grafana-lens

name	grafana-lens
description	Grafana tools for data visualization, monitoring, alerting, security, and SRE investigation. Use grafana_query, grafana_query_logs, grafana_query_traces, grafana_create_dashboard, grafana_update_dashboard, grafana_create_alert, grafana_share_dashboard, grafana_annotate, grafana_explore_datasources, grafana_list_metrics, grafana_search, grafana_get_dashboard, grafana_check_alerts, grafana_push_metrics, grafana_explain_metric, grafana_security_check, and grafana_investigate. Trigger when asked about metrics, dashboards, monitoring, alerts, costs, token usage, data visualization, PromQL, Prometheus, LogQL, Loki, log queries, error logs, log search, TraceQL, Tempo, traces, distributed tracing, span search, find slow traces, debug session traces, annotations, deployments, sharing charts, investigating alert notifications, pushing custom data (calendar, git, fitness, finance) to Grafana for visualization, pushing historical data, backfilling metrics, recording past data with timestamps, modifying dashboards, adding panels, removing panels, changing dashboard settings, updating dashboard time range, explain metric, metric trend, what is this metric, how has this changed, is this metric normal, why did my bill spike, cost visibility, security monitoring, security check, security audit, am I being attacked, is my agent compromised, suspicious activity, threat detection, prompt injection detection, set up security alerts, investigate, debug, triage, root cause, what's wrong, why is X broken, anomaly detection, RED method, USE method, alert fatigue, postmortem, incident summary.
metadata	{"openclaw":{"emoji":"🔭","requires":{"config":"[Truncated]"}}}

Grafana Lens

You have full native Grafana access — query data, create dashboards, set alerts, receive alert notifications, annotate events, explore datasources, push custom data, and deliver visualizations inline. Works with ANY data in Grafana, not just agent metrics.

Musts

• Always call grafana_explore_datasources first when you need a datasource UID — never guess UIDs
• Always call grafana_search before creating a dashboard — avoid duplicates
• Always call grafana_get_dashboard before grafana_share_dashboard — you need exact panel IDs
• Always call grafana_get_dashboard before grafana_update_dashboard — you need panel IDs and current structure
• Prefer grafana_query for direct answers over creating dashboards — "what's my cost?" needs a number, not a URL
• Prefer grafana_query over grafana_create_dashboard + grafana_share_dashboard for simple data questions — a number is faster than a chart
• Use grafana_query_logs for log searches — LogQL for logs, PromQL for metrics, TraceQL for traces. Never use grafana_query for Loki datasources
• Use grafana_query_traces for trace searches — TraceQL for traces, PromQL for metrics, LogQL for logs. Never use grafana_query or grafana_query_logs for Tempo datasources
• All tools work with ANY Prometheus datasource — not just openclaw_lens_* metrics
• When you see "GRAFANA ALERTS" in prompt context, investigate immediately with grafana_check_alerts — use the suggestedInvestigation field to go directly to querying (it provides the tool, query, and datasource)
• Run grafana_check_alerts with action setup once before alert notifications can reach the agent — this creates the webhook contact point
• Push data before querying or dashboarding it — data is pushed via OTLP and available immediately
• Prefer grafana_explain_metric for "what is this metric?" questions over manual grafana_query — it returns current value, trend, stats, and metadata in one call
• Use queryNames from push response for PromQL queries — don't guess metric names (counters get _total suffix)

Quick Decision Tree

• "What is [metric]?" / "Why did it spike?" → grafana_explain_metric
• "What's the current value of X?" / complex PromQL → grafana_query
• "Find error logs" / "Search logs for..." → grafana_query_logs
• "Find slow traces" / "Show trace for session X" / "Debug distributed spans" → grafana_query_traces
• "Debug this session" / "Why did it fail?" / "What went wrong?" → grafana_query_traces (search error/slow) → grafana_query_traces (get → follow correlationHint) → grafana_query_logs → grafana_query → grafana_annotate
• "Show me a chart" / "Visualize..." → grafana_search → grafana_get_dashboard → grafana_share_dashboard
• "Create a dashboard for..." → grafana_search (check duplicates) → grafana_create_dashboard
• "Add a panel to my dashboard" → grafana_get_dashboard → grafana_update_dashboard
• "Delete this dashboard" → grafana_update_dashboard with operation delete (confirm with user first)
• "Alert me when..." → grafana_check_alerts (setup) → grafana_create_alert
• "List my alert rules" / "What alerts do I have?" → grafana_check_alerts with action list_rules
• "Delete alert rule X" → grafana_check_alerts with action list_rules → delete_rule with ruleUid
• "Track my [custom data]" / "Record my [past data]" → grafana_push_metrics (with optional timestamp for historical data, auto-registers, returns queryNames) → grafana_query with queryNames
• "What data sources do I have?" → grafana_explore_datasources
• "What metrics are available?" → grafana_list_metrics
• "Set up monitoring" / "Monitor my agent" / "What dashboards should I have?" → grafana_search (check existing) → grafana_create_dashboard with llm-command-center → follow chain through remaining templates

Working with Multiple Grafana Instances

When several Grafana environments are configured (dev, staging, prod), every tool accepts an optional instance parameter. grafana_explore_datasources returns availableInstances — use the name values from that list.

