open-index/open-npm huggingface dataset:
npm package: @iflow-mcp/yvgude-lean-ctx
Description: lean-ctx — the token-efficient context tool for LLM coding agents. Installs a pre-built binary, no Rust required.
Latest version: 2.12.4

# lean-ctx-bin

Pre-built binary distribution of [lean-ctx](https://github.com/yvgude/lean-ctx) — the Cognitive Filter for AI Engineering.

No Rust toolchain required. The correct binary for your platform is downloaded automatically during `npm install`.

## Install

```bash
npm install -g lean-ctx-bin
```

After installing, run the one-command setup:

```bash
lean-ctx setup
```

This auto-detects your shell and editors, installs shell aliases, creates MCP configs, and verifies everything.

## Supported Platforms

| Platform | Architecture |
|----------|-------------|
| Linux | x86_64, aarch64 |
| macOS | x86_64, Apple Silicon |
| Windows | x86_64 |

## Alternative Install Methods

```bash
# Universal installer (no Rust needed)
curl -fsSL https://leanctx.com/install.sh | sh

# Homebrew (macOS/Linux)
brew tap yvgude/lean-ctx && brew install lean-ctx

# Cargo (requires Rust)
cargo install lean-ctx
```

## Links

- [Documentation](https://leanctx.com/docs/getting-started)
- [GitHub](https://github.com/yvgude/lean-ctx)
- [crates.io](https://crates.io/crates/lean-ctx)
- [Discord](https://discord.gg/pTHkG9Hew9)
npm package: @iflow-mcp/zacfire-mcp-web-reader
Description: MCP server for reading web content with Jina Reader and local parser support
Author: zacfire
Latest version: 2.0.0

# MCP Web Reader

一个强大的 MCP (Model Context Protocol) 服务器，让 Claude 和其他大语言模型能够读取和解析网页内容。支持突破访问限制，轻松获取微信文章、时代杂志等受保护内容。

## 功能特点

- 🚀 **三引擎支持**：集成 Jina Reader API、本地解析器和 Playwright 浏览器
- 🔄 **智能降级**：Jina Reader → 本地解析 → Playwright 浏览器三层自动切换
- 🌐 **突破限制**：使用 Playwright 处理 Cloudflare、验证码等访问限制
- 📦 **批量处理**：支持同时获取多个 URL
- 🎯 **灵活控制**：可选择强制使用特定解析方式
- 📝 **Markdown 输出**：自动转换为清晰的 Markdown 格式

## 安装

### 方法 1：从源码安装

```bash
# 克隆仓库
git clone https://github.com/zacfire/mcp-web-reader.git
cd mcp-web-reader

# 安装依赖
npm install

# 构建项目
npm run build

# 安装 Playwright 浏览器（必需）
npx playwright install chromium
```

### 方法 2：使用 npm 安装（如果已发布）

```bash
npm install -g mcp-web-reader
```

## 配置

在 Claude Desktop 的配置文件中添加：

**Windows**: `%APPDATA%\Claude\claude_desktop_config.json`

**macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`

```json
{
  "mcpServers": {
    "web-reader": {
      "command": "node",
      "args": ["/absolute/path/to/mcp-web-reader/dist/index.js"]
    }
  }
}
```

## 使用方法

配置完成后，在 Claude 中可以使用以下命令：

1.  **智能获取（推荐）**
    * "请获取 [https://example.com](https://example.com) 的内容"
    * 自动三层降级：Jina Reader → 本地解析 → Playwright 浏览器

2.  **批量获取**
    * "请获取这些网页：[url1, url2, url3]"
    * 每个URL都享受智能降级策略

3.  **强制使用 Jina Reader**
    * "使用 Jina Reader 获取 [https://example.com](https://example.com)"

4.  **强制使用本地解析**
    * "使用本地解析器获取 [https://example.com](https://example.com)"

5.  **强制使用浏览器模式**
    * "使用浏览器获取 [https://example.com](https://example.com)"
    * 直接跳过其他方式，适用于确定有访问限制的网站

## 支持的受限网站类型

✅ **微信公众号文章** - 自动绕过访问限制  
✅ **时代杂志、纽约时报** - 突破付费墙和地区限制  
✅ **Cloudflare 保护网站** - 通过真实浏览器绕过检测  
✅ **需要 JavaScript 渲染的页面** - 完整执行页面脚本  
✅ **有验证码/人机验证的网站** - 模拟真实用户行为

