Prometheus MCP Server

The Prometheus MCP Server is a lightweight, AI‑friendly gateway that exposes Prometheus metrics through the Model Context Protocol. It turns raw PromQL queries and metric metadata into JSON payloads that an AI assistant can consume, parse, and reason about without needing to understand Prometheus internals. By providing a standard MCP interface, the server enables developers to embed real‑time observability data directly into conversational AI workflows—whether for troubleshooting, monitoring dashboards, or automated incident response.

Why It Matters

Monitoring is a core part of modern DevOps and SRE practices, yet most AI assistants struggle to interact with metrics systems because they lack native support for Prometheus’ query language and data structures. This server solves that gap by:

• Normalizing Prometheus responses into JSON, eliminating the need for custom parsers in client code.
• Providing a set of high‑level tools (e.g., , ) that expose metric metadata in a way AI can easily ingest.
• Enabling chart generation () so that visual insights are available directly within chat or UI contexts.

These capabilities allow developers to ask an AI assistant questions like “Show me the CPU usage trend for the last 30 minutes” and receive a ready‑to‑display chart or structured data without manual API plumbing.

Core Features

• Metric Discovery – lets the AI find all metrics matching a regex pattern, simplifying exploration of large metric spaces.
• Metadata Retrieval – and expose label names and their possible values, enabling dynamic query construction.
• PromQL Execution – (instant) and (historical) tools execute arbitrary PromQL expressions, returning results in JSON for easy manipulation.
• Chart Generation – extends range queries by producing PNG images encoded in base64, which can be embedded directly into chat bubbles or dashboards.
• SSE Endpoint – The server exposes a Server‑Sent Events endpoint () for real‑time streaming of metric updates, allowing AI assistants to keep information fresh without polling.

Real‑World Use Cases

• Incident Diagnosis – An AI assistant can retrieve recent metric trends and generate charts to help engineers pinpoint the root cause of a service outage.
• Performance Analysis – Developers can query historical CPU, memory, or request latency metrics and receive structured data for trend analysis within a conversational interface.
• Auto‑Scaling Decisions – By exposing live metrics, the server lets AI systems recommend scaling actions based on real‑time telemetry.
• Observability Dashboards – Embedding the server’s chart outputs into existing chat or collaboration tools creates lightweight, AI‑driven dashboards without a full Grafana stack.

Integration with AI Workflows

The server is designed to fit seamlessly into existing MCP‑compatible pipelines. A client simply registers the endpoint in its configuration, and any MCP‑aware assistant can invoke the provided tools as part of a conversation. Because all responses are JSON, the assistant can parse them with minimal effort, extract values, and even render charts directly in the UI. This plug‑and‑play model removes friction for developers who want to augment their AI assistants with live observability data.

Unique Advantages

• Simplicity – A minimal Go implementation with a single port () makes deployment quick and reliable.
• AI‑First Design – JSON outputs and pre‑defined tools reduce the cognitive load on AI models, enabling more accurate interpretations.
• Extensibility – The modular structure (cmd, internal, pkg) allows teams to extend or replace components (e.g., swap Prometheus with another data source) without breaking the MCP contract.

In summary, the Prometheus MCP Server bridges the gap between sophisticated metrics systems and conversational AI, empowering developers to build smarter, data‑driven assistants that can observe, analyze, and act on real‑world telemetry.