server from the beginning.
But I didn't want to leave it as just turning the endpoints into the tool. I also included things that will come in real use such as Auth, OAuth2, read-only/policy rules, retry, caching, stdio + Streamable HTTP, health check. With mcpify doctor, you can check whether the API is agent-friendly, with mcpify try, you can try tools from the terminal without opening any MCP client.
My favourite part was the --lazy mode. In large APIs, instead of putting the entire tool list on the model, it calls the required tool. In the api.weather.gov example I tested, the tool listing has dropped from 38,882 characters to 1,741 characters.
I also wanted to reduce dependencies as much as possible; the runtime side is based on Python stdlib. There are currently 294 tests and there are true MCP protocol tests on both stdio and HTTP sides.
I published it as an open source. I would especially like to hear it if there is a criticism, a bug or something that you say “you should definitely add this”.
GitHub: https://github.com/furkan708/mcpify