Why this matters: Users often need to query production metrics, create dashboards in dev, or compare environments side by side. Each tool call targets one instance.

Smart defaults: Omitting instance always targets the configured default — safe and invisible for single-environment setups. Only specify instance when the user explicitly names a non-default environment.

Cross-environment workflows: Each call is independent. Query prod, create dashboard in dev — just set instance differently on each call. No context switching needed.

Tool Inventory

Tool	What It Does
grafana_explore_datasources	Discover configured datasources (UIDs, types, query routing) — tells you which tool + query language to use for each datasource
grafana_list_metrics	Discover available metrics or label values from a datasource. Use compact: true with metadata: true for minimal fields in multi-tool chains
grafana_query	Run PromQL instant/range queries — get numbers directly
grafana_query_logs	Run LogQL queries against Loki — search and filter logs
grafana_query_traces	Run TraceQL queries against Tempo — search traces or get full trace by ID
grafana_create_dashboard	Create dashboards from templates or custom JSON
grafana_update_dashboard	Add/remove/update panels, change dashboard metadata, or delete dashboard
grafana_get_dashboard	Get dashboard summary (panels, queries). Use compact: true for overview scans, audit: true to health-check all panels in one call
grafana_search	Search existing dashboards by title, tags, or starred status
grafana_share_dashboard	Render panel as image and deliver inline via messaging
grafana_create_alert	Create Grafana-native alert rules on any metric
grafana_annotate	Create or list annotations (events) on dashboards
grafana_check_alerts	Check, acknowledge, list/delete rules, silence/unsilence, or set up Grafana alert webhook notifications. Use compact: true with list_rules for minimal fields
grafana_push_metrics	Push custom data (calendar, git, fitness, finance) via OTLP
grafana_explain_metric	Get metric context: current value, trend, stats, metadata, drill-down queries — agent interprets

Tool Details

grafana_explore_datasources

When: First step when user mentions data, metrics, or monitoring. Gets datasource UIDs needed by grafana_query, grafana_query_logs, grafana_query_traces, grafana_list_metrics, grafana_create_alert, and grafana_explain_metric. Params: instance (optional — target Grafana instance, omit for default). Example: {} Example (multi-instance): { "instance": "prod" } Returns: List of datasources with uid, name, type, isDefault, plus routing hints: queryTool (which agent tool to use, e.g. "grafana_query", "grafana_query_logs", or "grafana_query_traces"), queryLanguage (e.g. "PromQL", "LogQL", "TraceQL"), and supported (boolean — whether an agent tool can query this datasource). Use queryTool to pick the right tool for each datasource. When multiple Grafana instances are configured, also returns instance (which instance was queried) and availableInstances (list of { name, url, isDefault } for all configured instances).

grafana_list_metrics

When: User asks "what metrics are available?" or you need to discover metrics before querying or composing dashboards. Also when grouping metrics by function — metadata mode adds category to each openclaw_* metric. Use purpose when user asks about a specific concern (e.g., "performance metrics", "cost metrics"). Params: datasourceUid (required), prefix (filter by prefix), search (targeted discovery — server-side regex, only matching metrics returned), purpose ("performance" | "cost" | "reliability" | "capacity" — pre-filter by intent, composable with prefix and search), label (list label values instead), metadata (boolean — enriched results with type/help/category), compact (boolean — with metadata, returns only name/type/category, ~60% smaller). Example names: { "datasourceUid": "prom1", "prefix": "openclaw_lens_" } Example search: { "datasourceUid": "prom1", "search": "steps" } Example purpose: { "datasourceUid": "prom1", "purpose": "performance", "metadata": true } Example combined: { "datasourceUid": "prom1", "prefix": "openclaw_ext_", "search": "fitness" } Example metadata: { "datasourceUid": "prom1", "metadata": true, "prefix": "openclaw_" } Example compact: { "datasourceUid": "prom1", "metadata": true, "compact": true } Returns names: { metrics: ["metric1", "metric2", ...] }. Truncated at 200. Returns metadata: { metadataSource, categorySummary: { cost: 3, usage: 4, session: 5, ... }, metrics: [{ name, type, help, category?, source? }, ...] }. Use this before composing custom dashboards — type tells you counter vs gauge vs histogram, category groups openclaw_* metrics by function. Search also matches help text. Categories: cost, usage, session, queue, messaging, webhook, tools, agent, custom. categorySummary gives counts per category for quick overview (omitted when no metrics). Purpose maps: → session + tools, → cost + usage, → webhook + messaging + agent, → queue + session. : when Prometheus metadata endpoint has data, when OTLP-only (metadata synthesized from known metric registry — histogram sub-metrics deduplicated, type/help from Grafana Lens definitions). On OTLP stacks, includes explaining why metadata is synthetic. on individual entries from the registry; on entries from the custom metrics store. : . Same as metadata but drops , , — use in multi-tool chains where you need metric names and types but not full descriptions. : : . Truncated at 200.

