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Query and analyze Jaeger traces to diagnose service performance and call issues.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "OTEL MCP Server" yet — see the docs or source repo.
Using the OTEL MCP Server, query slow traces for checkout-service in the last 2 hours, identify the trace with the highest P95 latency, and summarize the main time-consuming spans and likely bottlenecks.
A list of slow traces, key span timing analysis, and likely performance bottlenecks.
List downstream services related to payment-service, then pick one failed trace and show the full call chain, highlighting the service and span where the error occurred.
Service dependencies, failed trace details, and the identified error location.
Use the OTEL MCP Server to analyze changes in error trace volume for user-service over the last 24 hours, identify peak error periods, and summarize common failure patterns.
A brief analysis of error trends, peak periods, and common failure patterns.
Query Jaeger traces, services, and operations using natural language.
Query Jaeger traces with sanitized summaries for safe debugging.
Let AI query and analyze OpenTelemetry traces to debug apps faster.
Query and analyze telemetry data in natural language to find performance issues.
Connect to Grafana dashboards, data sources, and alerts for monitoring analysis.
Securely query metrics, alerts, and logs for read-only cluster diagnostics.