Useful Model Context Protocol Servers (MCPS)

by daltonnyx

AI/Development Tools AI Assistant Python Utility API

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 that support the MCP protocol.

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What is Useful Model Context Protocol Servers (MCPS)?

This is a collection of standalone Python scripts that act as Model Context Protocol (MCP) servers. These servers provide specialized tools for AI assistants to interact with external services using a standardized protocol.

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

Each MCP server is designed to be run using a Python environment manager like uv. Configure your MCP client application to launch the desired server using the command and args structure provided in the documentation. Ensure the command points to your uv executable and the args correctly specify --directory with the path to the MCP's folder and the script name to run. Pass necessary environment variables (like API tokens) using the env property.

Key features of Useful Model Context Protocol Servers (MCPS)

YouTube Data Extraction (chapters, subtitles)
Word Document Processing (template replacement, PDF conversion)
PlantUML Diagram Rendering
Mermaid Diagram Rendering
Standardized MCP interface
Easy to install and run with uv
Configurable via JSON
Extensible with new MCP servers

Use cases of Useful Model Context Protocol Servers (MCPS)

AI assistant extracting data from YouTube videos
AI assistant generating documents from templates
AI assistant rendering diagrams from text
AI assistant integrating with external services
Automated document generation workflows
Content creation tools
Educational applications

FAQ from Useful Model Context Protocol Servers (MCPS)

What is MCP?

The Model Context Protocol (MCP) is a standardized way for AI assistants to interact with external tools and services.

How do I install the MCP servers?

Clone the repository, install uv, and then run each server using uv run --directory <path> pointing to the specific MCP's directory.

What dependencies are required?

Dependencies are managed per-MCP via pyproject.toml. uv run will typically handle installing them automatically.

How do I configure an MCP server?

Configure your MCP client application to launch the desired server using the command and args structure shown in the documentation.

How do I add a new MCP server?

Create a new directory for your MCP, create pyproject.toml, pyrightconfig.json (optional), and your main Python file. Implement the required classes and functions (serve, list_tools, call_tool).