Query Generate

Prerequisites

This skill calls SigNoz MCP server tools heavily (signoz:signoz_execute_builder_query, signoz:signoz_query_metrics, signoz:signoz_search_logs, signoz:signoz_search_traces, signoz:signoz_aggregate_logs, signoz:signoz_aggregate_traces, signoz:signoz_get_field_keys, signoz:signoz_get_field_values, signoz:signoz_list_metrics, signoz:signoz_list_services, signoz:signoz_get_service_top_operations, signoz:signoz_get_trace_details). Before running the workflow, confirm the signoz:signoz_* tools are available. If they are not, run signoz-mcp-setup first to initialize or repair the MCP connection. Do not fall back to raw HTTP calls or fabricate query results without the MCP tools.

When to use

Use this skill when the user asks to:

• Query, search, or look up observability data (traces, logs, metrics)
• Compute aggregations (error rate, p99 latency, request count, throughput)
• Find specific log entries, traces, or metric values
• Investigate patterns (spikes, drops, trends over time)

Do NOT use when:

• User wants raw ClickHouse SQL for a dashboard panel (custom joins, window functions, regex over log bodies) — that’s a separate dashboard-panel SQL workflow, not this skill.

Instructions

Step 1: Determine the signal type

Map the user’s intent to the right signal:

User intent	Signal	Why
Error rate, latency, throughput, request count	metrics (preferred) or traces	Metrics are pre-aggregated and fastest. Use traces if the user needs per-request detail or no matching metric exists.
p50/p75/p90/p95/p99 latency	metrics (histogram) or traces (aggregate on durationNano)	Prefer metrics if a histogram metric exists (e.g., signoz_latency_bucket). Fall back to trace aggregation.
Find specific log entries, error messages, stack traces	logs	Text search, pattern matching, severity filtering.
Find specific traces, slow requests, error spans	traces	Per-request detail, span attributes, duration filtering.
Infrastructure metrics (CPU, memory, disk, network)	metrics	Always metrics for resource utilization.
“How many X per Y” (count/rate grouped by dimension)	traces or logs (aggregate)	Use signoz:signoz_aggregate_traces or signoz:signoz_aggregate_logs for grouped counts.

If the signal is genuinely ambiguous, ask the user before proceeding. The host application decides how the question is surfaced (e.g. a structured clarification tool or an inline <assistant_question> tag) — follow the host’s UI rendering rules.

Step 2: Discover available data

Always discover before querying. Use only names returned by tools — never guess from training knowledge.

Run discovery calls in parallel where possible:

• For metrics: Call signoz:signoz_list_metrics with a searchText substring matching the user’s intent (e.g., searchText: "http", searchText: "latency"). The response includes metric type, temporality, and isMonotonic — pass these to signoz:signoz_query_metrics to avoid extra lookups.
• For traces: Call signoz:signoz_list_services to confirm the service name exists. Optionally call signoz:signoz_get_service_top_operations for the service to find operation names. Call signoz:signoz_get_field_keys(signal: "traces") if you need to filter on a non-standard attribute.
• For logs: Call signoz:signoz_get_field_keys(signal: "logs") if filtering on attributes beyond body, severity_text, and service.name. Call signoz:signoz_get_field_values to validate specific filter values.

If the user already provides exact field names, service names, or metric names from context (e.g., from a dashboard or @mention), skip redundant discovery.

Step 3: Choose the right tool

Use the simplest tool that answers the question:

Question type	Tool	When to use
Metric time series or scalar	signoz:signoz_query_metrics	Any metrics query. Handles aggregation defaults automatically. Supports formulas via formula + formulaQueries params.
Log search (find matching entries)	signoz:signoz_search_logs	Finding specific log lines. Use searchText for body text, query for field filters, severity for level filtering.
Trace search (find matching spans)	signoz:signoz_search_traces	Finding specific traces/spans. Use service, operation, error, minDuration/maxDuration shortcuts plus query for field filters.
Log aggregation (count, avg, percentiles)	signoz:signoz_aggregate_logs	“How many errors?”, “error count by service”, “p99 response time from logs”. Set requestType to scalar for totals or time_series for trends.
Trace aggregation (count, avg, percentiles)	signoz:signoz_aggregate_traces	“p99 latency for checkout”, “error count per operation”, “request rate by endpoint”. Set requestType to scalar for totals or time_series for trends.
Complex multi-query or formula	signoz:signoz_execute_builder_query	Only when the simpler tools above cannot express the query — e.g., joining multiple data sources, complex filter expressions, or queries needing the full Query Builder v5 schema. Read signoz://traces/query-builder-guide before using.

requestType decision for aggregations:

• scalar (default): “How many?”, “What is the p99?”, “Which service has the most?”
• time_series: “When did errors spike?”, “How did latency change?”, “Show trend”
• If the question has ANY temporal component (spike, trend, change), use time_series

