Markdown MCP

🚀 Markdown MCP Server

A Model Context Protocol (MCP) server that extracts clean markdown content from web pages using Playwright. It provides a get_page_markdown tool to extract main content from any URL, filtering out non - content elements like navigation, headers, and footers.

🚀 Quick Start

This is a Model Context Protocol (MCP) server that leverages Playwright to extract clean markdown content from web pages. It offers a get_page_markdown tool which can extract the main content from any given URL while filtering out navigation, headers, footers, and other non - content elements.

✨ Features

• 🎯 Smart Content Extraction: Automatically identifies and extracts the main content from web pages.
• 🧹 Clean Output: Filters out navigation, headers, footers, sidebars, and advertisements.
• 🎨 Rich Markdown: Preserves formatting such as headings, bold, italic, code blocks, lists, and tables.
• 🖼️ Image Support: Optionally includes image references in markdown.
• 🔗 Link Support: Optionally includes hyperlinks in markdown.
• ⚡ Fast & Reliable: Uses Playwright for robust web scraping.
• 🔄 Dynamic Content: Handles JavaScript - heavy sites and dynamic content loading.
• 🛡️ Error Handling: Robust error handling with fallback extraction methods.

📦 Installation

1. Clone or download this repository:

   git clone <repository-url>
   cd markdown-mcp
   

2. Install dependencies:

   npm install
   

3. Install Playwright browsers:

   npx playwright install chromium
   

4. Make the script executable (optional):

   chmod +x markdown-mcp.js
   

💻 Usage Examples

Basic Usage

As an MCP Server, start the server:

node markdown-mcp.js

The server provides one tool: get_page_markdown

Tool Parameters

• url (required): The URL to extract markdown from.
• includeImages (optional, default: true): Whether to include image references in markdown.
• includeLinks (optional, default: true): Whether to include hyperlinks in markdown.
• waitForSelector (optional): CSS selector to wait for before extracting content (useful for dynamic content).
• timeout (optional, default: 30000): Navigation timeout in milliseconds.

Example Usage

{
  "name": "get_page_markdown",
  "arguments": {
    "url": "https://docs.confluent.io/cloud/current/flink/operate-and-deploy/monitor-statements.html",
    "includeImages": true,
    "includeLinks": true,
    "timeout": 30000
  }
}

Advanced Usage

Extract content from a specific section:

{
  "name": "get_page_markdown",
  "arguments": {
    "url": "https://example.com/article",
    "waitForSelector": ".main-content",
    "includeImages": false,
    "includeLinks": true
  }
}

Extract content with custom timeout:

{
  "name": "get_page_markdown",
  "arguments": {
    "url": "https://slow-loading-site.com",
    "timeout": 60000
  }
}

📚 Documentation

File Structure

This project includes two MCP server files optimized for different clients:

• markdown-mcp.js - Optimized for Claude Desktop.
• markdown-mcp-gemini.js - Optimized for Gemini CLI.

Both files provide the same get_page_markdown tool but are configured differently for optimal performance with each client.

Adding to AI Clients

This MCP server can be used with multiple AI clients that support the Model Context Protocol. Below are instructions for the most popular clients.

Claude Desktop Integration

To use this MCP server with Claude Desktop, you need to add it to your Claude Desktop configuration file.

Step 1: Locate Claude Desktop Configuration

• macOS: Configuration file: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: Configuration file: %APPDATA%\Claude\claude_desktop_config.json
• Linux: Configuration file: ~/.config/claude/claude_desktop_config.json

Step 2: Edit Configuration File

1. Open the configuration file in a text editor.
2. Add the markdown - mcp server to the mcpServers section.
3. Update the path to point to your markdown-mcp.js file.

Step 3: Configuration Examples

macOS Configuration

{
  "mcpServers": {
    "markdown-mcp": {
      "command": "node",
      "args": ["/Users/yourusername/path/to/markdown-mcp/markdown-mcp.js"],
      "env": {}
    }
  }
}

Windows Configuration

{
  "mcpServers": {
    "markdown-mcp": {
      "command": "node",
      "args": ["C:\\Users\\YourUsername\\path\\to\\markdown-mcp\\markdown-mcp.js"],
      "env": {}
    }
  }
}

Linux Configuration

{
  "mcpServers": {
    "markdown-mcp": {
      "command": "node",
      "args": ["/home/yourusername/path/to/markdown-mcp/markdown-mcp.js"],
      "env": {}
    }
  }
}

Step 4: Restart Claude Desktop

After updating the configuration file, restart Claude Desktop for the changes to take effect.

