s * `get_status`: Performs a health check on the configured SonarQube instance. * `list_projects`: Lists all accessible SonarQube projects, optionally filtered by name or key. * `get_sonarqube_metrics`: Retrieves specified metrics (bugs, vulnerabilities, code smells, coverage, duplication density) for a given SonarQube project key. * `get_sonarqube_metrics_history`: Retrieves historical metrics (bugs, vulnerabilities, code smells, coverage, duplication density) for a given SonarQube project using /api/measures/search_history. Optional date filters can be applied. * `get_sonarqube_component_tree_metrics`: Retrieves metric values for all components (e.g., files or directories) in a project using /api/measures/component_tree Automatically handles pagination to retrieve all results. * `get_project_issues`: Fetch SonarQube issues for a given project, optionally filtered by type, severity, and resolution status. Returns up to *limit* results (default: 10). ## Technology Stack * **Language:** Python * **Frameworks:** FastMCP * **Libraries:** httpx, pydantic, dotenv, asyncio, json, pathlib, typing, base64 * **Tools:** SonarQube API ## Directory Structure ``` ├── client_test.py - Client application for testing and interacting with the server. ├── server.py - Server application exposing tools to retrieve SonarQube metrics. ├── client_tool.py - Client with a graphical interface to interact with the SonarQube server. ├── client_langchain.py - Command-line client to interact with the FastMCP server and SonarQube tools via LangChain ├── .env - Environment configuration file (stores SonarQube URL and token). └── README.md - Project documentation. ``` ## Getting Started ### Prerequisites * Python 3.7+ * SonarQube instance with API access * A SonarQube API token with appropriate permissions * FastMCP installed (`pip install fastmcp`) * httpx installed (`pip install httpx`) * pydantic installed (`pip install pydantic`) * python-dotenv installed (`pip install python-dotenv`) ### General Build Steps 1. **Clone the repository:** `git clone ` 2. **Navigate to the project directory:** `cd fastmcp-sonarqube-metrics` 3. **Set up environment variables:** Create a `.env` file in the project root directory with the following content: ``` SONARQUBE_URL= SONARQUBE_TOKEN= TRANSPORT= GEMINI_API_KEY= GEMINI_MODEL= (Optional) ``` The schema is the same for *OpenAI* and *Groq*, if you want to use *AzureOpenAI*: ``` SONARQUBE_URL= SONARQUBE_TOKEN= TRANSPORT= AZURE_OPENAI_API_KEY= AZURE_OPENAI_ENDPOINT= AZURE_DEPLOYMENT= AZURE_API_VERSION= ``` Replace `` with the URL of your SonarQube instance (e.g., `http://localhost:9000`) and `` with your SonarQube API token. 4. **Run the server:** `python server.py` 5. **Run the client:** `python client_test.py` *(Optionally, only for test)* 6. **Connect to your client:** follow the [official documentation](https://modelcontextprotocol.io/quickstart/user#windows) ### Module Usage #### Server (`server.py`) The `server.py` module defines the FastMCP server that exposes tools for retrieving SonarQube metrics. It initializes the server, loads environment variables, defines the available tools, and handles communication with the SonarQube API. To use the server, you need to set the `SONARQUBE_URL` and `SONARQUBE_TOKEN` environment variables. The server is started by running the `server.py` script directly. #### Client (`client_test.py`) The `client_test.py` module defines the FastMCP client that interacts with the server. It prompts the user for a SonarQube project key, connects t

FastMCP SonarQube Metrics

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