by universal-mcp
Provides a standardized interface for interacting with Ahrefs's tools and services through a unified API.
It implements an Ahrefs Universal MCP server that exposes Ahrefs's SEO tools and services via a unified, MCP‑compliant API.
uv
(pip install uv
).uv sync
.source .venv/bin/activate
.venv\Scripts\Activate
mcp dev src/universal_mcp_ahrefs/server.py
(note the address/port shown).mcp install src/universal_mcp_ahrefs/server.py
.src/universal_mcp_ahrefs/README.md
).uv
and the mcp
CLI.Q: Which Ahrefs API credentials are needed?
A: Set your Ahrefs API key in the .env
file (e.g., AHREFS_API_KEY=your_key
). The server reads it at runtime.
Q: Can I run the server in production? A: Yes, after local testing you can containerize the project or deploy the Python entry point with any WSGI/ASGI server.
Q: Where can I find the list of available tools?
A: In src/universal_mcp_ahrefs/README.md
and via the MCP inspector UI after starting the server.
Q: Is there a Node.js version?
A: The current implementation is Python‑based; you can still interact with it using the mcp
CLI from any language.
Q: How do I contribute?
A: Fork the repository, make changes, and submit a pull request. Ensure tests pass with pytest
.
This repository contains an implementation of an Ahrefs Universal MCP (Model Context Protocol) server. It provides a standardized interface for interacting with Ahrefs's tools and services through a unified API.
The server is built using the Universal MCP framework.
This implementation follows the MCP specification, ensuring compatibility with other MCP-compliant services and tools.
You can start using Ahrefs directly from agentr.dev. Visit agentr.dev/apps and enable Ahrefs.
If you have not used universal mcp before follow the setup instructions at agentr.dev/quickstart
The full list of available tools is at ./src/universal_mcp_ahrefs/README.md
Ensure you have the following before you begin:
pip install uv
)Follow the steps below to set up your development environment:
Sync Project Dependencies
uv sync
This installs all dependencies from pyproject.toml
into a local virtual environment (.venv
).
Activate the Virtual Environment
For Linux/macOS:
source .venv/bin/activate
For Windows (PowerShell):
.venv\Scripts\Activate
Start the MCP Inspector
mcp dev src/universal_mcp_ahrefs/server.py
This will start the MCP inspector. Make note of the address and port shown in the console output.
Install the Application
mcp install src/universal_mcp_ahrefs/server.py
.
├── src/
│ └── universal_mcp_ahrefs/
│ ├── __init__.py # Package initializer
│ ├── server.py # Server entry point
│ ├── app.py # Application tools
│ └── README.md # List of application tools
├── tests/ # Test suite
├── .env # Environment variables for local development
├── pyproject.toml # Project configuration
└── README.md # This file
This project is licensed under the MIT License.
Generated with MCP CLI — Happy coding! 🚀
Please log in to share your review and rating for this MCP.
Explore related MCPs that share similar capabilities and solve comparable challenges
by exa-labs
Provides real-time web search capabilities to AI assistants via a Model Context Protocol server, enabling safe and controlled access to the Exa AI Search API.
by elastic
Enables natural‑language interaction with Elasticsearch indices via the Model Context Protocol, exposing tools for listing indices, fetching mappings, performing searches, running ES|QL queries, and retrieving shard information.
by graphlit
Enables integration between MCP clients and the Graphlit platform, providing ingestion, extraction, retrieval, and RAG capabilities across a wide range of data sources and connectors.
by mamertofabian
Fast cross‑platform file searching leveraging the Everything SDK on Windows, Spotlight on macOS, and locate/plocate on Linux.
by cr7258
Provides Elasticsearch and OpenSearch interaction via Model Context Protocol, enabling document search, index management, cluster monitoring, and alias operations.
by liuyoshio
Provides natural‑language search and recommendation for Model Context Protocol servers, delivering rich metadata and real‑time updates.
by ihor-sokoliuk
Provides web search capabilities via the SearXNG API, exposing them through an MCP server for seamless integration with AI agents and tools.
by fatwang2
Provides web and news search, URL crawling, sitemap extraction, deep‑reasoning, and trending topic retrieval via Search1API, exposed as an MCP server for integration with AI clients.
by cnych
Provides SEO data retrieval via Ahrefs, exposing MCP tools for backlink analysis, keyword generation, traffic estimation, and keyword difficulty, with automated CAPTCHA solving and response caching.