Step 5: Verify Installation

1. Open Claude Desktop.
2. Start a new conversation.
3. Try asking Claude to extract content from a webpage using the markdown - mcp tool.
4. Example: "Use markdown - mcp to extract content from https://example.com"

Troubleshooting

If the MCP server doesn't work:

1. Check the file path - Make sure the path to markdown-mcp.js is correct and the file exists.
2. Verify Node.js - Ensure Node.js is installed and accessible from the command line.
3. Check permissions - Make sure the script has execute permissions.
4. Test manually - Try running node markdown-mcp.js in the terminal to see if there are any errors.
5. Check Claude Desktop logs - Look for error messages in Claude Desktop's developer console.

Common Issues:

• Path not found: Double - check the file path in the configuration.
• Node.js not found: Make sure Node.js is installed and in your PATH.
• Permission denied: Run chmod +x markdown-mcp.js to make the script executable.
• Dependencies missing: Run npm install in the markdown - mcp directory.

Gemini CLI Integration

To use this MCP server with Gemini CLI, follow these steps:

Step 1: Install Gemini CLI

If you haven't already installed Gemini CLI:

npm install -g @google/gemini-cli

Verify the installation:

gemini --version

Step 2: Add MCP Server to Gemini CLI

Add your markdown - mcp server to Gemini CLI:

gemini mcp add markdown-mcp /Users/yourusername/path/to/markdown-mcp/markdown-mcp-gemini.js

Important: Replace /Users/yourusername/path/to/markdown-mcp/markdown-mcp-gemini.js with the actual path to your markdown-mcp-gemini.js file.

Step 3: Verify Integration

List all configured MCP servers to verify the integration:

gemini mcp list

You should see markdown-mcp listed among the servers.

Step 4: Test the Integration

Test the markdown - mcp server with Gemini CLI:

# Example: Extract content from a webpage
gemini "Use the markdown-mcp tool to extract content from https://example.com"

Or you can use the tool directly:

# If the tool is exposed as a command
gemini get_page_markdown "https://example.com"

Step 5: Complete Example - Extract and Save Markdown

# Extract content from a webpage and save to result.md
gemini "Use get_page_markdown to extract content from https://www.confluent.io/blog/event-driven-flink-agents-enterprise-ai/ and save the response as result.md"

This command will:

1. Use the get_page_markdown tool to extract clean markdown content from the Confluent blog post.
2. Save the extracted markdown content to a file named result.md in your current directory.
3. Provide you with a clean, readable markdown version of the webpage content.

Additional Examples:

# Extract content from documentation and save with custom filename
gemini "Use get_page_markdown to extract content from https://docs.confluent.io/cloud/current/flink/operate-and-deploy/monitor-statements.html and save it as flink-docs.md"

# Extract content from a GitHub repository README
gemini "Use get_page_markdown to extract content from https://github.com/microsoft/vscode and save as vscode-readme.md"

# Extract content with specific options
gemini "Use get_page_markdown with includeImages=false to extract content from https://example.com and save as clean-content.md"

Gemini CLI Troubleshooting

If the MCP server doesn't work with Gemini CLI:

1. Check the file path - Ensure the path to markdown-mcp-gemini.js is correct and absolute.
2. Verify Node.js - Make sure Node.js is accessible from the command line.
3. Check permissions - Ensure the script has execute permissions (chmod +x markdown-mcp-gemini.js).
4. Test the server manually - Run node markdown-mcp-gemini.js to check for errors.
5. Check Gemini CLI logs - Look for error messages in the Gemini CLI output.

Common Gemini CLI Issues:

• Path not found: Use absolute paths when adding the MCP server.
• Permission denied: Run chmod +x markdown-mcp-gemini.js to make the script executable.
• Node.js not found: Ensure Node.js is installed and in your PATH.
• Server not responding: Check if the server starts correctly with node markdown-mcp-gemini.js.

