Custom MCP Server

Overview

The Mcp Server 101 repository demonstrates how to expose a simple, yet powerful, Model Context Protocol (MCP) server that lets Claude and other AI assistants interact with external services. By running a lightweight Python process or a Docker‑based SQLite instance, developers can turn any API or database into an AI‑ready toolset with minimal setup. This solves the common pain point of bridging the gap between an LLM’s request language and real‑world data sources—making it trivial to fetch weather forecasts, trigger alerts, or run SQL queries directly from a conversational interface.

At its core, the server acts as an intermediary that receives structured JSON requests from the LLM, executes the requested operation (e.g., call a government weather API or run an SQL query), and returns results in the same format. The simplicity of this pattern means developers can add new tools by writing a small wrapper function or Docker image, then registering it in Claude’s configuration. The server’s value lies in its plug‑and‑play nature: once the MCP endpoint is running, any LLM that supports the protocol can invoke it without additional authentication or custom integration code.

Key capabilities include:

• Tool registration: Define arbitrary commands (Python scripts, Docker containers) that the LLM can call via MCP.
• Resource management: Expose datasets or APIs as first‑class resources, allowing the assistant to reference them in prompts.
• Prompt customization: Provide context‑specific prompts that guide the LLM on how to format requests and interpret responses.
• Sampling control: Adjust generation parameters (temperature, token limits) per tool invocation to fine‑tune outputs.

Typical use cases span from developer productivity tools—such as a “weather alert” service that automatically fetches forecasts for specified regions—to business intelligence scenarios, like querying a bakery’s inventory database to trigger low‑stock alerts. In each scenario the MCP server removes boilerplate code, letting developers focus on business logic while the AI assistant handles natural‑language interaction.

Integration into an existing workflow is straightforward. Once the MCP server is defined in Claude’s configuration file, the assistant can call it with a simple function‑like syntax (). The server processes the request, communicates with the underlying API or database, and returns structured data that the LLM can embed in its response. This tight coupling enables dynamic, real‑time information retrieval and decision support directly within conversational agents.

What sets this server apart is its dual‑mode execution: a native Python process for quick prototyping and a Docker container for production‑grade isolation. This flexibility allows teams to iterate rapidly during development while ensuring consistent, reproducible deployments in production environments. For developers familiar with MCP concepts, Mcp Server 101 offers a clear blueprint for turning any data source into an AI‑accessible tool with just a few configuration steps.