## 工具列表

-   `fetch_url` - 智能获取（三层降级：Jina → 本地 → Playwright）
-   `fetch_url_with_jina` - 强制使用 Jina Reader
-   `fetch_url_local` - 强制使用本地解析器  
-   `fetch_url_with_browser` - 强制使用 Playwright 浏览器（突破访问限制）
-   `fetch_multiple_urls` - 批量获取多个 URL

## 技术架构

### 智能降级策略
```
用户请求 URL
    ↓
1. Jina Reader API (最快，成功率高)
    ↓ 失败
2. 本地解析器 (Node.js + JSDOM)
    ↓ 检测到访问限制
3. Playwright 浏览器 (真实浏览器，突破限制)
```

### 访问限制检测
自动识别以下情况并启用浏览器模式：
- HTTP 状态码：403, 429, 503, 520-524
- 错误关键词：Cloudflare, CAPTCHA, Access Denied, Rate Limit
- 内容关键词：Security Check, Human Verification

## 开发

```bash
# 开发模式（自动重新编译）
npm run dev

# 构建
npm run build

# 测试运行
npm start

# 安装浏览器二进制文件（首次使用必需）
npx playwright install chromium
```

## 性能优化

- ⚡ **浏览器实例复用** - 避免重复启动开销
- 🚫 **资源过滤** - 阻止图片、样式表等不必要加载
- 🎯 **智能选择** - 优先使用快速方法，必要时才用浏览器
- 💾 **优雅关闭** - 正确清理浏览器资源

## 贡献

欢迎提交 Pull Request！

## 许可证

MIT License
npm package: @iflow-mcp/zcaceres-markdownify-server
Description: MCP Markdownify Server - Model Context Protocol Server for Converting Almost Anything to Markdown
Author: @zcaceres
Latest version: 1.0.0

# Markdownify MCP Server

> Help! I need someone with a Windows computer to help me add support for Markdownify-MCP on Windows. PRs exist but I cannot test them. Post [here](https://github.com/zcaceres/markdownify-mcp/issues/18) if interested.

![markdownify mcp logo](logo.jpg)

Markdownify is a Model Context Protocol (MCP) server that converts various file types and web content to Markdown format. It provides a set of tools to transform PDFs, images, audio files, web pages, and more into easily readable and shareable Markdown text.

<a href="https://glama.ai/mcp/servers/bn5q4b0ett"><img width="380" height="200" src="https://glama.ai/mcp/servers/bn5q4b0ett/badge" alt="Markdownify Server MCP server" /></a>

## Features

- Convert multiple file types to Markdown:
  - PDF
  - Images
  - Audio (with transcription)
  - DOCX
  - XLSX
  - PPTX
- Convert web content to Markdown:
  - YouTube video transcripts
  - Bing search results
  - General web pages
- Retrieve existing Markdown files

## Getting Started

1. Clone this repository
2. Install dependencies:
   ```
   pnpm install
   ```

Note: this will also install `uv` and related Python depdencies.

3. Build the project:
   ```
   pnpm run build
   ```
4. Start the server:
   ```
   pnpm start
   ```

## Development

- Use `pnpm run dev` to start the TypeScript compiler in watch mode
- Modify `src/server.ts` to customize server behavior
- Add or modify tools in `src/tools.ts`

## Usage with Desktop App

To integrate this server with a desktop app, add the following to your app's server configuration:

```js
{
  "mcpServers": {
    "markdownify": {
      "command": "node",
      "args": [
        "{ABSOLUTE PATH TO FILE HERE}/dist/index.js"
      ],
      "env": {
        // By default, the server will use the default install location of `uv`
        "UV_PATH": "/path/to/uv"
      }
    }
  }
}
```

## Available Tools

- `youtube-to-markdown`: Convert YouTube videos to Markdown
- `pdf-to-markdown`: Convert PDF files to Markdown
- `bing-search-to-markdown`: Convert Bing search results to Markdown
- `webpage-to-markdown`: Convert web pages to Markdown
- `image-to-markdown`: Convert images to Markdown with metadata
- `audio-to-markdown`: Convert audio files to Markdown with transcription
- `docx-to-markdown`: Convert DOCX files to Markdown
- `xlsx-to-markdown`: Convert XLSX files to Markdown
- `pptx-to-markdown`: Convert PPTX files to Markdown
- `get-markdown-file`: Retrieve an existing Markdown file. File extension must end with: *.md, *.markdown.
  
  OPTIONAL: set `MD_SHARE_DIR` env var to restrict the directory from which files can be retrieved, e.g. `MD_SHARE_DIR=[SOME_PATH] pnpm run start` 

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.