Developer Guide: Turning pgvector Search Tools into a Reusable MCP Server

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A developer tutorial on DEV Community walks through converting hardcoded pgvector search functions into a standalone Model Context Protocol (MCP) server. Previously, AI agents could only access these search tools from within a single Python script, limiting reusability. Using the FastMCP library, the search functions are exposed as a protocol-compliant server that any LLM client — including Claude Desktop or Gemini agents — can connect to. The server implements three core primitives: tools for searching documents, resources for reading category data, and reusable prompt templates. The underlying search logic remains unchanged; only the deployment and access layer is restructured.

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