Using with Multiple AI Clients

You can use the same markdown - mcp server with multiple AI clients simultaneously. The MCP server is designed to handle multiple concurrent requests efficiently.

Benefits of Multi - Client Setup

• Flexibility: Use the same tool with different AI models.
• Efficiency: Share the same server instance across clients.
• Consistency: Get the same extraction quality regardless of the AI client.
• Resource optimization: No need to run multiple server instances.

Setup for Multiple Clients

1. Set up Claude Desktop using markdown-mcp.js (as described above).
2. Set up Gemini CLI using markdown-mcp-gemini.js (as described above).
3. Both clients can use their respective server files - optimized for each client.

Usage Examples

With Claude Desktop:

Use markdown-mcp to extract content from https://docs.confluent.io/cloud/current/flink/operate-and-deploy/monitor-statements.html

With Gemini CLI:

# Extract and save to file
gemini "Use get_page_markdown to extract content from https://docs.confluent.io/cloud/current/flink/operate-and-deploy/monitor-statements.html and save as result.md"

# Or just extract without saving
gemini "Use get_page_markdown to extract content from https://docs.confluent.io/cloud/current/flink/operate-and-deploy/monitor-statements.html"

Performance Considerations

• The server handles multiple concurrent requests efficiently.
• Each request uses a fresh browser context for security.
• Memory usage scales with the number of concurrent requests.
• Typical response time: 5 - 15 seconds per request.

🔧 Technical Details

Testing

The server has been tested and verified to work correctly with various websites including:

• ✅ Documentation sites (Confluent, GitHub, etc.)
• ✅ News articles and blog posts
• ✅ Technical documentation with code examples
• ✅ E - commerce pages and product descriptions
• ✅ JavaScript - heavy sites with dynamic content

Tested Features

• ✅ Extracts headings, paragraphs, and text content.
• ✅ Preserves bold and italic formatting.
• ✅ Handles code blocks and inline code.
• ✅ Processes lists (ordered and unordered).
• ✅ Extracts tables with proper formatting.
• ✅ Filters out navigation and footer content.
• ✅ Handles images and links (when enabled).
• ✅ Responds to MCP protocol requests.
• ✅ Works with dynamic content and JavaScript - heavy sites.

Manual Testing

You can test the server manually by running:

# Test with a simple URL
node -e "
const { spawn } = require('child_process');
const server = spawn('node', ['markdown-mcp.js'], { stdio: ['pipe', 'pipe', 'pipe'] });
const request = {
  jsonrpc: '2.0',
  id: 1,
  method: 'tools/call',
  params: {
    name: 'get_page_markdown',
    arguments: { url: 'https://example.com' }
  }
};
server.stdin.write(JSON.stringify(request) + '\n');
setTimeout(() => {
  server.kill();
  console.log('Test completed');
}, 10000);
"

Supported Websites

This MCP server works well with:

• Documentation sites: Confluent, GitHub, GitLab, etc.
• News and blogs: Most major news sites and blogs.
• Technical content: Stack Overflow, Medium, Dev.to.
• E - commerce: Product pages and descriptions.
• Academic content: Research papers and articles.
• Social media: Twitter threads, LinkedIn articles.

Performance

• Typical extraction time: 5 - 15 seconds depending on page complexity.
• Memory usage: ~50 - 100MB per extraction.
• Supported content size: Up to several MB of text content.
• Concurrent requests: Handles multiple requests efficiently.

Requirements

• Node.js: Version 18 or higher.
• Playwright: Chromium browser (installed automatically).
• Memory: At least 512MB available RAM.
• Disk space: ~200MB for Playwright browser.

Security Considerations

• The server runs in headless mode for security.
• No cookies or persistent data is stored.
• Each request uses a fresh browser context.
• Network requests are limited by timeout settings.
• No sensitive data is logged or stored.

🤝 Contributing

1. Fork the repository.
2. Create a feature branch.
3. Make your changes.
4. Test thoroughly.
5. Submit a pull request.

💬 Support

If you encounter issues:

1. Check the troubleshooting section above.
2. Verify all requirements are met.
3. Test with a simple URL first.
4. Check Claude Desktop logs for error messages.
5. Open an issue with detailed error information.