Mastering Multi-Step AI Workflows with MCP Prompts and Resources

This article discusses the Model Context Protocol (MCP) and how its primitives - tools, prompts, and resources - can help manage complex AI workflows. It highlights the importance of delegating specific tasks to the appropriate control plane (model, user, or application) for reliable and accurate results.

Details

The article uses the example of a weekly sales report to illustrate the challenges of asking an LLM to choreograph a multi-step workflow. While the LLM excels at language-based tasks, it struggles with precise arithmetic and data flow management. By delegating the computational steps to a server-side component and having the LLM focus on formatting the output, the report can be generated accurately every time. This is the core idea behind MCP's prompts - user-controlled workflow packages that encapsulate the necessary steps and data flow, allowing the LLM to focus on its strengths. The article also introduces resources, which are application-controlled context that can be injected into the workflow as needed, without requiring explicit user or model intervention. Together, these MCP primitives form a comprehensive system for integrating AI models with enterprise data and systems.