open-index/open-npm huggingface dataset:
npm package: @iflow-mcp/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: @iflow-mcp/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: @iflow-mcp/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: @iflow-mcp/mcp-youtube-sheets
Description: MCP server for searching YouTube videos and saving results to Google Sheets
Author: Riku Kawashima
Latest version: 1.0.0

# YouTube to Google Sheets MCP Server

This MCP server provides functionality to search YouTube videos and automatically save the results to Google Sheets. It's designed to work with Claude and other AI assistants that support the Model Context Protocol.

[English](README.md) | [日本語](README.ja.md)

## Features

- Search YouTube videos using the YouTube Data API v3
- Save search results to Google Sheets automatically
- Configurable search parameters (query, max results)
- Results include video title, URL, channel name, and publish date

## Installation

```bash
npm install @rikukawa/youtube-sheets-server
```

## Prerequisites

1. YouTube Data API v3 Setup:
   - Go to [Google Cloud Console](https://console.cloud.google.com/)
   - Create a new project
   - Enable YouTube Data API v3
   - Create an API key

2. Google Sheets API Setup:
   - In the same project, enable Google Sheets API
   - Create a service account
   - Download the service account key (JSON format)
   - Share your target Google Sheet with the service account email

## Configuration

Add the server to your MCP settings file:

```json
{
  "mcpServers": {
    "youtube-sheets": {
      "command": "node",
      "args": ["path/to/youtube-sheets-server/build/index.js"],
      "env": {
        "YOUTUBE_API_KEY": "your-youtube-api-key",
        "SPREADSHEET_ID": "your-spreadsheet-id"
      },
      "disabled": false,
      "alwaysAllow": []
    }
  }
}
```

## Usage

“Ask the AI assistant to ‘search for YouTube videos with “ChatGPT usage” and retrieve 10 videos’ and try using it in that way.”

## Output Format

The tool will save the following information to your Google Sheet:
- Video Title
- Video URL
- Channel Name
- Publish Date

## License

MIT

## Author

Riku Kawashima

## Repository

[GitHub Repository](https://github.com/Rickyyy1116/mcp-youtube-sheets)

## NPM Package

[@rikukawa/youtube-sheets-server](https://www.npmjs.com/package/@rikukawa/youtube-sheets-server)
npm package: @iflow-mcp/md2card-mcp-server
Description: markdown to card
Latest version: 0.1.10

# MD2Card MCP 服务器

homepage: https://md2card.cn

MD2Card
Markdown 转知识卡片
将 Markdown 文档转换为精美的知识卡片，支持多种风格

## 安装与使用

### 方法一：使用 npx（推荐）

直接使用 npx 运行（需要设置 API 密钥环境变量）：

```bash
# 设置API密钥并运行
MD2CARD_API_KEY="您的API密钥" npx md2card-mcp-server
```

### 方法二：全局安装

```bash
# 安装
npm install -g md2card-mcp-server

# 运行（需要设置API密钥环境变量）
MD2CARD_API_KEY="您的API密钥" md2card-mcp-server
```

### 方法三：本地配置

clone 项目到本地，找到 index.js 路径替换到客户端 mcp 配置文件中

## 功能特性

- 支持 22 种主题样式：苹果备忘录 波普艺术 艺术装饰 玻璃拟态 温暖柔和 简约高级灰 梦幻渐变 清新自然 紫色小红书 笔记本 暗黑科技 复古打字机 水彩艺术 中国传统 儿童童话 商务简报 日本杂志 极简黑白 赛博朋克
- 智能尺寸适配
- 三种内容拆分模式：默认自动拆分
- 通过 MCP 协议提供标准化接口
- **新功能**：支持直接读取 Markdown 文件
- **新功能**：支持通过 type 参数直接指定卡片类型/尺寸
- **新功能**：支持抓取微信公众号文章并转换为 Markdown 格式
- **新功能**：自动提取文章标题、作者、描述和封面图片

## 使用方法

### 方法一：提供 Markdown 内容

```json
{
  "markdown": "# 标题\n\n内容"
}
```

### 方法二：提供 Markdown 文件路径

```json
{
  "markdownFile": "/path/to/your/file.md"
}
```

### 方法三：指定卡片类型/尺寸

可以通过`type`参数直接指定卡片类型，支持以下类型：

- 小红书：440x586
- 正方形：500x500
- 手机海报：440x782
- A4 纸打印：595x842

```json
{
  "markdown": "# 标题\n\n内容",
  "type": "小红书"
}
```

也可以通过 width 和 height 参数指定：

```json
{
  "markdown": "# 标题\n\n内容",
  "width": "小红书",
  "height": 586
}
```

### 方法四：抓取微信公众号文章

使用 `wechat_scraper` 工具抓取微信公众号文章：

```json
{
  "url": "https://mp.weixin.qq.com/s/your-article-id"
}
```

返回结果包含完整的 Markdown 格式文章，包括：
- 文章标题
- 作者信息
- 文章描述
- 封面图片
- 正文内容

## 客户端配置

### 通用 MCP 客户端配置

在 MCP 客户端配置文件中添加以下内容：

```json
{
  "md2card-server": {
    "command": "npx",
    "args": ["md2card-mcp-server@latest"],
    "env": {
      "MD2CARD_API_KEY": "您的API密钥"
    }
  }
}
```

### Cursor 客户端配置

在 Cursor 的 MCP 客户端配置文件中添加以下内容：

```json
{
  "md2card-server": {
    "command": "npx",
    "args": ["-y", "md2card-mcp-server@latest"],
    "env": {
      "MD2CARD_API_KEY": "您的API密钥"
    }
  }
}
```

> 注意：MD2CARD_API_KEY 环境变量是必需的，只有在实际运行时才会检查此环境变量。安装包时不需要此环境变量。

密钥申请地址：https://md2card.cn/zh?referralCode=github