Bring Your API: Building an MCP Server Builder
I started this project with a question: if a company already has APIs, how much work should it take to let an AI assistant use them? Say a company has a support-ticket API. People use it through an application to look up tickets and check their status. Now the company wants someone to ask an assistant, “What’s happening with ticket 1042?” and get an answer from that same system. The data and the…
The project aims to simplify the process of integrating APIs with AI assistants, allowing users to ask questions and receive answers using existing APIs without extensive development effort. The core concept, referred to as "bring your API," involves providing an API specification, defining what an agent should accomplish, and then having a builder agent create and test the necessary tools.
The MCP (Multi-Modal Call) server builder is at the heart of this project, enabling AI applications to discover and utilize tools without needing to understand the underlying API details. Initially, the creator misunderstood the need for the MCP protocol, believing it was essential for API calls. However, they later realized that a simple function call can suffice.
The key challenge is building the connection between the API and the AI assistant, which involves deciding what inputs a tool accepts, which API it calls, what it returns, and how to handle errors. The project is divided into three main components: the harness, which provides shared code for the MCP server and tool management; integrations, which contain the specifics for each API, including tool definitions and API handlers; and the builder agent, which creates these integrations based on API specifications.
The setup process allows the builder to propose tools and generate files, while the assistant interacts with the running server for actual queries. This modular approach allows for easy addition of new APIs and ensures that the core server code remains stable. The author has tested basic functionalities like tool discovery, execution, and handling of missing data, and is currently refining the architecture and integration rules.
The next steps involve getting the harness to load integrations through the defined contract and demonstrating the end-to-end flow from API connection to assistant query.
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