What is the health check endpoint?
The `sonar_health_check` endpoint returns the current health status and connection information of your SonarQube server.
@dhanush-dev01
Basic information
The SonarQube MCP (Model Context Protocol) Server is a custom-built AI-powered backend designed to integrate with SonarQube and enable intelligent, context-aware interactions between static code analysis and modern AI agents like GitHub Copilot, VS Code Agents, or any LLM-powered
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"sonarqubemcp": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"--init",
"-e",
"SONARQUBE_URL",
"-e",
"SONARQUBE_TOKEN",
"-e",
"PROJECT_KEY",
"dhanushhypervisor/sonarqube_mcp_server"
],
"env": {
"SONARQUBE_URL": "",
"SONARQUBE_TOKEN": "",
"PROJECT_KEY": ""
}
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
This server bridges SonarQube APIs to MCP-compliant clients, enabling you to monitor and analyze code quality. It provides tools for health checks, token validation, project issues, project listing, and project metrics.
Set the environment variables SONARQUBE_URL, SONARQUBE_TOKEN, and PROJECT_KEY. Install the required dependencies (FastMCP, Requests) from requirements.txt and run the server using Python. Alternatively, use Docker by passing the environment variables at runtime. The server can be integrated with VS Code via the MCP settings.
The `sonar_health_check` endpoint returns the current health status and connection information of your SonarQube server.
Use the `get_token_info` endpoint. It validates your `SONARQUBE_TOKEN` and returns authentication status and associated user details.
The server returns a detailed error message. Error handling covers invalid project keys, connection issues, authentication failures, and permission problems.
Yes. Set the required environment variables, install the dependencies listed in `requirements.txt`, and run the Python server directly.
Python 3.10 and the `uv` package manager are used for dependency management and execution.

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AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
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