Prometheus MCP Server

Give AI assistants the power to query your Prometheus metrics.

A Model Context Protocol (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.

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

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)

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_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 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

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