# Prometheus MCP Server [![GitHub Container Registry](https://img.shields.io/badge/ghcr.io-pab1it0%2Fprometheus--mcp--server-blue?logo=docker)](https://github.com/users/pab1it0/packages/container/package/prometheus-mcp-server) [![GitHub Release](https://img.shields.io/github/v/release/pab1it0/prometheus-mcp-server)](https://github.com/pab1it0/prometheus-mcp-server/releases) [![Codecov](https://codecov.io/gh/pab1it0/prometheus-mcp-server/branch/main/graph/badge.svg)](https://codecov.io/gh/pab1it0/prometheus-mcp-server) ![Python](https://img.shields.io/badge/python-3.10%2B-blue) [![License](https://img.shields.io/github/license/pab1it0/prometheus-mcp-server)](https://github.com/pab1it0/prometheus-mcp-server/blob/main/LICENSE) Give AI assistants the power to query your Prometheus metrics. A [Model Context Protocol][mcp] (MCP) server that 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 ## Getting Started ### Prerequisites - Prometheus server accessible from your environment - MCP-compatible client (Claude Desktop, VS Code, Cursor, Windsurf, etc.) ### Installation Methods

Claude Desktop

Add to your Claude Desktop configuration: ```json { "mcpServers": { "prometheus": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "PROMETHEUS_URL", "ghcr.io/pab1it0/prometheus-mcp-server:latest" ], "env": { "PROMETHEUS_URL": "" } } } } ```

Claude Code

Install via the Claude Code CLI: ```bash claude mcp add prometheus --env PROMETHEUS_URL=http://your-prometheus:9090 -- docker run -i --rm -e PROMETHEUS_URL ghcr.io/pab1it0/prometheus-mcp-server:latest ```

VS Code / Cursor / Windsurf

Add to your MCP settings in the respective IDE: ```json { "prometheus": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "PROMETHEUS_URL", "ghcr.io/pab1it0/prometheus-mcp-server:latest" ], "env": { "PROMETHEUS_URL": "" } } } ```

Docker Desktop

The easiest way to run the Prometheus MCP server is through Docker Desktop:

1. **Via MCP Catalog**: Visit the [Prometheus MCP Server on Docker Hub](https://hub.docker.com/mcp/server/prometheus/overview) and click the button above 2. **Via MCP Toolkit**: Use Docker Desktop's MCP Toolkit extension to discover and install the server 3. Configure your connection using environment variables (see Configuration Options below)

Manual Docker Setup

Run directly with Docker: ```bash # With environment variables docker run -i --rm \ -e PROMETHEUS_URL="http://your-prometheus:9090" \ ghcr.io/pab1it0/prometheus-mcp-server:latest # With authentication docker run -i --rm \ -e PROMETHEUS_URL="http://your-prometheus:9090" \ -e PROMETHEUS_USERNAME="admin" \ -e PROMETHEUS_PASSWORD="password" \ ghcr.io/pab1it0/prometheus-mcp-server:latest ```

### Configuration Options | Variable | Description | Required | |----------|-------------|----------| | `PROMETHEUS_URL` | URL of your Prometheus server | Yes | | `PROMETHEUS_URL_SSL_VERIFY` | Set to False to disable SSL verification | No | | `PROMETHEUS_DISABLE_LINKS` | Set to True to disable Prometheus UI links in query results (saves context tokens) | No | | `PROMETHEUS_REQUEST_TIMEOUT` | Request timeout in seconds to prevent hanging requests (DDoS protection) | No (default: 30) | | `PROMETHEUS_USERNAME` | Username for basic authentication | No | | `PROMETHEUS_PASSWORD` | Password for basic authentication | No | | `PROMETHEUS_TOKEN` | Bearer token for authentication | No | | `ORG_ID` | Organization ID for multi-tenant setups | No | | `PROMETHEUS_MCP_SERVER_TRANSPORT` | Transport mode (stdio, http, sse) | No (default: stdio) | | `PROMETHEUS_MCP_BIND_HOST` | Host for HTTP transport | No (default: 127.0.0.1) | | `PROMETHEUS_MCP_BIND_PORT` | Port for HTTP transport | No (default: 8080) | | `PROMETHEUS_CUSTOM_HEADERS` | Custom headers as JSON string | No | | `TOOL_PREFIX` | Prefix for all tool names (e.g., `staging` results in `staging_execute_query`). Useful for running multiple instances targeting different environments in Cursor | No | ## Available Tools | Tool | Category | Description | | --- | --- | --- | | `health_check` | System | Health check endpoint for container monitoring and status verification | | `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 with pagination and filtering support | | `get_metric_metadata` | Discovery | Get metadata for one metric or bulk metadata with optional filtering | | `get_targets` | Discovery | Get information about all scrape targets | 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. ## Features - Execute PromQL queries against Prometheus - Discover and explore metrics - List available metrics - Get metadata for specific metrics - Search metric metadata by name or description in a single call - View instant query results - View range query results with different step intervals - Authentication support - Basic auth from environment variables - Bearer token auth from environment variables - Docker containerization support - Provide interactive tools for AI assistants ## Development Contributions are welcome! Please see our [Contributing Guide](CONTRIBUTING.md) for detailed information on how to get started, coding standards, and the pull request process. 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 . ``` ### 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 ``` When adding new features, please also add corresponding tests. ## License MIT ---