How to Build an MCP Server to Expose AI Tools Using Python and Flask

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A developer working on a LangGraph-powered support bot encountered persistent context-retention failures, which were traced back to the absence of a centralized tooling server. To solve this, they outline how to build a Model Context Protocol (MCP) server that exposes tools to AI applications in a scalable way. The walkthrough uses Python to define a basic sentiment analysis tool and a Flask-based REST server to host and serve it via HTTP endpoints. A key practical warning is raised: tools must be registered before the server starts, or a dynamic registration mechanism should be used to keep the tool list current. The post is part of a 30-day series on agentic AI development with LangGraph and MCP.

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