Prometheus MCP Server

# Prometheus MCP Server A [Model Context Protocol][mcp] (MCP) server for Prometheus. This provides access to your Prometheus metrics and queries through standardized MCP interfaces, allowing AI assistants to execute PromQL queries and analyze your metrics data. [mcp]: https://modelcontextprotocol.io ## Features - [x] Execute PromQL queries against Prometheus - [x] Discover and explore metrics - [x] List available metrics - [x] Get metadata for specific metrics - [x] View instant query results - [x] View range query results with different step intervals - [x] Authentication support - [x] Basic auth from environment variables - [x] Bearer token auth from environment variables - [x] Docker containerization support - [x] Provide interactive tools for AI assistants The list of tools is configurable, so you can choose which tools you want to make available to the MCP client. This is useful if you don't use certain functionality or if you don't want to take up too much of the context window. ## Usage 1. Ensure your Prometheus server is accessible from the environment where you'll run this MCP server. 2. Configure the environment variables for your Prometheus server, either through a `.env` file or system environment variables: ```env # Required: Prometheus configuration PROMETHEUS_URL=http://your-prometheus-server:9090 # Optional: Authentication credentials (if needed) # Choose one of the following authentication methods if required: # For basic auth PROMETHEUS_USERNAME=your_username PROMETHEUS_PASSWORD=your_password # For bearer token auth PROMETHEUS_TOKEN=your_token ``` 3. Add the server configuration to your client configuration file. For example, for Claude Desktop: ```json { "mcpServers": { "prometheus": { "command": "uv", "args": [ "--directory", "<full path to prometheus-mcp-server directory>", "run", "src/prometheus_mcp_server/main.py" ], "env": { "PROMETHEUS_URL": "http://your-prometheus-server:9090", "PROMETHEUS_USERNAME": "your_username", "PROMETHEUS_PASSWORD": "your_password" } } } } ``` > Note: if you see `Error: spawn uv ENOENT` in Claude Desktop, you may need to specify the full path to `uv` or set the environment variable `NO_UV=1` in the configuration. ## Docker Usage This project includes Docker support for easy deployment and isolation. ### Building the Docker Image Build the Docker image using: ```bash docker build -t prometheus-mcp-server . ``` ### Running with Docker You can run the server using Docker in several ways: #### Using docker run directly: ```bash docker run -it --rm \ -e PROMETHEUS_URL=http://your-prometheus-server:9090 \ -e PROMETHEUS_USERNAME=your_username \ -e PROMETHEUS_PASSWORD=your_password \ prometheus-mcp-server ``` #### Using docker-compose: Create a `.env` file with your Prometheus credentials and then run: ```bash docker-compose up ``` ### Running with Docker in Claude Desktop To use the containerized server with Claude Desktop, update the configuration to use Docker with the environment variables: ```json { "mcpServers": { "prometheus": { "command": "docker", "args": [ "run", "--rm", "-i", "-e", "PROMETHEUS_URL", "-e", "PROMETHEUS_USERNAME", "-e", "PROMETHEUS_PASSWORD", "prometheus-mcp-server" ], "env": { "PROMETHEUS_URL": "http://your-prometheus-server:9090", "PROMETHEUS_USERNAME": "your_username", "PROMETHEUS_PASSWORD": "your_password" } } } } ``` This configuration passes the environment variables from Claude Desktop to the Docker container by using the `-e` flag with just the variable name, and providing the actual values in the `env` object. ## Development Contributions are welcome! Please open an issue or submit a pull request if you have any suggestions or improvements. This project uses [`uv`](https://github.com/astral-sh/uv) to manage dependencies. Install `uv` following the instructions for your platform: ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` You can then create a virtual environment and install the dependencies with: ```bash uv venv source .venv/bin/activate # On Unix/macOS .venv\Scripts\activate # On Windows uv pip install -e . ``` ## Project Structure The project has been organized with a `src` directory structure: ``` prometheus-mcp-server/ ├── src/ │ └── prometheus_mcp_server/ │ ├── __init__.py # Package initialization │ ├── server.py # MCP server implementation │ ├── main.py # Main application logic ├── Dockerfile # Docker configuration ├── docker-compose.yml # Docker Compose configuration ├── .dockerignore # Docker ignore file ├── pyproject.toml # Project configuration └── README.md # This file ``` ### Testing The project includes a comprehensive test suite that ensures functionality and helps prevent regressions. Run the tests with pytest: ```bash # Install development dependencies uv pip install -e ".[dev]" # Run the tests pytest # Run with coverage report pytest --cov=src --cov-report=term-missing ``` Tests are organized into: - Configuration validation tests - Server functionality tests - Error handling tests - Main application tests When adding new features, please also add corresponding tests. ### Tools | Tool | Category | Description | | --- | --- | --- | | `execute_query` | Query | Execute a PromQL instant query against Prometheus | | `execute_range_query` | Query | Execute a PromQL range query with start time, end time, and step interval | | `list_metrics` | Discovery | List all available metrics in Prometheus | | `get_metric_metadata` | Discovery | Get metadata for a specific metric | | `get_targets` | Discovery | Get information about all scrape targets | ## License MIT --- [mcp]: https://modelcontextprotocol.io