June 2025 Summaries

Model Context Protocol (MCP) has emerged as a promising framework for building agents and workflows on top of Large Language Models (LLMs) by addressing past challenges faced by similar attempts. Despite not being revolutionary, MCP is praised for its simplicity and timely introduction, which coincides with the improved reliability of LLMs that can now handle complex tasks with reduced error handling. MCP offers a shared, vendor-neutral protocol that simplifies tool integration across platforms, allowing tool developers and agent developers to work more efficiently without being bogged down by platform-specific requirements. The protocol's tooling is accessible, with SDKs available in multiple languages, facilitating easier development and sharing of tools. MCP's growing momentum is evident as major foundational model providers like OpenAI and Google's DeepMind have adopted it, and a robust ecosystem of independent resources is emerging. While skepticism remains about its longevity, MCP's current success is attributed to its practicality and the community's enthusiasm, making it likely to persist as a crucial component in the future of APIs.

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.