Prometheus MCP Server

prometheus-mcp

MCP server for Prometheus metrics and observability. Give Claude (or any MCP-capable agent) read access to your Prometheus instance — query metrics with PromQL, inspect active alerts, and explore scrape targets — without leaving the conversation.

Why another Prometheus MCP?

The existing Prometheus integrations require custom scripts or direct API knowledge. This server:

• Speaks the standard Model Context Protocol over stdio — works with Claude Desktop, Claude Code, Cursor, and any MCP client.
• Is read-only: all 5 tools carry readOnlyHint: true — zero risk of modifying Prometheus data.
• Returns dual-channel output: structured JSON (structuredContent) for programmatic use + Markdown (content) for human-readable display.
• Has actionable error messages that name the exact env var to fix and suggest a next step.
• Supports Bearer token, HTTP Basic auth, or no auth (common for internal deployments).

Tools

v4.0 Advanced Alert Correlation Features

Version 4.0 introduces powerful new capabilities for AI agents to autonomously investigate production errors:

Cross-Instance Alert Correlation

• Automatically identify related alerts across multiple Prometheus instances
• Group alerts by service identifiers to understand incident scope
• Detect cascading alert patterns with directional dependency inference

Root Cause Analysis

• Anomaly detection in metrics with automatic seasonality adjustment
• Dependency chain traversal from symptoms to potential root causes
• Change point detection correlating alerts with recent deployments or config changes
• Ranked root cause candidates based on evidence strength and impact analysis

Dependency Mapping & Health

• Dynamic service dependency maps built from traffic correlation analysis
• Cross-cluster dependency visualization showing service interoperation
• Synthetic health probing to assess dependency resilience
• Load shedding recommendations based on dependency fragility

Trend Analysis & Benchmarking

• Historical pattern recognition for recurring alert schedules
• Capacity forecasting to predict resource exhaustion
• MTTR benchmarking comparing resolution times against historical data
• Deviation detection triggering higher-priority notifications for pattern breaks

Integrated Analysis Tool

• New federation_analyze_alerts tool combining all v4.0 features
• Unified output format optimized for AI agent consumption
• Comprehensive incident context in a single tool call

Installation

pip install prometheus-mcp

Or run directly without installing:

uvx prometheus-mcp

Configuration

All configuration is via environment variables:

Copy .env.example to .env and fill in your values.

Claude Desktop / Claude Code setup

Add to your MCP config (claude_desktop_config.json or .claude/mcp.json):

{
  "mcpServers": {
    "prometheus": {
      "command": "prometheus-mcp",
      "env": {
        "PROMETHEUS_URL": "https://prometheus.example.com",
        "PROMETHEUS_TOKEN": "your-token-here"
      }
    }
  }
}

Or with uvx (no install required):

{
  "mcpServers": {
    "prometheus": {
      "command": "uvx",
      "args": ["prometheus-mcp"],
      "env": {
        "PROMETHEUS_URL": "https://prometheus.example.com"
      }
    }
  }
}

Docker

docker run --rm -e PROMETHEUS_URL=https://prometheus.example.com prometheus-mcp

Example queries

Once configured, ask Claude:

• "What metrics does Prometheus have about HTTP requests?"
• "What is the current request rate for the payment service?"
• "Show me CPU usage over the last hour with 5-minute resolution"
• "Are there any firing alerts? What's their severity?"
• "Which scrape targets are currently down and why?"
• "How many node-exporter instances are up?"

Tool usage guide

prometheus_list_metrics

Returns all metric names Prometheus knows about. Use pattern to filter by substring (case-insensitive). Start here when you don't know which metrics are available. Output is capped at 500 metrics with a truncation hint.

prometheus_query

Execute an instant PromQL expression and get current values. Returns result type (vector/scalar/matrix/string), sample count, and per-sample labels and values.

Parameters:

• query (required) — PromQL expression, e.g. up, rate(http_requests_total[5m])
• time (optional) — RFC3339 or Unix timestamp; defaults to now

prometheus_query_range

Execute a PromQL expression over a time window. Returns one series per matching time series with timestamped values. Total data points across all series are capped at 5000.

Parameters:

• query (required) — PromQL expression
• start / end (required) — RFC3339 or Unix timestamps
• step (required) — resolution like 15s, 1m, 5m

Prometheus rejects steps that would produce > 11,000 points per series (HTTP 422). Increase step or narrow the range if this happens.

Note: The Prometheus range API does not support filtering by branch or commit — filters are expressed purely in PromQL label matchers.

prometheus_list_alerts

Returns all active/pending alerts with labels (including alertname, severity), state, activation time, and current value. Includes a state summary (firing vs pending counts).

prometheus_list_targets

Returns scrape targets with job name, instance address, health (up/down/unknown), last scrape duration in milliseconds, and any error message. Includes a per-job summary. Filter by state: active (default), dropped, or any.

Performance characteristics

• All tools use a single persistent requests.Session with connection pooling.
• The session has trust_env = False to bypass environment proxies (Prometheus is typically an internal service).
• Requests time out after 30 seconds.
• prometheus_query_range caps output at 5000 total points across all series — use a larger step for long windows.
• prometheus_list_metrics returns up to 500 metrics after filtering.

Development

git clone https://github.com/mshegolev/prometheus-mcp
cd prometheus-mcp
pip install -e '.[dev]'
pytest tests/ -v
ruff check src tests
ruff format src tests

API Specification

This project includes an OpenAPI 3.0 specification in the specs/ directory that documents all MCP tools exposed by the server.

To validate the specification:

python3 specs/validate_spec.py

Automation

This repository includes automated scripts and GitHub Actions workflows to streamline the release process:

Scripts

• scripts/auto-commit-push.sh - Automatically commit and push changes with optional release trigger
• scripts/release.sh - Full release automation including pipeline checking, version bumping, and tagging

GitHub Actions Workflows

• post-push-check.yml - Monitors test pipeline status after each push and comments on the commit
• auto-release.yml - Manual workflow to create releases with version bumping (patch, minor, or major)

To trigger an automated release:

1. Go to the Actions tab in GitHub
2. Select "Auto Release" workflow
3. Run the workflow with your preferred version bump type

License

MIT — see LICENSE.