MCP 服务器

Connects AI agents to Datadog for monitoring, observability, and incident management. Enables querying metrics, viewing traces, searching logs, and managing monitors programmatically. Supports dashboard creation, alert configuration, and SLO tracking through the Model Context Protocol.

MCP server for Grafana's observability platform. Enables AI agents to query metrics from Prometheus, search and analyze logs from Loki, query traces, list and manage dashboards, and investigate incidents. Useful for debugging production issues, building monitoring dashboards, and performing root cause analysis with AI assistance across the full Grafana LGTM stack.

MCP server for Sentry error tracking integration. Enables AI agents to retrieve and analyze issues, view error stack traces, search events by query, and access project performance data. Helps developers debug production errors by providing contextual error information directly in AI-powered development workflows.

Apache SkyWalking's MCP server gives agents access to observability data for tracing, service topology, metrics, logs, and production incident investigation.

Dynatrace's official MCP server that brings the Dynatrace observability platform into AI workflows. Lets assistants query problems and vulnerabilities, run DQL against logs, metrics, and traces, inspect entities, and pull real-time monitoring data directly into a developer's coding environment for faster troubleshooting and root-cause analysis.

Honeycomb's MCP server that lets AI assistants query and analyze observability data, including events, traces, alerts (triggers), and boards. Agents can run queries against datasets, inspect columns and schemas, and cross-reference production behavior with the codebase to investigate incidents. Connects to Honeycomb via API key or OAuth.

MCP server that lets AI assistants query and analyze Prometheus metrics through standardized interfaces. Exposes instant and range PromQL queries, metric and label discovery, and target/health inspection, allowing agents to investigate system performance and troubleshoot incidents using natural language instead of hand-writing PromQL.