API to MCP: step-by-step guide for developers

The integration of the Model Context Protocol (MCP) with APIs is enhancing how AI agents interact with applications, allowing for more machine-oriented interactions rather than human-centric ones. MCP servers expose tools with specific titles, descriptions, and input schemas, which AI agents use to interpret and generate requests. These requests are then converted into API calls by the server, with the results returned to the client. While a straightforward conversion of each API endpoint into an MCP tool may seem appealing, better results often require more nuanced design considerations, such as strategizing endpoint exposure, condensing tool descriptions, and simplifying input schemas. Stainless, a company experienced in making APIs MCP-compatible, emphasizes the importance of a well-designed API as the foundation for a functional MCP server. Different approaches to structuring an MCP server include providing all endpoints, selecting subsets for specific use-cases, or creating composite tools that abstract over multiple endpoints. Testing and iteration are crucial for optimizing MCP servers, as they reveal practical insights into how AI models interact with the server and help refine the tool design and architecture for improved AI readiness.