Seo Research

[!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:

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

# 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"

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

claude mcp list

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

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

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

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

📖 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 styl

README truncated. View full README on GitHub.