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Useful MCP Servers (MCPS)

@daltonnyx

代码仓库

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

daltonnyx

关于 Useful MCP Servers

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.

#mermaid#mcp-server#model-context-protocol-servers#docx-replace

Useful MCP Servers 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "userful-mcps": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "./ytdlp",
        "ytdlp_mcp"
      ]
    }
  }
}

Useful MCP Servers 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.

常见问题

1

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.

2

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.

3

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.

4

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.

5

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.

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