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
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
Ensure your Prometheus server is accessible from the environment where you'll run this MCP server.
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
- 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.
Building the Docker Image
Build the Docker image using:
docker build -t prometheus-mcp-server .
Running with Docker
You can run the server using Docker in several ways:
Using docker run directly:
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",
"prometheus-mcp-server"
],
"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.
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
| Tool |
Category |
Description |
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 |
get_metric_metadata |
Discovery |
Get metadata for a specific metric |
get_targets |
Discovery |
Get information about all scrape targets |
License
MIT
