open-index/open-npm huggingface dataset:
npm package: @mseep/mcp-webhook
Description: Generic Webhook MCP Server
Author: kevinwatt
Latest version: 0.2.2

[![MseeP.ai Security Assessment Badge](https://mseep.net/mseep-audited.png)](https://mseep.ai/app/kevinwatt-mcp-webhook)

# MCP Webhook Server
[![smithery badge](https://smithery.ai/badge/@kevinwatt/mcp-webhook)](https://smithery.ai/server/@kevinwatt/mcp-webhook)

An MCP server implementation that integrates with webhooks, providing message sending capabilities.

## Features

* **Generic Webhook Support**: Send messages to any webhook endpoint
* **Custom Username**: Set custom display name for messages
* **Avatar Support**: Customize message avatar
* **MCP Integration**: Works with Dive and other MCP-compatible LLMs

<a href="https://glama.ai/mcp/servers/ijmd1ia5zg"><img width="380" height="200" src="https://glama.ai/mcp/servers/ijmd1ia5zg/badge" alt="Webhook Server MCP server" /></a>

## Installation

### Installing via Smithery

To install MCP Webhook Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@kevinwatt/mcp-webhook):

```bash
npx -y @smithery/cli install @kevinwatt/mcp-webhook --client claude
```

### Manual Installation
```bash
npm install @kevinwatt/mcp-webhook
```

## Configuration with [Dive Desktop](https://github.com/OpenAgentPlatform/Dive)

1. Click "+ Add MCP Server" in Dive Desktop
2. Copy and paste this configuration:

```json
{
  "mcpServers": {
    "webhook": {
      "command": "npx",
      "args": [
        "-y",
        "@kevinwatt/mcp-webhook"
      ],
      "env": {
        "WEBHOOK_URL": "your-webhook-url"
      },
      "alwaysAllow": [
        "send_message"
      ]
    }
  }
}
```

3. Click "Save" to install the MCP server

## Tool Documentation

* **send_message**
  * Send message to webhook endpoint
  * Inputs:
    * `content` (string, required): Message content to send
    * `username` (string, optional): Display name
    * `avatar_url` (string, optional): Avatar URL

## Usage Examples

Ask your LLM to:
```
"Send a message to webhook: Hello World!"
"Send a message with custom name: content='Testing', username='Bot'"
```

## Manual Start

If needed, start the server manually:

```bash
npx @kevinwatt/mcp-webhook
```

## Requirements

* Node.js 18+
* MCP-compatible LLM service

## License

MIT

## Author

kevinwatt

## Keywords

* mcp
* webhook
* chat
* dive
* llm
* automation
npm package: @mseep/mcp-windows-desktop-automation
Description: MCP server for Windows desktop automation
Latest version: 0.1.0

# MCP Windows Desktop Automation

A Model Context Protocol (MCP) server for Windows desktop automation using AutoIt.

## Overview

This project provides a TypeScript MCP server that wraps the [node-autoit-koffi](https://www.npmjs.com/package/node-autoit-koffi) package, allowing LLM applications to automate Windows desktop tasks through the MCP protocol.

The server exposes:
- **Tools**: All AutoIt functions as MCP tools
- **Resources**: File access and screenshot capabilities
- **Prompts**: Templates for common automation tasks

## Features

- Full wrapping of all AutoIt functions as MCP tools
- Support for both stdio and WebSocket transports
- File access resources for reading files and directories
- Screenshot resources for capturing the screen or specific windows
- Prompt templates for common automation tasks
- Strict TypeScript typing throughout

## Installation

```bash
# Clone the repository
git clone https://github.com/yourusername/mcp-windows-desktop-automation.git
cd mcp-windows-desktop-automation

# Install dependencies
npm install

# Build the project
npm run build
```

## Usage

### Starting the Server

```bash
# Start with stdio transport (default)
npm start

# Start with WebSocket transport
npm start -- --transport=websocket --port=3000

# Enable verbose logging
npm start -- --verbose
```

### Command Line Options

- `--transport=stdio|websocket`: Specify the transport protocol (default: stdio)
- `--port=<number>`: Specify the port for WebSocket transport (default: 3000)
- `--verbose`: Enable verbose logging

## Tools

The server provides tools for:

- **Mouse operations**: Move, click, drag, etc.
- **Keyboard operations**: Send keystrokes, clipboard operations, etc.
- **Window management**: Find, activate, close, resize windows, etc.
- **Control manipulation**: Interact with UI controls, buttons, text fields, etc.
- **Process management**: Start, stop, and monitor processes
- **System operations**: Shutdown, sleep, etc.

