Prometheus MCP Server: Query and Analyze Metrics

Overview

Prometheus MCP Server is a lightweight bridge that lets developers query and analyze Prometheus metrics through a simple HTTP API. It connects to an existing Prometheus server, exposes query endpoints, and formats the results for programmatic use — enabling real-time inspection, troubleshooting workflows, and integration with other tools or models that need metric context.

This server is useful when you want a dedicated, minimal layer between consumers (dashboards, automation scripts, or external systems) and Prometheus itself. Instead of calling Prometheus directly, teams can centralize access, apply consistent configuration, and integrate metric queries into automated analysis or monitoring pipelines.

Features

• Exposes Prometheus query endpoints over a clean HTTP API
• Supports instant and range PromQL queries (Prometheus HTTP API compatibility)
• Configurable Prometheus target URL and server bind address
• Docker-friendly: run as a container for easy deployment
• Lightweight and easy to integrate into CI/CD or observability workflows
• Returns JSON results ready for consumption by scripts, UIs, or other services

Installation / Configuration

Clone the repository and run locally, or use Docker.

Clone repository:

git clone https://github.com/pab1it0/prometheus-mcp-server.git
cd prometheus-mcp-server

Build and run with Go (if repository contains a Go server):

# Build
go build -o prometheus-mcp-server ./cmd/server

# Run with environment variables
PROMETHEUS_URL="http://localhost:9090" BIND_ADDR="0.0.0.0:8080" ./prometheus-mcp-server

Run with Docker:

# Build an image (optional)
docker build -t prometheus-mcp-server:latest .

# Run container
docker run -d \
  -e PROMETHEUS_URL="http://<prometheus-host>:9090" \
  -e BIND_ADDR="0.0.0.0:8080" \
  -p 8080:8080 \
  prometheus-mcp-server:latest

Example configuration (YAML):

prometheus:
  url: "http://prometheus.example.local:9090"

server:
  bind_addr: "0.0.0.0:8080"
  read_timeout: 10s
  write_timeout: 10s

Common environment variables:

• PROMETHEUS_URL — full URL to the Prometheus HTTP API (required)
• BIND_ADDR — host:port to bind the MCP server (default: 0.0.0.0:8080)
• LOG_LEVEL — logging verbosity (optional)

Available Resources

The server exposes a small set of HTTP endpoints that mirror common Prometheus operations. Replace with your MCP host:port.

Endpoint	Method	Description
/query	GET	Instant PromQL query. Params: query, time (optional)
/query_range	GET	Range PromQL query. Params: query, start, end, step
/targets	GET	(Optional) List Prometheus scrape targets (if proxied)
/health	GET	Health check for the MCP server

Example instant query:

curl -G "http://<server>/query" --data-urlencode 'query=up{job="api"}'

Example range query:

curl -G "http://<server>/query_range" \
  --data-urlencode 'query=rate(http_requests_total[5m])' \
  --data-urlencode 'start=2026-04-08T00:00:00Z' \
  --data-urlencode 'end=2026-04-08T01:00:00Z' \
  --data-urlencode 'step=60s'

Refer to the project repository for additional endpoints, health checks, and any custom routes added by the implementation.

Use Cases

• Troubleshooting incidents

  • Run an instant query to check service availability: curl -G “http:///query” –data-urlencode ‘query=up{instance=“api-1:8080”}’
  • Inspect recent error rates with a range query to correlate an alert window: curl -G “http:///query_range” –data-urlencode ‘query=rate(app_errors_total[5m])’ –data-urlencode ‘start=…’ –data-urlencode ‘end=…’ –data-urlencode ‘step=60s’

  • Feeding dashboards or analytics tools

    • Use the MCP server as a stable data source for internal dashboards, returning JSON-formatted timeseries data that frontends can directly consume.

  • Automation and runbooks

    • Integrate with incident-response scripts to fetch baseline metrics automatically (e.g., CPU, request latency) before executing remediation steps.

  • Model context for observability-driven automation

    • Provide metric snapshots to automation engines or analysis models that require recent telemetry context to make decisions or generate summaries.

  Notes and Next Steps

  • Security: If exposing this server in production, put it behind authentication, network policies, or an API gateway. The basic server proxies Prometheus requests and may reveal metrics that should be access-controlled.
  • Performance: For heavy query loads, consider caching, rate limiting, or direct Prometheus client usage tailored to your scale.
  • Contribution & Support: See the GitHub repository for issues, contribution guidelines, and source code: https://github.com/pab1it0/prometheus-mcp-server

  For exact configuration keys, runtime flags, and advanced usage, refer to the project README and source files in the repository.

  Tags:data-analytics