Query Prometheus metrics for Kubernetes cluster monitoring and visualization. Retrieve snapshot or time-series data formatted for charting to analyze system performance and resource usage.
186,943 tools. Last updated 2026-06-10 05:00
"Prometheus" matching MCP tools:
- Query and visualize Prometheus metrics from Kubernetes clusters using PromQL. Supports instant and time-series data, grouping by labels, and customized units for Recharts integration.
- Access Prometheus query rules and guidelines to retrieve time-series data, metadata, alerts, and system status using PromQL queries.MIT
- Retrieve the Prometheus build version and configuration details to verify your monitoring setup.MIT
- List all Prometheus label names currently available. Use this to explore metric dimensions and construct targeted queries.MIT
- List all available Prometheus metrics to identify which data points can be queried and monitored.MIT
Matching MCP Servers
- AlicenseAqualityCmaintenanceProvides seamless integration between AI assistants and Prometheus, enabling natural language interactions with your monitoring infrastructure. This server allows for effortless querying, discovery, and analysis of metrics.Last updated1042925MIT
- AlicenseBqualityCmaintenanceA proof-of-concept Prometheus MCP server, which likely enables Claude AI to interact with Prometheus monitoring systems through the Model Context Protocol.Last updated21MIT
- Retrieve all possible values for a specific Prometheus label to analyze label dimensions or use in queries.MIT
- Retrieve current alerting and recording rules from Prometheus, including active alerts from each rule.MIT
- Retrieve metadata for a specified Prometheus metric, including its type and description.MIT
- Retrieve time series data from Prometheus over a specified time range and step resolution.MIT
- Retrieve runtime information from a Prometheus server to monitor its status and configuration.MIT
- List all monitored targets in Prometheus to gain complete visibility into your monitoring infrastructure.MIT
- Execute a Prometheus query to retrieve monitoring metrics. Optionally specify a time in RFC3339 format for historical data.MIT
- Create a unified post-mortem report by combining anomaly history, blast-radius, traces, and log highlights for one service over a specified time window. Ideal for after-incident analysis to get a quick overview.MIT
- List configured observability backends and check their reachability. Use this first to identify available source names and their health before running queries.MIT
- Retrieve historical anomaly scores for a service to analyze past incidents or detector trends. Returns a time-series of scores from the TSDB.MIT
- Sample OpenSIPS runtime statistics over a short window to detect proxy load pressure. Collects data at evenly-spaced points and returns raw time-series for delta computation. Limited to 10 minutes and 60 samples for manageable responses.Apache 2.0
- Generate a complete observability deployment bundle from an OpenSIPS scenario, including a Prometheus scrape configuration, Grafana dashboards, and a docker-compose stack, with auto-injected Prometheus module for immediate monitoring.Apache 2.0
- Generate a Docker Compose snippet for a Prometheus and Grafana monitoring stack that scrapes OpenSIPS metrics. Configure deployment name, ports, Grafana admin password, and external Docker network for side-car deployment.Apache 2.0
- Inject Prometheus monitoring into an OpenSIPS configuration by adding required module loads and parameters for HTTP server and statistics. Idempotent and warns if port conflicts exist.Apache 2.0