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Ecosystem & Marketplaces›Open-Source MCP Servers (10)›Prometheus MCP Server

Prometheus MCP server

The Prometheus MCP Server is a Model Context Protocol server for cloud-native metrics observability, exporter lifecycle management, and TSDB FinOps. It exposes 28 tools, 14 prom:// resources, and 5 guided operational prompts across Prometheus, Thanos, Grafana Mimir, Cortex, and VictoriaMetrics backends.

28 PromQL & FinOps Tools

Execute PromQL with counter-rate enforcement, deploy 19 pre-packaged Kubernetes exporters, test alert rules, and audit TSDB cardinality.

14 Live Resources

Discover active backends (prom://system/backends), scrape targets, exporter catalogs, and audit logs.

Guided Operational Workflows

Follow step-by-step runbooks for application metric onboarding, alert rule simulation, and TSDB cardinality reduction.

Multi-Backend Configuration

Configure multi-backend routing, rate-limiting middleware, response size caps, and Kubernetes ServiceMonitor RBAC.

Core capabilities​

Capability DomainWhat the Server Provides
Safe PromQL ExecutionValidates PromQL syntax prior to execution, enforces rate()/increase() wrapping on counters, and downsamples range queries automatically to 100–200 data points per series.
Application OnboardingRecommends instrumentation strategies (client SDK, sidecar exporter, or built-in /metrics), generates Go/Java/Python/Node.js snippets, validates OpenMetrics output, and provisions ServiceMonitor CRDs.
Exporter Lifecycle (19 Catalog Entries)One-command Kubernetes deployment (Deployment/DaemonSet, Service, RBAC) for 19 common infrastructure exporters with live up{} series verification.
Alert Rule Testing & SimulationDrafts Prometheus alerting rules, validates syntax via promtool, runs synthetic unit tests, and backtests rules against historical TSDB data.
TSDB FinOps & Cardinality ControlIdentifies top-N high-cardinality metric series, generates metric_relabel_configs to drop noisy labels, creates pre-computed recording rules, and configures remote-write pipelines.

Architecture​

Quickstart​

1
Verify prerequisites

Ensure you have network reachability to a Prometheus-compatible HTTP API endpoint (Prometheus, Thanos, Mimir, Cortex, or VictoriaMetrics) and optionally a KUBECONFIG for exporter and ServiceMonitor operations.

2
Start the Prometheus MCP Server container

Run the Docker container pointing to your Prometheus endpoint:

docker run --rm -it \
  -p 8767:8767 \
  -e PROMETHEUS_BASE_URL=http://host.docker.internal:9090 \
  -e MCP_TRANSPORT=http \
  talkopsai/prometheus-mcp-server:latest
3
Register the server in your MCP client

Add the server URL to your MCP client configuration:

mcp.json
{
  "mcpServers": {
    "prometheus": {
      "url": "http://localhost:8767/mcp",
      "description": "MCP Server for Prometheus observability and monitoring management"
    }
  }
}

Security and middleware guardrails​

  • 7-layer middleware protection: Enforces response payload caps (100 KB max), token-bucket rate limiting (10 req/s, burst 20), response caching, and structured audit logging.
  • Explicit backend_id targeting: In multi-backend deployments, every tool call requires an explicit backend_id parameter so queries and rule updates never target an unintended environment.
  • Scoped backend access policies: Configure per-backend read-only or allow/deny policies via governance tools.

Project structure​

prometheus-mcp-server/
├── prometheus_mcp_server/
│   ├── tools/           # 28 MCP tools across 10 functional categories
│   ├── resources/       # 14 MCP resources (prom:// URIs)
│   ├── prompts/         # 5 guided workflow prompts
│   ├── services/        # Prometheus HTTP v1 + Kubernetes API services
│   ├── server/          # FastMCP setup + 7-layer middleware stack
│   ├── utils/           # 19-exporter catalog, snippet generator, PromQL parser
│   ├── models/          # Pydantic request/response schemas
│   └── config.py        # Multi-backend and environment configuration
├── tests/               # Unit and integration test suites
├── Dockerfile
└── pyproject.toml
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