Prometheus MCP Server MCP Server
io.github.pab1it0/prometheus-mcp-server
Query Prometheus metrics and execute PromQL through AI assistants with MCP.
What is the Prometheus MCP Server MCP server?
The Prometheus MCP Server is a Model Context Protocol server that gives AI assistants like Claude and Cursor the ability to query your Prometheus metrics and execute PromQL queries. It provides standardized interfaces for metric discovery, instant/range queries, and target inspection against your Prometheus instance.
This server bridges AI assistants and Prometheus, enabling them to explore metrics, execute PromQL queries, and analyze time-series data. Use it to let Claude or Cursor investigate system metrics, troubleshoot performance issues, and answer questions about your monitoring data without leaving your IDE.
How to install Prometheus MCP Server
Copy-paste configuration for popular MCP clients.
PROMETHEUS_URLrequiredPrometheus server URL (e.g., http://localhost:9090)
PROMETHEUS_URL_SSL_VERIFYSet to False to disable SSL verification
PROMETHEUS_DISABLE_LINKSSet to True to disable Prometheus UI links in query results (saves context tokens in MCP clients)
PROMETHEUS_USERNAMEUsername for Prometheus basic authentication
PROMETHEUS_PASSWORDsecretPassword for Prometheus basic authentication
PROMETHEUS_TOKENsecretBearer token for Prometheus authentication
ORG_IDOrganization ID for multi-tenant Prometheus setups
PROMETHEUS_CLIENT_CERTPath to client certificate file for mutual TLS authentication
PROMETHEUS_CLIENT_KEYPath to client private key file for mutual TLS authentication
PROMETHEUS_MCP_SERVER_TRANSPORTMCP server transport type (stdio, http, or sse)
PROMETHEUS_MCP_BIND_HOSTHost address for HTTP/SSE transport (default: 127.0.0.1)
PROMETHEUS_MCP_BIND_PORTPort number for HTTP/SSE transport (default: 8080)
PROMETHEUS_MCP_STATELESS_HTTPEnable stateless HTTP mode for multi-replica support (default: false)
PROMETHEUS_CUSTOM_HEADERSCustom headers as JSON string to include in Prometheus requests
Tools & capabilities
Tools this server exposes to the agent.
health_check— Health check endpoint for container monitoring and status verificationexecute_query— Execute a PromQL instant query against Prometheusexecute_range_query— Execute a PromQL range query with start time, end time, and step intervallist_metrics— List all available metrics in Prometheus with pagination and filtering supportget_metric_metadata— Get metadata for one metric or bulk metadata with optional filteringget_targets— Get scrape targets, with server-side state/scrape_pool filtering and optional pagination
Use cases
- Execute PromQL queries to investigate system performance metrics and anomalies
- Discover available metrics and explore their metadata to understand your monitoring setup
- Retrieve scrape target status to verify which endpoints are being monitored
- Analyze time-series data with range queries across custom time windows and step intervals
- Troubleshoot infrastructure issues by querying metrics directly from your IDE
Prometheus MCP Server MCP server FAQ
It's an MCP server that connects AI assistants to your Prometheus instance, allowing them to execute PromQL queries, list metrics, and explore monitoring data.
Yes, the Prometheus MCP Server is open-source under the MIT license.
Add the server to your MCP configuration with the Docker image `ghcr.io/pab1it0/prometheus-mcp-server:latest` and set the `PROMETHEUS_URL` environment variable to your Prometheus server URL.
Basic authentication (username/password), bearer token authentication, mutual TLS (client certificate/key), and custom headers.
Docker is the recommended method, but you can also deploy it to Kubernetes using the provided Helm chart or run it directly if you have Python 3.10+.
Yes, use the `TOOL_PREFIX` environment variable to prefix tool names, allowing you to run multiple instances in the same client targeting different environments.
README (reference)
Source of truth, from the repository.
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
<details> <summary><b>Claude Desktop</b></summary>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>"
}
}
}
}
</details>
<details>
<summary><b>Claude Code</b></summary>
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
</details>
<details>
<summary><b>VS Code / Cursor / Windsurf</b></summary>
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>"
}
}
}
</details>
<details>
<summary><b>Docker Desktop</b></summary>
The easiest way to run the Prometheus MCP server is through Docker Desktop:
<a href="https://hub.docker.com/open-desktop?url=https://open.docker.com/dashboard/mcp/servers/id/prometheus/config?enable=true"> <img src="https://img.shields.io/badge/+%20Add%20to-Docker%20Desktop-2496ED?style=for-the-badge&logo=docker&logoColor=white" alt="Add to Docker Desktop" /> </a>-
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)
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
</details>
<details>
<summary><b>Helm Chart (Kubernetes)</b></summary>
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.
</details>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 scrape targets, with server-side state/scrape_pool filtering and optional pagination |
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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