LLM Tool Calling and MCP: Structured Function Calls and a Common Protocol for AI Tools

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The article explains how LLMs such as those served through ChatGroq indicate which tool should be invoked, using LangChain for structured tool calling within a manual loop. It notes that only SELECT queries are permitted in production, with updates possible under human-in-the-loop oversight but carrying risk. The Model Context Protocol (MCP) is presented as a common standard, likened to USB for AI tools, where an MCP client decides which exposed method to call. Unlike individual API endpoints, MCP bundles multiple operations into a single exposed interface that an agent-like client selects from. The author also mentions Pydantic for data validation, SQLite as a file-based database, and Qwen models for local testing.

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