Useful Model Context Protocol Servers (MCPS)

What is Useful Model Context Protocol Servers (MCPS)?

A collection of standalone Python scripts that implement Model Context Protocol (MCP) servers for various utility functions. Each server provides specialized tools that can be used by AI assistants or other applications supporting the MCP protocol. Communication happens via standard input/output using JSON messages.

How to use Useful Model Context Protocol Servers (MCPS)?

Clone the repository, install uv, then run each server with uv run --directory <path> (e.g., uv run --directory ./ytdlp ytdlp_mcp). Alternatively, configure an MCP client with the command/args structure shown in the server‑specific JSON configuration examples, passing any required environment variables (like API tokens) via the env property. Dependencies are managed per‑server in pyproject.toml and installed automatically on first run.

Key features of Useful Model Context Protocol Servers (MCPS)

• Standalone Python MCP servers for utility functions.
• Extract chapters and subtitles from YouTube videos.
• Process Word document templates and convert to PDF.
• Render PlantUML and Mermaid diagrams to PNG images.
• Convert RSS feed content to Markdown with date filtering.
• Communicate via standard input/output using JSON messages.

Use cases of Useful Model Context Protocol Servers (MCPS)

• A chatbot fetching YouTube video chapters or subtitles for summarization.
• Automating Word document generation by replacing placeholders and managing content blocks.
• Producing PNG diagrams from PlantUML or Mermaid code inside an AI assistant.
• Aggregating RSS feed articles into a Markdown list filtered by date.

FAQ from Useful Model Context Protocol Servers (MCPS)

What is the Model Context Protocol (MCP)?

MCP is a standardized way for AI assistants to interact with external tools and services. It allows AI models to extend their capabilities by calling specialized functions provided by MCP servers.

How do I install and run a server?

Clone the repository, install uv (e.g., pip install uv), and run a server with uv run --directory <path>. For example: uv run --directory ./ytdlp ytdlp_mcp. The first run will automatically install dependencies.

Do I need any API keys or external services?

Some servers require external services: for Mermaid rendering you need a Mermaid Chart API access token set as MERMAID_CHART_ACCESS_TOKEN; for PlantUML rendering you need a running PlantUML server (e.g., via Docker). The YouTube, Word, and RSS servers do not require API keys.

What configuration is needed for an MCP client?

Your MCP client must launch the server using uv run --directory <path> with the correct script name. The README provides JSON configuration examples for each server, including optional friendly names and the env property for setting environment variables.

Can I add my own MCP server to this collection?

Yes. Create a new directory with a pyproject.toml (defining the script entry point) and a Python file implementing the MCP logic (using list_tools, call_tool, and the serve function). A development template and instructions are provided in the README.