{"slug": "incident-specific-dashboard-spin-up", "title": "Incident Specific Dashboard Spin-Up", "summary": "SigNoz users can now create incident-specific dashboards via natural language prompts using an AI assistant connected through the MCP server. In a demonstration, a user asked for a checkout service incident dashboard, and the AI automatically identified the payment service as the root cause of a 75% error rate, building a comprehensive dashboard with golden signals, error traces, dependency health, and infrastructure metrics.", "body_md": "The checkout service is throwing errors. Your error rate alert fired and you need visibility into what's happening right now. Instead of manually parsing through all your telemetry data, you want a focused dashboard that shows exactly what's broken.\n\nYou open your AI assistant, connected to SigNoz via the MCP server, and ask it to build an incident-specific dashboard.\n\nPrerequisites\n\n- Connect your AI assistant to SigNoz using the\n[MCP Server guide](https://signoz.io/docs/ai/signoz-mcp-server/).\n\nStep 1: Create the Incident Dashboard from a Natural Language Prompt\n\n```\nOur checkout service error alert is firing. Create an incident dashboard for the last 1 hour with:\n\nCheckout error rate, latency (p95/p99), and throughput vs last week's baseline\nTop error messages and failing traces\nHealth of key dependencies services\nInfrastructure: CPU, memory, pod restarts\n```\n\nYour assistant will analyze the checkout service, identify its dependencies, gather the relevant metrics, and build a comprehensive incident dashboard:\n\n```\n✓ Service confirmation: Checkout service detected\n  - Error rate: 75% (27 errors / 36 calls in last hour)\n  - Dependencies identified: payment, cart, product-catalog, currency, shipping, email\n\n✓ Root cause identified: Payment service\n  - Error rate: 52% (26 errors / 50 calls)\n  - Top failing operations:\n    - oteldemo.CheckoutService/PlaceOrder: 27 failures\n    - oteldemo.PaymentService/Charge: 26 failures\n  - Pattern: Nearly identical error counts suggest every checkout that reaches payment fails\n\n✓ Dashboard created: \"Checkout Service — Incident Response (Last 1h)\"\n  - Dashboard ID: 019dad99-26b3-75aa-a76d-9060062cbadb\n\nDashboard organized into four sections:\n\nSection 1: Golden Signals vs 7-day Baseline\n- Error Rate % (current vs same window 7 days ago)\n- Throughput (req/s) with baseline overlay\n- P95 Latency with baseline comparison\n- P99 Latency with baseline comparison\n\nSection 2: Top Errors & Failing Traces\n- Table of failing operations grouped by span name\n- List of recent error traces with `trace_id` and status messages for one-click drill-down\n\nSection 3: Dependency Health\n- Error rate % for each dependency: payment, cart, product-catalog, currency, shipping, email\n- P95 latency broken out by each downstream service\n\nSection 4: Infrastructure\n- CPU utilization for checkout container (grouped by container.id)\n- Memory usage for checkout container (grouped by container.id)\n- Note: Pod restart metrics not available (requires k8s.pod.* metrics from k8s-infra collector)\n\nOpen the dashboard in SigNoz under Dashboards → \"Checkout Service — Incident Response (Last 1h)\"\n```\n\nThe dashboard is now live and provides a complete incident view.\n\nFinal Summary\n\nYou now have a fully functional incident dashboard created from just using a simple prompt.\n\nThe dashboard clearly shows that payment-service is the likely root cause with elevated errors and high latency.\n\nUnder the Hood\n\n[Under the Hood](#under-the-hood)\n\nDuring this workflow, the MCP server called these tools:\n\n| Step | MCP Tool | What It Did |\n|---|---|---|\n| 1 | `signoz_list_services` | Verified the checkout service exists and retrieved initial error rate statistics |\n| 1 | `signoz_get_service_top_operations` | Identified checkout service dependencies (payment, cart, product-catalog, currency, shipping, email) and top failing operations |\n| 1 | `signoz_aggregate_traces` | Retrieved error rates, latency percentiles (p95/p99), throughput metrics, and compared against 7-day baseline |\n| 1 | `signoz_create_dashboard` | Created the incident dashboard with four sections covering golden signals, errors, dependency health, and infrastructure |\n\nRelated Use Cases\n\n[Dashboard Creation from Natural Language](https://signoz.io/docs/ai/use-cases/dashboard-creation-natural-language/)- Create custom dashboards by describing what you want to visualize in plain English.[Alert Correlation Analysis](https://signoz.io/docs/ai/use-cases/alert-correlation-analysis/)- When multiple services alert simultaneously, identify whether it's a cascade from one failure or separate incidents.[On-Call Handoff Brief](https://signoz.io/docs/ai/use-cases/oncall-handoff-brief/)- Generate a handoff summary of recent incidents and ongoing issues for the next on-call engineer.\n\nIf you need help with the steps in this topic, please reach out to us on [SigNoz Community Slack](https://signoz.io/slack/). If you are a SigNoz Cloud user, please use in product chat support located at the bottom right corner of your SigNoz instance or contact us at [cloud-support@signoz.io](mailto:cloud-support@signoz.io).", "url": "https://wpnews.pro/news/incident-specific-dashboard-spin-up", "canonical_source": "https://signoz.io/docs/ai/use-cases/incident-specific-dashboard", "published_at": "2026-06-27 00:00:00+00:00", "updated_at": "2026-06-28 10:35:56.629040+00:00", "lang": "en", "topics": ["ai-tools", "developer-tools", "ai-agents"], "entities": ["SigNoz", "MCP", "Checkout Service", "Payment Service"], "alternates": {"html": "https://wpnews.pro/news/incident-specific-dashboard-spin-up", "markdown": "https://wpnews.pro/news/incident-specific-dashboard-spin-up.md", "text": "https://wpnews.pro/news/incident-specific-dashboard-spin-up.txt", "jsonld": "https://wpnews.pro/news/incident-specific-dashboard-spin-up.jsonld"}}