MCP Ahrefs

Ahrefs MCP Server for SAAGA

Quick Start with AI Assistant

Need help getting started? Have your AI coding assistant guide you!

Simply tell your AI assistant: "I have a MCP Ahrefs project. Please read and follow WORKING_WITH_SAAGA_PROMPT.md to help me understand and work with this MCP server."

For quick reference, the .ai-prompts.md file contains a condensed version of key patterns.

For detailed technical documentation, see docs/DECORATOR_PATTERNS.md.

Testing with MCP Inspector

Ready to test your MCP server? The MCP Inspector Guide provides:

Step-by-step setup instructions with virtual environment troubleshooting
Test examples for all included tools
JSON mode instructions for parallel tools
Common issues and solutions

Quick start:

source .venv/bin/activate  # Or use: uv shell
uv run mcp dev mcp_ahrefs/server/app.py

Testing with Claude CLI

This project includes a convenient test script for testing your MCP server with Claude:

# Test with a simple prompt
./test_mcp_with_claude.sh "List all available tools"

# Test a specific tool
./test_mcp_with_claude.sh "Run the echo_tool with message 'Hello World'"

# Test with multiple tools
./test_mcp_with_claude.sh "Test calculate_fibonacci with n=10 and echo_tool with message 'Done'"

# On Windows
.\test_mcp_with_claude.ps1 "List all available tools"

The script automatically:

Uses the generated mcp.integration_test.json configuration (created by cookiecutter)
Runs Claude with the Sonnet model
Includes proper MCP configuration flags
Provides colored output for better readability

MCP Integration Testing

This project includes comprehensive integration tests that validate tools work correctly with real MCP client interactions:

Running Integration Tests

# Run all integration tests
test-mcp-integration

# Run with verbose output
test-mcp-integration --verbose

# Test specific tool
test-mcp-integration --tool echo_tool

# List all available tools
test-mcp-integration --list

# Cross-platform scripts also available
./test_mcp_integration.sh        # Unix/Mac
.\test_mcp_integration.ps1        # Windows

What's Tested

The integration tests validate:

Tool Discovery: All tools are discoverable with correct schemas (no "kwargs" parameters)
Parameter Conversion: String parameters from MCP are converted to appropriate types
Error Handling: Invalid parameters and exceptions return proper error responses
SAAGA Integration: Decorators work correctly in the full MCP protocol flow
Protocol Compliance: Tools work with real MCP client connections

Generating Tests for New Tools

When you add a new tool, generate integration tests for it:

# Generate test template
generate-mcp-tests my_new_tool

# This creates a test template you can customize
# Add it to tests/integration/test_mcp_integration.py

Integration vs Unit Tests

Unit Tests (test_decorators.py): Test SAAGA decorators in isolation
Integration Tests (test_mcp_integration.py): Test complete MCP protocol flow with real client

Run both test suites to ensure full coverage:

# Run all tests
pytest

# Run only unit tests
pytest tests/test_decorators.py

# Run only integration tests
test-mcp-integration

Overview

This MCP server was generated using the SAAGA MCP Server Cookie Cutter template. It includes:

FastMCP Integration: Modern MCP framework with dual transport support
SAAGA Decorators: Automatic exception handling, logging, and parallelization
Platform-Aware Configuration: Cross-platform configuration management
Streamlit Admin UI: Web-based configuration and monitoring interface
SQLite Logging: Comprehensive logging with database persistence

Installation

Prerequisites

Python 3.12 or higher
UV - An extremely fast Python package manager

Install from Source

# Install UV (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh  # On macOS/Linux
# Or visit https://github.com/astral-sh/uv for Windows instructions

git clone <your-repository-url>
cd mcp_ahrefs
uv venv
uv sync

Development Installation

git clone <your-repository-url>
cd mcp_ahrefs
uv venv
uv sync --extra dev

Usage

Running the MCP Server

The server can be run in two modes:

