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.

Forked Changes

This fork includes the following enhancements:

• Automatic timestamp conversion: All Unix timestamps in query results are automatically converted to human-readable ISO 8601 format (e.g., 2021-04-08T16:14:08Z instead of 1617898448)
• Dashboard visualization links: Query results include clickable links to view the data graphed in the Prometheus UI
• Enhanced LLM readability: Timestamps in readable format allow AI assistants to better reason about time-series data

Original repository: pab1it0/prometheus-mcp-server

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

• Automatic timestamp conversion - Unix timestamps are automatically converted to ISO 8601 format (e.g., 2021-04-08T16:14:08Z) for better readability

• Interactive dashboard links - Query results include links to visualize data in the Prometheus UI

• 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.

Installation

Prerequisites

• Python 3.10+ and uv package manager
• Prometheus server accessible from your environment
• Cursor IDE (or other MCP-compatible client)

Setup

1. Clone this repository:

Fork maintained by R. Kirkendall

git clone https://github.com/rkirkendall/prometheus-mcp-server.git
cd prometheus-mcp-server

2. Install dependencies:

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

3. Configure Cursor MCP:

Add to your .cursor/mcp.json:

{
  "mcpServers": {
    "prometheus-local": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/prometheus-mcp-server",
        "python",
        "-m",
        "prometheus_mcp_server.main"
      ],
      "env": {
        "PROMETHEUS_URL": "http://localhost:9090"
      }
    }
  }
}

Replace /path/to/prometheus-mcp-server with the actual path where you cloned this repository.

4. Restart Cursor to load the MCP server

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:

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.

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

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