grafana_query

When: User asks a data question that needs a direct answer, not a dashboard. Also for re-running an existing dashboard panel's query with different time ranges. Params: datasourceUid, expr (PromQL), queryType (instant/range), start (range only, required), end (range only, default "now"), step (range only, optional — auto-calculated from time range if omitted, targeting ~300 datapoints), dashboardUid (optional — resolve query from panel), panelId (optional — use with dashboardUid). Example instant: { "datasourceUid": "prom1", "expr": "sum(increase(openclaw_lens_cost_by_model_total[1d])) or vector(0)" } Example range (auto-step): { "datasourceUid": "prom1", "expr": "rate(openclaw_tokens_total[5m])", "queryType": "range", "start": "now-30d" } Example range (explicit step): { "datasourceUid": "prom1", "expr": "rate(openclaw_tokens_total[5m])", "queryType": "range", "start": "now-1h", "end": "now", "step": "60" } Example panel re-run: { "dashboardUid": "openclaw-command-center", "panelId": 10, "queryType": "range", "start": "now-7d" } Tip: start/end accept Unix seconds or relative expressions like "now-1h", "now-7d". For range queries, just set start — end defaults to "now" and step is auto-calculated. Override step only when you need specific resolution. Tip (panel re-run): Set dashboardUid + panelId to re-run a panel's query without manually extracting PromQL. The tool auto-resolves expr and datasourceUid from the panel definition. Template variables are replaced with wildcards. You can still override expr or datasourceUid explicitly if needed. Get panel IDs from grafana_get_dashboard. Returns instant: { metrics: [{ metric: {...}, value: "1.23", timestamp: "...", healthContext?: { status, thresholds, description, direction } }], datasourceUid, resultCount, warnings?, hint? } — is included for well-known gauge metrics, providing SRE-grade health assessment: ("healthy"/"warning"/"critical"), (warning/critical values), (what the metric means), ("higher_is_worse"/"lower_is_worse"). Omitted for unknown metrics. Capped at 50 results; when exceeded includes , , and advising to narrow the query. : — truncated to 20 points per series and 50 series max. When series are truncated includes , , and . When step is auto-calculated, includes . : Includes , , , alongside normal query results. If the panel uses a Loki datasource, returns an error directing you to use instead. : When Prometheus flags a non-fatal issue (e.g., on a gauge), is included. Example: on a gauge → cause says "rate() applied to 'metric' which appears to be a gauge", suggestion says "use delta() or deriv() instead", example shows the corrected query. : When the query returns zero results, explains why (metric may not exist, label filters may not match) and suggests using to verify. : On query failure, includes alongside the raw error. Pattern-matched for common PromQL mistakes: unclosed parenthesis, missing range selector, timeout, auth failure, rate on gauge, etc. Omitted when the error is unrecognized. : Both instant and range responses include — pass it directly to or other tools without re-calling . This enables zero-friction query→alert chains.

