FastMCP SonarQube Metrics

What is FastMCP SonarQube Metrics?

It is a set of tools built with the FastMCP framework that retrieve metrics, historical data, component tree details, and issues from SonarQube projects. Designed for developers, DevOps engineers, and analysts who need programmatic access to SonarQube data for reporting, analysis, and integration.

How to use FastMCP SonarQube Metrics?

Clone the repository, set environment variables (SONARQUBE_URL, SONARQUBE_TOKEN, TRANSPORT) in a .env file, install Python dependencies, then run server.py. Launch the included test client with client_test.py or integrate with Claude Desktop by configuring its MCP server settings. A hosted deployment is also available on Fronteir AI.

Key features of FastMCP SonarQube Metrics

• Health check: get_status verifies the SonarQube instance.
• Project management: create and delete SonarQube projects.
• List all accessible SonarQube projects, optionally filtered.
• Retrieve current metrics: bugs, vulnerabilities, code smells, coverage, duplication.
• Fetch historical metrics with optional date filters.
• Get component tree metrics with automatic pagination.
• Retrieve project issues filtered by type, severity, and resolution.

Use cases of FastMCP SonarQube Metrics

• Automate SonarQube metric collection for dashboards or reports.
• Integrate code quality data into CI/CD pipelines.
• Build custom analytics tools that consume SonarQube data.
• Perform health checks and monitor project status programmatically.
• Extract issue lists for triage or compliance tracking.

FAQ from FastMCP SonarQube Metrics

What are the prerequisites to use this server?

Python 3.7+, a running SonarQube instance with API access, a valid SonarQube API token, and installed packages: fastmcp, httpx, pydantic, python-dotenv.

What transport modes are supported?

The server supports both stdio and sse (Server-Sent Events) transport, configured via the TRANSPORT environment variable.

How do I configure this server with Claude Desktop?

Add a JSON entry to claude_desktop_config.json specifying the command uv with the directory pointing to the cloned repository and running server.py.

Is there a hosted version available?

Yes, a hosted deployment is available on Fronteir AI at the URL provided in the README.

Are there any known limitations?

The project is a work in progress; some features may not be perfect.