Dockerized MCP Server Template

Overview

The Dockerized MCP Server Template offers a turnkey solution for developers who want to expose custom data and functionality to Large Language Models (LLMs) via the Model Context Protocol. By containerizing the server and leveraging Streamable HTTP, it eliminates the need for persistent connections or specialized infrastructure. This makes the server ideal for quick prototyping, continuous integration pipelines, and deployment in cloud environments where stateless services are preferred.

At its core, the server implements a lightweight Python MCP stack that can be extended with user‑defined tools and resources. Developers simply add annotated functions, and the server automatically publishes them as MCP tools that clients can discover and invoke. The template includes a minimal example to illustrate how tool definitions translate into exposed endpoints, allowing clients to perform calculations or other operations without hard‑coding logic into the LLM itself.

Key capabilities of this template include:

• Stateless Streamable HTTP transport: Replaces older Server‑Sent Events (SSE) by sending a single HTTP request/response pair for each interaction. This removes the overhead of maintaining long‑lived connections, enabling seamless scaling in serverless or containerized environments.
• Docker compatibility: The entire stack is wrapped in a Docker image, making it straightforward to run locally or on any platform that supports containers. Docker Compose is provided for quick local orchestration, while the server can also be launched directly with Python if preferred.
• Extensibility: Developers can add new tools, resources, or custom prompts by following the same annotation pattern. The server automatically handles routing and validation, allowing rapid iteration on feature sets without modifying the core infrastructure.
• Production‑ready defaults: The template includes sensible port configurations, health endpoints, and logging hooks that align with typical deployment pipelines. This reduces the friction between a development prototype and a production‑grade service.

Typical use cases span from internal automation (e.g., invoking business logic or querying databases) to external API integration where an LLM needs to perform domain‑specific calculations. For instance, a finance team could expose risk assessment functions as MCP tools, enabling an assistant to compute portfolio metrics on demand. In a serverless CI/CD context, the stateless nature of Streamable HTTP allows the MCP server to spin up on demand and shut down without lingering connections, keeping operational costs low.

In summary, the Dockerized MCP Server Template delivers a ready‑to‑deploy, highly scalable foundation for integrating custom tools into LLM workflows. Its stateless architecture, containerization, and straightforward extensibility make it a compelling choice for developers looking to bridge the gap between AI assistants and real‑world data or services.