pab1it0/prometheus-mcp-server
🐍 ☁️ - Query and analyze Prometheus, open-source monitoring system.
Quick Install
{
"mcpServers": {
"pab1it0-prometheus-mcp-server": {
"command": "npx",
"args": [
"-y",
"pab1it0-prometheus-mcp-server"
]
}
}
}Using an AI coding agent (Claude Code, Cursor, etc.)? Copy a ready-made prompt that tells it to fetch the setup instructions and install this server for you.
Documentation Overview
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
Claude Desktop
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>"
}
}
}
}
Claude Code
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
VS Code / Cursor / Windsurf
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>"
}
}
}
Docker Desktop
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)
Manual Docker Setup
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
Helm Chart (Kubernetes)
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 1.0.0 \
--set prometheus.url="http://prometheus:9090"
With authentication:
helm install prometheus-mcp-server \
oci://ghcr.io/pab1it0/charts/prometheus-mcp-server \
--version 1.0.0 \
--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 1.0.0 \
-f values.yaml
See the chart values for all available configuration options.
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 |
PROMETHEUS_CLIENT_CERT | Path to client certificate file for mutual TLS authentication | No |
PROMETHEUS_CLIENT_KEY | Path to client private key file for mutual TLS authentication | No |
REQUESTS_CA_BUNDLE | Path to CA bundle file for verifying the server's TLS certificate (standard requests library env var) | 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_MCP_STATELESS_HTTP | Enable stateless HTTP mode for multi-replica support | No (default: False) |
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