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BaseMcpServer

MCP Server

Minimal Docker base for Model Context Protocol servers

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Updated 12 days ago

About

BaseMcpServer offers a lightweight, containerized foundation built on the MCP Python SDK, supporting HTTP+SSE and stdio protocols. It bundles common dependencies and configuration so developers can focus on implementing custom MCP server functionality.

Capabilities

Resources
Access data sources
Tools
Execute functions
Prompts
Pre-built templates
Sampling
AI model interactions

BaseMcpServer

BaseMcpServer is a lightweight, container‑ready foundation for building Model Context Protocol (MCP) servers. It bundles the MCP Python SDK, standard tooling, and a minimal Docker image that exposes both HTTP+SSE and stdio interfaces. The goal is to remove the repetitive plumbing so developers can concentrate on implementing custom tools, resources, and prompts that extend an AI assistant’s capabilities.

The server solves the pain point of repeatedly configuring environments, installing dependencies, and exposing ports for MCP implementations. By providing a pre‑configured Docker image that already listens on port 7501, the base eliminates version drift and environment inconsistencies. Developers can spin up a clean container with a single command, confident that the core MCP stack is compatible across projects. For local work, the included and scripts automate virtual‑environment creation, dependency installation, and protocol selection, allowing quick iteration without manual setup.

Key features of BaseMcpServer include:

  • Dual‑protocol support: HTTP+SSE for web‑based assistants (e.g., Claude or Cline) and stdio for command‑line integration.
  • Docker‑centric design: A minimal image that exposes only the necessary port, making it ideal for CI/CD pipelines and cloud deployments.
  • Reusable base: Projects can extend the image or fork the repository, inheriting all core logic while adding domain‑specific tools or data sources.
  • Simplified configuration: Environment variables and port mapping guidelines are baked in, reducing runtime errors caused by mis‑configured ports or missing secrets.

Typical use cases include building a custom knowledge‑base server that serves structured data to an AI assistant, creating a toolset for automated code review or documentation generation, or exposing internal APIs to an LLM without writing boilerplate networking code. In each scenario, developers can focus on the business logic of their tools—such as querying a database or invoking an external API—and rely on BaseMcpServer to handle the MCP handshake, streaming responses, and resource discovery.

Because it is built around the official MCP Python SDK, BaseMcpServer offers a clear contract between client and server. This consistency means that any MCP‑compliant assistant can plug into the server with minimal configuration, fostering rapid prototyping and deployment of AI‑powered services across diverse environments.