Step 4: Execute the query

• Always include searchContext with the user’s original question — it improves result relevance.
• Default time range is last 1 hour. Respect the user’s time range if specified. Convert relative times (“last 6 hours”, “yesterday”) to timeRange param format (e.g., 6h, 24h) or Unix millisecond start/end.
• Use shortcut parameters (service, severity, operation, error) when they match the user’s filters — they are simpler and less error-prone than building query expressions.
• Combine shortcut params with query/filter for additional constraints — they are ANDed together.
• For signoz:signoz_query_metrics, pass metricType, temporality, and isMonotonic from the signoz:signoz_list_metrics response to avoid an extra auto-fetch round trip.

Step 5: Handle results

Data returned:

• Present findings as neutral observations with timestamps and values.
• Include the time range in your response.
• For aggregations with groupBy, highlight the top entries and mention total group count if truncated by limit.
• For search results, summarize patterns rather than listing every entry.

No data returned — apply three-way distinction:

1. Healthy zero: The query ran successfully but the count is zero. Say so: “No errors found for checkout-service in the last hour — error count is zero.”
2. No data in range: The field/metric exists but no data points fall in the time window. Suggest expanding: “No data in the last hour. Try a wider range?”
3. Missing instrumentation: The metric, field, or service doesn’t exist in discovery results. Say what’s missing and suggest how to instrument.

Drill-down:

• If an aggregation reveals an interesting pattern (spike, outlier service), offer to drill into individual traces or logs for that scope.
• If a trace search returns interesting spans, offer to fetch full trace details via signoz:signoz_get_trace_details.

Guardrails

• Discovery first: Never guess metric names, field names, or service names. Use discovery tools or context to confirm they exist before querying.
• Never claim root cause: Present data patterns and correlations. Write “Error rate for checkout increased from 0.2% to 4.1% at 14:05” not “The deployment caused the errors.”
• One focused query per question: Do not scatter-shot multiple queries when one precise query answers the question. Use parallel discovery calls, but be precise for execution.
• Respect MCP server rules: The MCP server enforces rules about resource attribute filters, filter operators, and redundant queries. Follow them — especially preferring resource attributes in filters for faster queries.
• No raw ClickHouse SQL: Always use the Query Builder tools. Never construct raw SQL.
• Scope boundary: This skill queries data. If the user wants to wrap the query into a recurring alert, redirect to signoz-creating-alerts.
• Emit apply_filter on the final message. When the user asks you to write, build, generate, or show a query, include an apply_filter action on your final assistant message with the resolved compositeQuery from the tool result and the appropriate signal field (metrics, logs, or traces). This signals to the SigNoz UI that the user wants to apply the query to an explorer page. Only emit apply_filter when the user’s primary intent is to obtain a runnable query — not when the user is asking a one-shot data question that the analysis text already answers.

Examples

User: “Show me the error rate for the checkout service in the last hour”

Agent:

1. Calls signoz:signoz_list_metrics(searchText: "error") — finds signoz_calls_total metric.
2. Calls signoz:signoz_query_metrics with metricName: "signoz_calls_total", filter: "service.name = 'checkout'", formula: "A / B * 100" where A is error count and B is total count, timeRange: "1h".
3. Presents: “Error rate for checkout-service: 2.3% over the last hour (14:00– 15:00 UTC). 47 errors out of 2,041 total requests.”
4. Offers drill-down: “Want me to check which operations have the highest error rate?”

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User: “Find timeout errors in logs”

Agent:

1. Calls signoz:signoz_search_logs(searchText: "timeout", severity: "ERROR", timeRange: "1h").
2. Presents: “Found 23 timeout errors in the last hour. Most are from payment-service (18) and gateway (5). Common pattern: ‘context deadline exceeded’ in gRPC calls to downstream-api.”
3. Offers: “Want me to check traces for these services to see where the timeouts occur?”

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User: “What’s the p99 latency for the cart service?”

Agent:

1. Calls signoz:signoz_aggregate_traces(aggregation: "p99", aggregateOn: "durationNano", service: "cart-service", requestType: "scalar", timeRange: "1h").
2. Presents: “p99 latency for cart-service: 1.2s over the last hour.”
3. Offers: “Want me to break this down by operation or show the trend over time?”

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User: “When did errors spike for the frontend?”

Agent:

1. Calls signoz:signoz_aggregate_traces(aggregation: "count", error: "true", service: "frontend", requestType: "time_series", timeRange: "6h").
2. Presents: “Error count for frontend over the last 6 hours. Spike at 11:30 UTC — error count jumped from ~5/min to ~45/min, returning to baseline by 12:15.”
3. Offers: “Want me to check what error types appeared during the spike?”