## Resources

The server provides resources for:

- **File access**: Read files and list directories
- **Screenshots**: Capture the screen or specific windows

## Prompts

The server provides prompt templates for:

- **Window interaction**: Find and interact with windows
- **Form filling**: Automate form filling tasks
- **Automation tasks**: Create scripts for repetitive tasks
- **Monitoring**: Wait for specific conditions

## Development

```bash
# Run in development mode
npm run dev

# Lint the code
npm run lint

# Run tests
npm run test
```

## License

MIT
npm package: @mseep/mcp-wordcounter
Description: A Model Context Protocol server for analyzing text documents with word and character counting capabilities
Author: Kenneth
Latest version: 0.1.0

# MCP Word Counter

A Model Context Protocol server that provides tools for analyzing text documents, including counting words and characters. This server helps LLMs perform text analysis tasks by exposing simple document statistics functionality.

## Features

- Count words in documents
- Count total characters (including spaces)
- Count characters excluding spaces
- Process files directly without exposing content to LLMs

## Installation

```bash
npm install mcp-wordcounter
```

## Usage

### As a CLI tool

```bash
npx mcp-wordcounter
```

### In Claude Desktop

Add to your Claude Desktop configuration (`claude_desktop_config.json`):

```json
{
  "mcpServers": {
    "mcp-wordcounter": {
      "command": "npx",
      "args": ["-y", "mcp-wordcounter"],
      "alwaysAllow": ["analyze_text"]
    }
  }
}
```

### Available Tools

#### analyze_text

Counts words and characters in a text document.

Parameters:
- `filePath` (string, required): Path to the text file to analyze

Returns:
- Word count
- Character count (including spaces)
- Character count (excluding spaces)

Example response:
```json
{
  "content": [{
    "type": "text",
    "text": "Analysis Results:\n• Word count: 150\n• Character count (including spaces): 842\n• Character count (excluding spaces): 702"
  }]
}
```

## Development

```bash
# Install dependencies
npm install

# Build the project
npm run build

# Run in watch mode during development
npm run watch

# Test with MCP Inspector
npm run inspector
```

## License

MIT License - see LICENSE file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
npm package: @mseep/mcp-youtube-music
Description: Simple MCP Starter Package
Author: Kevin Kern
Latest version: 0.0.3

# YouTube Music MCP 🎵

This is a simple MCP server that allows you to search for and play tracks on YouTube Music directly from your AI assistant like Cursor or Claude Desktop.

Built with:

- [YouTube Music](https://music.youtube.com/)
- [Anthropic MCP](https://docs.anthropic.com/en/docs/agents-and-tools/mcp)
- [Cursor](https://cursor.so/)

## Available Tools

- `searchTrack`: Search for tracks on YouTube Music by name.
- `playTrack`: Play tracks directly by searching and opening them in your default browser.


# Installation

## 1. Get a key

To make this work you need a valid [Google Youtube API Key](https://console.cloud.google.com/marketplace/product/google/youtube.googleapis.com)

## 2. Add to cursor

Add the following MCP configuration to your Cursor `.cursor/mcp.json` settings:

```json
{
  "mcpServers": {
    "youtube-music-mcp": {
      "command": "npx",
      "args": ["-y", "@instructa/mcp-youtube-music"],
      "env": {
        "YOUTUBE_API_KEY": "<INSERT_API_KEY_HERE>"
      }
    }
  }
}
```

**Develop**

This MCP is typically run directly using `npx` and doesn't require local installation or building unless you intend to modify the source code. If you want to develop it locally, you would typically clone the source repository (if available) and follow its specific contribution guidelines.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

---

## Links

- X/Twitter: [@kregenrek](https://x.com/kregenrek)
- Bluesky: [@kevinkern.dev](https://bsky.app/profile/kevinkern.dev)

## Courses
- Learn Cursor AI: [Ultimate Cursor Course](https://www.instructa.ai/en/cursor-ai)
- Learn to build software with AI: [instructa.ai](https://www.instructa.ai)

## See my other projects:

* [AI Prompts](https://github.com/instructa/ai-prompts/blob/main/README.md) - Curated AI Prompts for Cursor AI, Cline, Windsurf and Github Copilot
* [codefetch](https://github.com/regenrek/codefetch) - Turn code into Markdown for LLMs with one simple terminal command
* [aidex](https://github.com/regenrek/aidex) A CLI tool that provides detailed information about AI language models, helping developers choose the right model for their needs.
* [codetie](https://github.com/codetie-ai/codetie) - XCode CLI
npm package: @mseep/mcp_rss
Description: MCP server for interacting with RSS
Author: buhe
Latest version: 1.0.6

# MCP RSS

MCP RSS is a Model Context Protocol (MCP) server for interacting with RSS feeds.

## Features

- Parse OPML files to import RSS feed subscriptions
- Automatically fetch and update articles from RSS feeds
- Expose RSS content through MCP API
- Mark articles as favorites
- Filter articles by source and status

## Installation

### Prerequisites

- Node.js (v14 or higher)
- MySQL database

### Setup MySQL

```bash
$ docker run -itd --name mysql-test -p 3306:3306 -e MYSQL_ROOT_PASSWORD=123456 mysql
```

### Install MCP

This MCP uses stdio for communication. Fill in ``` npx mcp_rss ``` in the command, fill in the configuration that needs to be customized in the environment variable, and at least use your own opml file.

Use in claude desktop
```json
{
  "key": "rss",
  "command": "npx",
  "args": [
    "mcp_rss"
  ],
  "env": {
    "OPML_FILE_PATH": "/Users/guyanhua/feeds.opml"
  }
}

```

## Configuration

Set the following environment variables to configure MCP RSS:

### Configuration Options

| Option | Description | Default Value |
|--------|-------------|--------------|
| DB_HOST | Database host | localhost |
| DB_PORT | Database port | 3306 |
| DB_USERNAME | Database username | root |
| DB_PASSWORD | Database password | 123456 |
| DB_DATABASE | Database name | mcp_rss |
| OPML_FILE_PATH | Path to your OPML file with RSS feeds | "./feeds.opml" |
| RSS_UPDATE_INTERVAL | Interval to fetch RSS updates (in minutes) | 1 |

## MCP API Reference

The MCP RSS server exposes the following API methods:

### get_content

Get articles from subscribed RSS feeds.

**Parameters:**

| Parameter | Type | Description | Required |
|-----------|------|-------------|---------|
| status | string | Filter by article status ("normal" or "favorite") | No |
| source | string | Filter by source (feed title) | No |
| limit | number | Maximum number of articles to return | No (default: 10) |

**Response:**

```json
{
  "articles": [
    {
      "id": 1,
      "title": "Article Title",
      "content": "Article content...",
      "link": "https://example.com/article",
      "pubDate": "2023-01-01T12:00:00Z",
      "fetchDate": "2023-01-01T12:30:00Z",
      "status": "normal",
      "feedTitle": "Example Feed",
      "feedCategory": "Technology"
    }
  ],
  "success": true
}
```

### get_sources

Get all available RSS feed sources.

**Parameters:** None

**Response:**

```json
{
  "sources": [
    {
      "id": 1,
      "title": "Example Feed",
      "category": "Technology"
    }
  ],
  "success": true
}
```

### set_tag

Set the status of an article (normal or favorite).

**Parameters:**

| Parameter | Type | Description | Required |
|-----------|------|-------------|---------|
| status | string | Article status ("normal" or "favorite") | Yes |
| articleId | number | ID of the article to update | Yes |

**Response:**

```json
{
  "success": true,
  "message": "Article 1 status has been updated to favorite"
}
```

## License

MIT
npm package: @mseep/memory-bank-loader-mcp
Description: This is an MCP server for retrieving all files in the memory-bank directory at once. Its purpose is to reduce AI tool invocation frequency when loading memory-bank, saving both time and money.
Author: https://github.com/abeyuya
Latest version: 1.0.6

# memory-bank-loader-mcp

This is an MCP server for retrieving all files in the memory-bank directory at once.
Its purpose is to reduce AI tool invocation frequency when loading memory-bank, saving both time and money.

![screenshot](docs/screenshot.png)

## Features

-   Recursively reads all files within the specified directory (e.g., a 'memory-bank' directory).
-   Sorts files with a specific priority order:
    1.  Root-level files come first.
    2.  Specific priority files (`projectbrief.md`, etc.) within the root are ordered first.
    3.  All other files (non-priority root and all subdirectory files) are sorted alphabetically by relative path.
-   Optimized for providing clear and structured context to AI models.

## Tools

- `read-memory-bank`
  - Read and consolidates files from the specified memory bank directory. Use this to provide project context (Memory Bank) to the AI.
  - **Input:**
    - `memoryBankDirectoryFullPath` (string, **required**): The full, absolute path to the directory containing the memory bank files.
  - **Returns:**
    - Read file list.

## Setup

### NPX

```json
{
  "mcpServers": {
    "memory-bank-loader": {
      "command": "npx",
      "args": [
        "-y",
        "memory-bank-loader-mcp@latest",
      ],
    }
  }
}
```

### Build

```bash
$ npm run build
```

```json
{
  "mcpServers": {
    "memory-bank-loader": {
      "command": "node",
      "args": [
        "/full/path/to/memory-bank-loader-mcp/build/index.js"
      ]
    }
  }
}
```

## License

MIT