Prometheus MCP

Prometheus MCP is a lightweight, proof‑of‑concept server that extends the Model Context Protocol (MCP) ecosystem with a real‑time metrics gateway. By exposing Prometheus query capabilities over MCP, it lets AI assistants such as Claude retrieve, filter, and visualize time‑series data directly from a Prometheus instance. This eliminates the need for separate monitoring dashboards or manual export steps, enabling developers to embed live operational insights into conversational workflows.

The server implements the standard MCP resource and tool interfaces. It offers a metrics resource that accepts PromQL queries, returning structured JSON with timestamped values. A companion chart tool can render these results into SVG or PNG images using Matplotlib, which the assistant can then embed in chat. This dual capability means an AI can both analyze raw data and present it visually, all within a single interaction. The design is intentionally modular: developers can add additional tools (e.g., anomaly detection or alert generation) without altering the core server logic.

Key features include:

• Direct PromQL integration: send arbitrary queries and receive results in the same format Claude expects for data tables.
• On‑the‑fly chart generation: convert numeric series into instantly viewable graphs, improving interpretability for non‑technical stakeholders.
• Secure credential handling: the server reads a file for Prometheus credentials, keeping secrets out of code.
• MCP‑compatible tooling: the server exposes its capabilities through standard MCP endpoints, making it plug‑and‑play with any MCP‑compliant client.

Typical use cases involve DevOps teams who want to surface latency, error rates, or resource usage directly into a conversational interface. For example, an engineer could ask Claude, “Show me the 5‑minute average response time for service X over the last hour,” and receive a concise table or chart without leaving the chat. In incident response, operators can quickly pull up trend data and immediately discuss mitigation steps with an AI assistant.

Because Prometheus MCP is built on the same protocol that powers Claude’s tool integrations, it fits naturally into existing AI workflows. Once added to the configuration, any Claude session can invoke its resources with a simple JSON payload. The server’s lightweight Python implementation (leveraging for dependency management) ensures minimal overhead, making it suitable for both local experimentation and production deployment behind a reverse proxy.