Designing an MCP server from a REST API

Creating an effective Model Context Protocol (MCP) server from a REST API involves more than simply wrapping each endpoint as a tool; it requires thoughtful design to ensure the server serves an LLM-based agent effectively. The key challenge is identifying which functions to implement, focusing on user goals rather than API endpoints, and utilizing MCP's three primitives: tools, resources, and prompts. Tools are actions the model decides to call, resources are data for context, and prompts start structured workflows. A successful design involves grouping endpoints into workflows, setting the right granularity, designing parameters and returns for LLMs, and planning for errors as part of the conversation. The process involves curating the server design to suit the agent's needs, avoiding over-exposure of endpoints, and ensuring tool descriptions are comprehensive. The guide suggests splitting large APIs into domain-specific servers or using dynamic tool loading for scalability, emphasizing that generated servers should be curated for optimal performance. The follow-up article will detail the implementation of an MCP server using Python, illustrating the design principles with a fictional API called Pantry.