Mrconfluencelinker MCP Server

🚀 GitLab PR Analysis MCP Server Project Documentation

GitLab PR Analysis MCP Server is a tool designed for automating the processing of Pull Requests on GitLab. It can be integrated into Confluence, facilitating teams to record and share development activities.

✨ Features

• Fetch Pull Request Details: Retrieve comprehensive information about pull requests from GitLab.
• Analyze Code Changes: Conduct in - depth analysis of code changes within pull requests.
• Generate Reports: Produce detailed reports including basic pull request information, code statistics, and file type analysis.
• Integrate with Confluence: Store analysis results in Confluence for easy team collaboration and sharing.

📦 Installation

Prerequisites

1. Python 3.8 or higher
2. GitLab account and API access permissions
3. Optional: Confluence account and API access permissions

Installation Steps

Method 1: Install dependencies using pip

pip install -r requirements.txt

Method 2: Manage dependencies using uv

uv add

Configuration

1. Copy .env.example to the project root directory and rename it to .env.
2. Edit the .env file and fill in the following environment variables:
   • GITLAB_URL: The URL of your GitLab instance.
   • GITLAB_TOKEN: The access token for the GitLab API.
   • CONFLUENCE_URL: The URL of your Confluence instance (optional).
   • CONFLUENCE_TOKEN: The access token for the Confluence API (optional).

Starting the Server

Method 1: Run server.py directly

python src/MRConfluenceLinker - mcp - server/server.py

Method 2: Configure and start via Claude Desktop

Add the following configuration to uv.json in Claude Desktop:

{
  "name": "gitlab - pr - analysis",
  "path": "/path/to/your/project/src/MRConfluenceLinker - mcp - server/server.py"
}

💻 Usage Examples

1. Fetch Pull Request Details (fetch_mr_details)

• Function: Retrieve detailed information about a pull request for a specified project from GitLab.
• Parameters:
  • project_id: The ID of the project to fetch.
  • mr_id: The ID of the pull request to fetch.

2. Analyze Code Changes (analyze_code_changes)

• Function: Analyze code changes in a pull request and generate a detailed change report.
• Parameters:
  • project_id: The ID of the project to analyze.
  • mr_id: The ID of the pull request to analyze.

3. Store in Confluence (store_in_confluence)

• Function: Store detailed information and analysis results of a pull request in Confluence.
• Parameters:
  • project_id: The ID of the project to store.
  • mr_id: The ID of the pull request to store.

Logging

The server generates logs in the mcp_server.log file during operation, which is useful for debugging and troubleshooting.

Error Handling

The project has built - in multiple error handling mechanisms, including:

• Authentication Failure: Verify the correctness of environment variables.
• Network Issues: Check API access permissions and the validity of URLs.
• Other Exceptions: Check the mcp_server.log file for more information.

Contribution Guidelines

1. Fork the project repository.
2. Create a feature branch.
3. Submit code changes.
4. Submit a Pull Request.

Project Structure

MRConfluenceLinker - mcp - server/
├── src/                           # Source code directory
│   └── MRConfluenceLinker - mcp - server/  # Main server package
│       ├── resources/            # Resource module
│       │   ├── __init__.py
│       │   ├── client.py        # Client implementation / GitLab PR integration
│       ├── server.py            # Main server implementation
│       └── mcp_server.log       # Server logs
├── __pycache__/                 # Python cache files
├── .git/                        # Git repository
├── .gitignore                   # Git ignore rules
├── CONTRIBUTING.md              # Contribution guidelines
├── LICENSE                      # Project license
├── README.md                    # Project documentation
└── requirements.txt             # Dependency management file

Contact

If you have any questions or suggestions, please contact the project maintainers.