Prometheus MCP Server

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

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:

1. Via MCP Catalog: Visit the Prometheus MCP Server on Docker Hub and click the button above

1. Via MCP Toolkit: Use Docker Desktop's MCP Toolkit extension to discover and install the server

1. 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

Deploy to Kubernetes using the Helm chart from the OCI registry:

helm install prometheus-mcp-server \
  oci://ghcr.io/pab1it0/charts/prometheus-mcp-server \
  --version 1.1.1 \
  --set prometheus.url="http://prometheus:9090"

With authentication:

helm install prometheus-mcp-server \
  oci://ghcr.io/pab1it0/charts/prometheus-mcp-server \
  --version 1.1.1 \
  --set prometheus.url="http://prometheus:9090" \
  --set auth.username="admin" \
  --set auth.password="secret"

With a custom values file:

helm install prometheus-mcp-server \
  oci://ghcr.io/pab1it0/charts/prometheus-mcp-server \
  --version 1.1.1 \
  -f values.yaml

See the chart values for all available configuration options.

Configuration Options

Available Tools

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 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.

License

MIT