SEO Research MCP

[!CAUTION]

⚠️ Educational Use Only

This project is for educational and research purposes only.

• This tool interfaces with third-party services (Ahrefs, CapSolver)
• Users must comply with all applicable terms of service
• The authors do not endorse any use that violates third-party ToS
• Use responsibly and at your own risk

By using this software, you acknowledge that you understand and accept these terms.

🎯 What is this?

SEO Research MCP brings powerful SEO research capabilities directly into your AI coding assistant. Using the Model Context Protocol (MCP), it connects your IDE to Ahrefs' SEO data, allowing you to:

• Research competitor backlinks while coding
• Generate keyword ideas without leaving your editor
• Analyze traffic patterns for any website
• Check keyword difficulty before creating content

✨ Features

📋 Prerequisites

Before you start, you'll need:

1. Python 3.10 or higher

   python --version  # Should be 3.10+
   

2. CapSolver API Key (for CAPTCHA solving)

   👉 Get your API key here

📦 Installation

Option 1: From PyPI (Recommended)

pip install seo-mcp

Or using uv:

uv pip install seo-mcp

Option 2: From Source

git clone https://github.com/egebese/seo-research-mcp.git
cd seo-research-mcp
pip install -e .

🛠️ IDE Setup Guides

Choose your IDE and follow the setup instructions:

🟣 Claude Desktop

Step 1: Open Config File

1. Open Claude Desktop
2. Go to Settings → Developer → Edit Config

Step 2: Add Configuration

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "seo-research": {
      "command": "uvx",
      "args": ["--python", "3.10", "seo-mcp"],
      "env": {
        "CAPSOLVER_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

Step 3: Restart & Verify

1. Restart Claude Desktop
2. Look for the hammer/tools icon in the bottom-right corner

📁 Config file locations:| OS | Path ||----|------|| macOS | ~/Library/Application Support/Claude/claude_desktop_config.json || Windows | %APPDATA%\Claude\claude_desktop_config.json |

🔵 Claude Code (CLI)

Option A: Quick Setup (CLI)

# Add the MCP server
claude mcp add seo-research --scope user -- uvx --python 3.10 seo-mcp

# Set your API key
export CAPSOLVER_API_KEY="YOUR_API_KEY_HERE"

Option B: Config File

Add to ~/.claude.json:

{
  "mcpServers": {
    "seo-research": {
      "command": "uvx",
      "args": ["--python", "3.10", "seo-mcp"],
      "env": {
        "CAPSOLVER_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

Verify Installation

claude mcp list

🟢 Cursor

Global Setup (All Projects)

Create ~/.cursor/mcp.json:

{
  "mcpServers": {
    "seo-research": {
      "command": "uvx",
      "args": ["--python", "3.10", "seo-mcp"],
      "env": {
        "CAPSOLVER_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

Project Setup (Single Project)

Create .cursor/mcp.json in your project root with the same content.

Verify Installation

1. Go to File → Preferences → Cursor Settings
2. Select MCP in the sidebar
3. Check that seo-research appears under Available Tools

🌊 Windsurf

Step 1: Open Settings

• Mac: Cmd + Shift + P → "Open Windsurf Settings"
• Windows/Linux: Ctrl + Shift + P → "Open Windsurf Settings"

Step 2: Add Configuration

Navigate to Cascade → MCP Servers → Edit raw mcp_config.json:

{
  "mcpServers": {
    "seo-research": {
      "command": "uvx",
      "args": ["--python", "3.10", "seo-mcp"],
      "env": {
        "CAPSOLVER_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

📁 Config location: ~/.codeium/windsurf/mcp_config.json

💜 VS Code (GitHub Copilot)

⚠️ Requires VS Code 1.102+ with GitHub Copilot

Setup

Create .vscode/mcp.json in your workspace:

{
  "servers": {
    "seo-research": {
      "command": "uvx",
      "args": ["--python", "3.10", "seo-mcp"],
      "env": {
        "CAPSOLVER_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

Activate

1. Open the .vscode/mcp.json file
2. Click the Start button that appears
3. In Chat view, click Tools to toggle MCP tools
4. Use #tool_name in prompts to invoke tools

⚡ Zed

Setup

Add to your Zed settings.json:

{
  "context_servers": {
    "seo-research": {
      "command": {
        "path": "uvx",
        "args": ["--python", "3.10", "seo-mcp"],
        "env": {
          "CAPSOLVER_API_KEY": "YOUR_API_KEY_HERE"
        }
      }
    }
  }
}

Verify

1. Open Agent Panel settings
2. Check the indicator dot next to seo-research
3. Green dot = Server is active

📖 API Reference

get_backlinks_list(domain)

Get backlink data for any domain.

# Input
domain: str  # e.g., "example.com"

# Output
{
  "overview": {
    "domainRating": 76,
    "backlinks": 1500,
    "refDomains": 300
  },
  "backlinks": [
    {
      "anchor": "Example link",
      "domainRating": 76,
      "title": "Page title",
      "urlFrom": "https://source.com/page",
      "urlTo": "https://example.com/page",
      "edu": false,
      "gov": false
    }
  ]
}

keyword_generator(keyword, country?, search_engine?)

Generate keyword ideas from a seed keyword.

# Input
keyword: str        # Seed keyword
country: str        # Default: "us"
search_engine: str  # Default: "Google"

# Output
[
  {
    "keyword": "example keyword",
    "volume": 1000,
    "difficulty": 45
  }
]

get_traffic(domain_or_url, country?, mode?)

Estimate search traffic for a website.

# Input
domain_or_url: str  # Domain or full URL
country: str        # Default: "None" (all countries)
mode: str           # "subdomains" | "exact"

# Output
{
  "traffic": {
    "trafficMonthlyAvg": 50000,
    "costMontlyAvg": 25000
  },
  "top_pages": [...],
  "top_countries": [...],
  "top_keywords": [...]
}

keyword_difficulty(keyword, country?)

Get keyword difficulty score with SERP analysis.

# Input
keyword: str   # Keyword to analyze
country: str   # Default: "us"

# Output
{
  "difficulty": 45,
  "serp": [...]
}

⚙️ How It Works

┌──────────────┐     ┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│     Your     │     │   CapSolver  │     │    Ahrefs    │     │   Formatted  │
│    AI IDE    │────▶│   (CAPTCHA)  │────▶│     API      │────▶│    Results   │
└──────────────┘     └──────────────┘     └──────────────┘     └──────────────┘

1. Request → Your AI assistant calls an MCP tool
2. CAPTCHA → CapSolver handles Cloudflare verification
3. Data → Ahrefs API returns SEO data
4. Response → Formatted results appear in your IDE

🐛 Troubleshooting

🤝 Contributing

Contributions are welcome! Here's how you can help:

Ways to Contribute

• 🐛 Report Bugs - Found an issue? Open a bug report
• 💡 Suggest Features - Have an idea? Request a feature
• 📝 Improve Docs - Fix typos, clarify instructions, add examples
• 🔧 Submit Code - Bug fixes, new features, optimizations

Development Setup

# Clone the repo
git clone https://github.com/egebese/seo-research-mcp.git
cd seo-research-mcp

# Install dependencies
uv sync

# Run locally
python main.py

Pull Request Process

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to your branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Code Guidelines

• Keep code simple and readable
• Add comments for complex logic
• Test your changes before submitting
• Follow existing code style

📊 Star History

📄 License

This project is licensed under the MIT License with an educational use notice.

See LICENSE for full details.

🙏 Credits

This project is a fork of seo-mcp by @cnych.

Special thanks to the original author for creating this tool.