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Prometheus MCP Server

Prometheus MCP Server

Access and analyze Prometheus metrics through standardized MCP interfaces.

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Prometheus MCP Server enables seamless access to Prometheus metrics by providing a standardized Model Context Protocol (MCP) interface. It allows AI assistants and compatible clients to execute PromQL queries, discover and explore metric data, and retrieve instant or range-based results. The server supports authentication via basic and bearer token mechanisms and can be easily deployed via Docker or integrated into multiple development environments.

Key Features

Execute PromQL queries against Prometheus

Discover and list available metrics

View metadata for specific metrics

Display instant and range query results

Customizable set of interactive tools for clients

Supports basic and bearer token authentication

Docker containerization for easy deployment

Integration with popular IDEs like VS Code and Claude Desktop

Configurable via environment variables

Standardized protocols for AI assistant compatibility

Use Cases

Querying Prometheus metrics through MCP-compatible AI assistants

Automating PromQL-based analysis workflows

Integrating Prometheus metrics exploration into IDEs

Securing access to metrics data with authentication methods

Deploying Prometheus analysis tools in containerized environments

Customizing the set of metrics tools available to AI assistants

Enabling teams to explore and analyze time-series metrics collaboratively

Embedding metrics querying functionality in development workflows

Supporting observability dashboards with standardized metric access

Providing context-aware metric data for software engineering assistants

README

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

Execute PromQL queries against Prometheus
Discover and explore metrics
- List available metrics
- Get metadata for specific metrics
- 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

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.

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:

json

{
  "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:

bash

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:

json

{
  "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:

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:

bash

# 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_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)

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:

bash

curl -LsSf https://astral.sh/uv/install.sh | sh

You can then create a virtual environment and install the dependencies with:

bash

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:

bash

# 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.

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 a specific metric
`get_targets`	Discovery	Get information about all scrape targets

License

MIT

Star History

Repository Owner

pab1it0

User

Repository Details

Language Python

Default Branch main

Size 798 KB

Contributors 11

License MIT License

MCP Verified Nov 12, 2025

Programming Languages

Python

97.65%

Dockerfile

2.35%

Topics

ai devops llm mcp model-context-protocol prometheus

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Prometheus MCP Server

Key Features

Use Cases

README

Prometheus MCP Server

Features

Getting Started

Prerequisites

Installation Methods

Configuration Options

Development

Testing

Tools

License

Star History

Repository Owner

Repository Details

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