1. STDIO Mode (for MCP clients like Claude Desktop)

# Run with default settings
uv run python -m mcp_ahrefs.server.app

# Run with custom log level
uv run python -m mcp_ahrefs.server.app --log-level DEBUG

# Run the server directly
uv run python mcp_ahrefs/server/app.py

uv run mcp_ahrefs-server

2. SSE Mode (for web-based clients)

# Run with SSE transport
uv run python -m mcp_ahrefs.server.app --transport sse --port 3001

# Run with custom host and port
uv run python -m mcp_ahrefs.server.app --transport sse --host 0.0.0.0 --port 8080

Command Line Options

uv run python -m mcp_ahrefs.server.app --help

Available options:

--transport: Choose between "stdio" (default) or "sse"
--host: Host to bind to for SSE transport (default: 127.0.0.1)
--port: Port to bind to for SSE transport (default: 3001)
--log-level: Logging level - DEBUG, INFO, WARNING, ERROR (default: INFO)

MCP Client Configuration

Claude Desktop Configuration

Add the following to your Claude Desktop MCP settings (claude_desktop_config.json):

{
  "mcpServers": {
    "mcp_ahrefs": {
      "command": "uv",
      "args": ["run", "python", "-m", "mcp_ahrefs.server.app"],
      "cwd": "/Users/jakub/Ragnarson/saaga/mcp_ahrefs"
    }
  }
}

Advanced Configuration Options

{
  "mcpServers": {
    "mcp_ahrefs": {
      "command": "uv",
      "args": [
        "run", "python", "-m", "mcp_ahrefs.server.app",
        "--log-level", "DEBUG"
      ],
      "cwd": "/Users/jakub/Ragnarson/saaga/mcp_ahrefs",
      "env": {
        "UV_PROJECT_ENVIRONMENT": "/Users/jakub/Ragnarson/saaga/mcp_ahrefs/.venv"
      }
    }
  }
}

Using with System Python (Alternative)

{
  "mcpServers": {
    "mcp_ahrefs": {
      "command": "/Users/jakub/Ragnarson/saaga/mcp_ahrefs/.venv/bin/python",
      "args": ["-m", "mcp_ahrefs.server.app"]
    }
  }
}

Using with uv tool

{
  "mcpServers": {
    "mcp_ahrefs": {
      "command": "uv",
      "args": ["--directory=/Users/jakub/Ragnarson/saaga/mcp_ahrefs", "run" ,"mcp_ahrefs-server"]
    }
  }
}

Configuration File Locations

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json

Admin UI

Launch the Streamlit admin interface:

uv run streamlit run mcp_ahrefs/ui/app.py

Dashboard

Streamlit Admin UI Dashboard

The dashboard provides:

Real-time server status monitoring
Project information and configuration overview
Quick access to common actions
System resource usage

Configuration Editor

Streamlit Admin UI Configuration

The configuration editor features:

Live configuration editing with validation
Diff preview showing pending changes
Export/import functionality (JSON & YAML formats)
Reset to defaults with confirmation dialog
Automatic server restart notifications

Log Viewer

Streamlit Admin UI Logs

The log viewer includes:

Date range filtering for historical analysis
Status filtering (success/error/all)
Tool-specific filtering
Export capabilities for further analysis
Real-time log updates

AI Assistant Instructions

When working with this MCP Ahrefs MCP server in an AI coding assistant (like Claude, Cursor, or GitHub Copilot):

Understanding the Server Architecture

This server uses SAAGA decorators that automatically wrap all MCP tools with:

Exception handling: All errors are caught and returned as structured error responses
Comprehensive logging: All tool invocations are logged with timing and parameters
Optional parallelization: Tools marked for parallel execution run concurrently

