What is an MCP Server?

MCP (Model Context Protocol) server acts as a bridge, allowing AI models to access and interact with external data sources and tools. It standardizes how applications provide context to LLMs.

What are the prerequisites for using this MCP Server?

You need Python 3.8 or higher, a GitLab account with API access, and optionally, a Confluence account for storing analysis results.

How do I install the GitLab PR Analysis MCP Server?

Clone the repository, create a virtual environment, install dependencies using `pip install -r requirements.txt`, and configure the `.env` file with your credentials.

How do I start the MCP server?

Run `python src/MRConfluenceLinker-mcp-server/server.py` in your terminal after setting up the environment.

What credentials do I need to configure the server?

You need a GitLab personal access token with `api` scope and, optionally, a Confluence API token.

Can I use this server with Claude Desktop?

Yes, you can configure the server in Claude Desktop by modifying the `claude_desktop_config.json` file to include the server's command and arguments.

What tools are available in this MCP server?

The server provides tools like `fetch_mr_details`, `analyze_code_changes`, and `store_in_confluence` for interacting with GitLab and Confluence.

Where can I find the server logs?

The server generates detailed logs in `mcp_server.log` and outputs to stderr, which helps in debugging issues.

What kind of errors does the server handle?

The server handles errors such as missing environment variables, API authentication issues, network connectivity problems, and invalid project or merge request IDs.

How can I contribute to this project?

Fork the repository, create a feature branch, commit your changes, push to the branch, and create a pull request.

What license is this project released under?

This project is licensed under the MIT License.

How does this MCP Server integrate with UBOS?

The GitLab PR Analysis MCP Server integrates with the UBOS platform, allowing you to orchestrate AI Agents, connect them with your enterprise data, and build custom AI Agents for more sophisticated automation workflows.