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Let AI query Prometheus metrics, analyze monitoring data, and inspect rules.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "prometheus-mcp-server" yet — see the docs or source repo.
Use Prometheus to query the 5xx error rate of payment-service over the last hour and explain whether there is any obvious abnormal trend.
Returns the relevant PromQL query results and a brief analysis of the error-rate trend and anomalies.
List the current Prometheus rules related to CPU usage and explain which rules are firing or close to firing.
Outputs the matching rule list, with rule states and brief explanations.
Check the current Prometheus scrape target status, identify unhealthy targets, and summarize which ones should be investigated first.
Returns an overview of target health, lists problematic targets, and suggests investigation priorities.
DevOps engineers or developers can ask AI to query metrics through the Prometheus API and perform basic analysis on the results. It is useful for quickly checking trends, anomalies, or service health.
When you need to understand current monitoring rules or scrape target status, this tool helps AI inspect rules and targets. It is suitable for investigating alert sources or scraping issues.
For common Prometheus tasks, users can rely on the tool’s embedded documentation and guided runbooks. This is helpful when analyzing metrics while also needing operational guidance.
This is an MCP server that lets LLMs interact with a running Prometheus instance through its API. It supports metric analysis, PromQL queries, and rule and target inspection.
Based on the description, you need at least a running Prometheus instance, and the tool accesses it through the Prometheus API. For installation or configuration details, see the source repository.
Its main difference is that it exposes Prometheus capabilities to an LLM through MCP instead of relying only on manual querying. It also provides embedded documentation and guided runbooks for common tasks.
Query and analyze Prometheus metrics through AI-friendly standardized interfaces.
Query Prometheus metrics with PromQL and analyze monitoring trends and anomalies.
Connect multiple Prometheus instances for AI-driven metrics analysis and SRE troubleshooting.
Track AI usage, costs, logs, and debug model interactions across apps.
Improve model outputs to expert-level quality through iterative creative direction.
Query real-time and historical server metrics from Prometheus for monitoring and troubleshooting.