What can you do with this server?

The Prometheus MCP Server provides a Model Context Protocol (MCP) interface for AI assistants to interact with Prometheus metrics data. You can: * **Execute PromQL Queries**: Run both instant queries and range queries (with specified time ranges and step intervals) * **Discover Metrics**: List all available metrics and retrieve metadata for specific metrics * **Get Scrape Targets**: Retrieve information about all configured scrape targets * **Authentication Support**: Connect using Basic or Bearer token authentication * **Docker Deployment**: Easily deploy and run using Docker or Docker Compose * **Configurable Tools**: Customize available tools to optimize context window usage

Which integrations are available for this server?

Provides access to [Prometheus](/mcp/servers/integrations/prometheus) metrics and queries, allowing execution of PromQL queries, metrics discovery and exploration, viewing instant and range query results, and retrieving target information from a Prometheus server.

Prometheus MCP Server

GitHub Container Registry GitHub Release Codecov Python License

A (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.

Features

Execute PromQL queries against Prometheus
Discover and explore metrics
- List available metrics
- Get metadata for specific metrics
- 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

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.

Related MCP server: Prometheus MCP Server

Getting Started

Prerequisites

Prometheus server accessible from your environment
MCP-compatible client (Claude Desktop, VS Code, Cursor, Windsurf, etc.)

Installation Methods

Add to your Claude Desktop configuration:

{ "mcpServers": { "prometheus": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "PROMETHEUS_URL", "ghcr.io/pab1it0/prometheus-mcp-server:latest" ], "env": { "PROMETHEUS_URL": "<your-prometheus-url>" } } } }

Install via the Claude Code CLI:

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

Add to your MCP settings in the respective IDE:

{ "prometheus": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "PROMETHEUS_URL", "ghcr.io/pab1it0/prometheus-mcp-server:latest" ], "env": { "PROMETHEUS_URL": "<your-prometheus-url>" } } }

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

Via MCP Catalog: Visit the Prometheus MCP Server on Docker Hub and click the button above
Via MCP Toolkit: Use Docker Desktop's MCP Toolkit extension to discover and install the server
Configure your connection using environment variables (see Configuration Options below)

Run directly with Docker:

# 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_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

Development

Contributions are welcome! Please see our Contributing Guide for detailed information on how to get started, coding standards, and the pull request process.

This project uses uv to manage dependencies. Install uv following the instructions for your platform:

curl -LsSf https://astral.sh/uv/install.sh | sh

You can then create a virtual environment and install the dependencies with:

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:

# 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.

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 a specific metric
`get_targets`	Discovery	Get information about all scrape targets