GitLab PR Analysis MCP Server
@CodeByWaqas
About GitLab PR Analysis MCP Server
No overview available yet
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"MRConfluenceLinker-mcp-server": {
"command": "python",
"args": [
"-m",
"venv",
".venv"
]
}
}
}Tools
3`project_id`: The GitLab project ID
`project_id`: The GitLab project ID
`project_id`: The GitLab project ID
Overview
What is GitLab PR Analysis MCP Server?
An MCP server that integrates GitLab merge request analysis with Confluence documentation. It fetches merge request details, analyzes code changes, and stores results in Confluence pages. Designed for developers and teams using GitLab who want automated code review summaries.
How to use GitLab PR Analysis MCP Server?
Install dependencies, create a virtual environment, and copy the .env.example file to .env with your GitLab token, project ID, and optional Confluence credentials. Start the server with python src/MRConfluenceLinker-mcp-server/server.py or configure it as an MCP server in Claude Desktop. The server listens for commands via stdin/stdout.
Key features of GitLab PR Analysis MCP Server?
- Fetch details of a specific or all merge requests
- Analyze code changes in a merge request
- Store analysis results in Confluence
- Generate reports with code change statistics and file type analysis
- Comprehensive logging for debugging
Use cases of GitLab PR Analysis MCP Server?
- Automate merge request summaries for code reviews
- Document code changes in Confluence for team visibility
- Track file‑type changes across multiple merge requests
- Integrate GitLab analysis into an MCP‑aware assistant like Claude Desktop
FAQ from GitLab PR Analysis MCP Server
What credentials are required?
A GitLab personal access token with api scope and a GitLab project ID. For Confluence integration, provide a Confluence URL, username, API token, and space key.
What are the runtime dependencies?
Python 3.8 or higher, plus packages: mcp[cli], python-gitlab, python-dotenv, atlassian-python-api, and requests.
Does the server handle errors?
Yes, it includes handling for missing environment variables, API authentication failures, network issues, invalid project/merge request IDs, and Confluence permission problems.
What tools does the server expose?
Three tools: fetch_mr_details, analyze_code_changes, and store_in_confluence. Each accepts a project ID and optional merge request ID.
Where are logs stored?
Logs are written to mcp_server.log and also output to stderr for debugging GitLab API access, Confluence integration, and code analysis.
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