grafana_query_logs

When: User asks about logs, errors, or needs to investigate issues by searching log data. Also for session debugging, OTel log investigation, and re-running existing log panel queries. Params: datasourceUid, expr (LogQL), queryType (instant/range, default range), start/end (default now-1h/now), step (metric queries only), limit (default 100), direction (backward/forward), lineLimit (max chars per log line, default 500, max 2000), extractFields (boolean, default false — extract structured OTel attributes into a clean fields object), dashboardUid (optional — resolve query from panel), panelId (optional — use with dashboardUid). Example log search: { "datasourceUid": "loki1", "expr": "{job=\"api\"} |= \"error\"" } Example with filters: { "datasourceUid": "loki1", "expr": "{job=\"api\"} |~ \"timeout|refused\"", "limit": 50, "direction": "forward" } Example full stack traces: { "datasourceUid": "loki1", "expr": "{job=\"api\"} |= \"Exception\"", "lineLimit": 2000 } Example session debugging: { "datasourceUid": "loki1", "expr": "{service_name=\"openclaw\"} | json | component=\"lifecycle\"", "extractFields": true } Example metric query: { "datasourceUid": "loki1", "expr": "rate({job=\"api\"}[5m])", "queryType": "range", "start": "now-6h", "end": "now", "step": "60" } Example panel re-run: { "dashboardUid": "openclaw-command-center", "panelId": 18, "start": "now-24h", "extractFields": true } Returns streams: { entries: [{ labels: {...}, timestamp: "...", line: "..." }], datasourceUid, totalEntries, truncated } — capped at 100 entries, lines at 500 chars (set lineLimit: 2000 for full stack traces). Returns streams (extractFields): { entries: [{ labels: {...cleaned...}, timestamp: "...", line: "...", fields: { component, event_name, session_id, trace_id, model, duration_s, ... } }], datasourceUid } — infrastructure noise labels removed, prefix stripped from field keys, numeric values auto-converted. Also parses JSON log bodies if present. : When and entries contain , includes — up to 5 unique trace IDs ready for with . : Same shape as range/instant results (matrix capped at 50 series, vector capped at 50 results — includes , , /, and when exceeded). : Includes , , , alongside normal results. If the panel uses a Prometheus datasource, returns an error directing you to use instead. : On query failure, includes alongside the raw error. Pattern-matched for common LogQL mistakes: bare text without stream selector, empty , unclosed braces, missing label matchers, auth failure, timeout. Omitted when the error is unrecognized. : LogQL: selects streams, substring filter, regex, exclude. Metric wrappers: , , . Use when investigating OTel/lifecycle logs — it surfaces , , , , and other attributes as first-class fields instead of buried in raw labels. : Same as — set + to auto-resolve LogQL and datasource. The tool routes Prometheus panels to with a helpful error.

grafana_query_traces

When: User asks about traces, distributed tracing, slow spans, session trace hierarchies, or needs to debug request flows across services. Params: datasourceUid, query (TraceQL expression or trace ID), queryType (search/get, default search), start/end (default now-1h/now), limit (default 20, max 50), minDuration/maxDuration (e.g., "1s", "10s"), dashboardUid (optional — resolve query from panel), panelId (optional — use with dashboardUid). Example search: { "datasourceUid": "tempo1", "query": "{ resource.service.name = \"openclaw\" }" } Example search slow: { "datasourceUid": "tempo1", "query": "{ resource.service.name = \"openclaw\" }", "minDuration": "5s" } Example search with time: { "datasourceUid": "tempo1", "query": "{ span.gen_ai.system = \"anthropic\" }", "start": "now-24h", "limit": 50 } Example get: { "datasourceUid": "tempo1", "query": "abc123def456789...", "queryType": "get" } Example panel re-run: { "dashboardUid": "openclaw-session-explorer", "panelId": 12, "start": "now-24h" } Returns search: { traces: [{ traceId, rootServiceName, rootTraceName, startTime, durationMs, spanCount? }], datasourceUid, totalTraces, truncated?, correlationHint? } — capped at 50 traces. When exceeded includes truncated: true and truncationHint. When traces are found, includes correlationHint: { logQuery, tool, tip } with a ready-to-use LogQL expression for grafana_query_logs. Returns get: { traceId, spans: [{ traceId, spanId, parentSpanId?, operationName, serviceName, startTime, durationMs, status, kind?, attributes: {...} }], datasourceUid, totalSpans, truncated? } — flattened OTLP spans with resolved attributes (string/number/boolean). Capped at 200 spans. Sorted by start time (earliest first). Returns (panel re-run): Includes resolvedFrom: "panel", panelTitle, panelType, alongside normal results. If the panel uses a Prometheus or Loki datasource, returns an error directing you to use the correct tool. : On query failure, includes alongside the raw error. Pattern-matched for common TraceQL mistakes: syntax errors, invalid attributes, auth failure, timeout, not-found, invalid trace ID. Omitted when the error is unrecognized. : When search returns zero traces, includes suggesting to broaden the query or check the datasource. : TraceQL: matches all traces, for service filter, for HTTP spans, for operation name, for span duration, for error/ok filtering. Use / to find performance outliers. : search and get results include — pass it directly to to find correlated logs. : results (with ) include — pass any ID to with . : Same as — set + to auto-resolve TraceQL and datasource. The tool routes Prometheus/Loki panels to the correct tool with a helpful error.

grafana_create_dashboard

When: User wants a persistent dashboard for ongoing monitoring. