What is Prometheus

The Prometheus MCP Server is a Model Context Protocol (MCP) server that allows access to and interaction with Prometheus metrics through standardized MCP interfaces. It facilitates executing PromQL queries and analyzing metrics data, making it useful for AI assistants.

Use cases

This server can be used for tasks such as executing real-time PromQL queries to retrieve metrics, exploring available metrics for insights, and integrating Prometheus functionality into AI-driven tools and applications, enhancing monitoring and data analysis capabilities.

How to use

To use the Prometheus MCP Server, set up the Prometheus server URL and, if necessary, authentication credentials in environment variables. Then, configure a client, like Claude Desktop, to run the MCP server with appropriate commands and options, either directly or via Docker for isolated deployment.

Key features

Key features include executing PromQL queries, discovering and exploring metrics, accessing metric metadata, supporting both basic and bearer token authentication, Docker containerization for easy deployment, and configurable interactive tools tailored for AI assistants.

Where to use

The server can be deployed in environments where Prometheus is used for monitoring, such as cloud-based infrastructures, on-premises setups, or any context that benefits from real-time metrics analysis and integration with AI tools for enhanced decision-making.

Prometheus MCP Server

A Model Context Protocol (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

• [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:

# 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

# Optional: For multi-tenant setups like Cortex, Mimir or Thanos
ORG_ID=your_organization_id

3. Add the server configuration to your client configuration file. For example, for Claude Desktop:

{
  "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.

Pre-built Docker Image

The easiest way to use this project is with the pre-built image from GitHub Container Registry:

docker pull ghcr.io/pab1it0/prometheus-mcp-server:latest

You can also use specific versions with tags:

docker pull ghcr.io/pab1it0/prometheus-mcp-server:1.0.0

Building the Docker Image Locally

If you prefer to build the image yourself:

docker build -t prometheus-mcp-server .

Running with Docker

You can run the server using Docker in several ways:

Using docker run with the pre-built image:

docker run -it --rm \
  -e PROMETHEUS_URL=http://your-prometheus-server:9090 \
  -e PROMETHEUS_USERNAME=your_username \
  -e PROMETHEUS_PASSWORD=your_password \
  ghcr.io/pab1it0/prometheus-mcp-server:latest

Using docker run with a locally built image:

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:

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:

{
  "mcpServers": {
    "prometheus": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e",
        "PROMETHEUS_URL",
        "-e",
        "PROMETHEUS_USERNAME",
        "-e",
        "PROMETHEUS_PASSWORD",
        "ghcr.io/pab1it0/prometheus-mcp-server:latest"
      ],
      "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.

Note about Docker implementation: The Docker setup has been updated to match the structure of the chess-mcp project, which has been proven to work correctly with Claude. The new implementation uses a multi-stage build process and runs the entry point script directly without an intermediary shell script. This approach ensures proper handling of stdin/stdout for MCP communication.

Development

Contributions are welcome! Please open an issue or submit a pull request if you have any suggestions or improvements.

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 .

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:

# 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

License

MIT