Key Points for AI Assistants

Tool Registration Pattern: Tools are registered with decorators already applied. Do NOT manually wrap tools with decorators - this is handled automatically in server/app.py.
Parameter Types: MCP passes all parameters as strings from the client. Ensure your tools handle type conversion:
def my_tool(count: str) -> dict: # Convert string to int count_int = int(count) return {"result": count_int * 2}
Error Handling: Tools can raise exceptions freely - the exception_handler decorator will catch them and return proper error responses.
Async Support: Both sync and async tools are supported. The decorators automatically detect and handle both patterns.
Logging: Check logs at the platform-specific data directory for debugging:
- macOS: ~/Library/Application Support/mcp_ahrefs/logs.db
- Linux: ~/.local/share/mcp_ahrefs/logs.db
- Windows: %APPDATA%/mcp_ahrefs/logs.db

Common Tasks

Adding a new tool:

# In mcp_ahrefs/tools/my_new_tool.py
def my_new_tool(param: str) -> dict:
    """Description of what this tool does."""
    # Implementation
    return {"result": "processed"}

# In mcp_ahrefs/tools/__init__.py
from .my_new_tool import my_new_tool
example_tools.append(my_new_tool)

Testing with MCP Inspector:

# From the project root
uv run mcp dev mcp_ahrefs/server/app.py

Debugging a tool:

Check the SQLite logs for error messages
Run with --log-level DEBUG for verbose output
Test directly with MCP Inspector to see parameter handling

Important Implementation Notes

The server uses the standard MCP SDK (from mcp.server.fastmcp import FastMCP)
Function signatures are preserved through careful decorator implementation
The register_tools() function in server/app.py handles all decorator application
Tools should return JSON-serializable Python objects (dict, list, str, int, etc.)

Configuration

Configuration files are stored in platform-specific locations:

macOS: ~/Library/Application Support/mcp_ahrefs/
Linux: ~/.local/share/mcp_ahrefs/
Windows: %APPDATA%/mcp_ahrefs/

Configuration Options

log_level: Logging level (INFO)
log_retention_days: Days to retain logs (30)
server_port: HTTP server port (3001)

Development

Project Structure

mcp_ahrefs/
├── mcp_ahrefs/
│   ├── config.py              # Platform-aware configuration
│   ├── server/
│   │   └── app.py             # FastMCP server with decorators
│   ├── tools/                 # Your MCP tools
│   ├── decorators/            # SAAGA decorators
│   └── ui/                    # Streamlit admin UI
├── tests/                     # Test suite
├── docs/                      # Documentation
└── pyproject.toml            # Project configuration

Adding New Tools

Create a new Python file in mcp_ahrefs/tools/
Define your tool function
Import and register it in server/app.py

Example:

# mcp_ahrefs/tools/my_tool.py
def my_tool(message: str) -> str:
    """Example MCP tool."""
    return f"Processed: {message}"

# The server will automatically apply SAAGA decorators

Running Tests

pytest tests/

Code Quality

This project uses several code quality tools:

# Format code
black mcp_ahrefs/
isort mcp_ahrefs/

# Lint code
flake8 mcp_ahrefs/
mypy mcp_ahrefs/

SAAGA Decorators

This server automatically applies three key decorators to your MCP tools:

Exception Handler: Graceful error handling with logging
Tool Logger: Comprehensive logging to SQLite database
Parallelize: Optional parallel processing for compute-intensive tools

Logging

Logs are stored in a SQLite database with the following schema:

timestamp: When the tool was called
tool_name: Name of the MCP tool
duration_ms: Execution time in milliseconds
status: Success/failure status
input_args: Tool input arguments
output_summary: Summary of tool output
error_message: Error details (if any)

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests for new functionality
Run the test suite
Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For issues and questions:

Create an issue on GitHub
Check the documentation in the docs/ directory
Review the test suite for usage examples

Acknowledgments

FastMCP for the MCP framework
SAAGA for the decorator patterns
Cookiecutter for the templating system

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Enables integration with Ahrefs SEO tools and services through MCP protocol. Provides automated exception handling, comprehensive logging, and parallel processing capabilities for SEO data analysis and operations.

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