Prometheus MCP Server

The Prometheus MCP Server is a purpose‑built bridge that lets large language models (LLMs) query and interrogate a running Prometheus instance through the Model Context Protocol. Instead of writing custom scripts or manually navigating the Prometheus UI, an AI assistant can issue high‑level requests—such as “list all active alerts” or “provide a summary of the current metric landscape”—and receive structured responses directly from the Prometheus API. This capability is especially valuable for DevOps, observability engineers, and SREs who rely on AI to surface insights from complex telemetry data.

At its core, the server exposes a rich set of tools that map to Prometheus API endpoints. From retrieving build and runtime information (, ) to executing instant or range queries (, ), each tool is designed for a specific telemetry task. Advanced functions like exemplar queries, TSDB statistics, and alertmanager discovery give users deep access to the underlying data store. The server also supports documentation retrieval (, ) so an LLM can reference official Prometheus docs in real time, enhancing the quality of explanations it generates.

Developers integrate this MCP server into their AI workflows by simply pointing a Claude or Gemini instance at the server’s URL. The LLM can then invoke any of the available tools via its prompt, receiving JSON responses that can be parsed or further processed. Because the server runs in Go and follows MCP best practices, it is lightweight, highly performant, and can be deployed behind existing Prometheus setups without intrusive changes. The optional flag unlocks admin‑level TSDB operations for advanced use cases, though it requires explicit acknowledgment to prevent accidental data loss.

Real‑world scenarios include automated health checks of a Prometheus deployment, generating compliance reports from metric data, or building conversational dashboards where an AI can answer “Why did CPU usage spike last week?” by running a range query and summarizing the results. The server’s tight coupling with Prometheus’ native API ensures that responses are accurate and up‑to‑date, while the MCP interface guarantees seamless integration with any LLM that supports the protocol. This combination of observability depth and conversational ease makes the Prometheus MCP Server a standout tool for teams looking to harness AI in their monitoring pipelines.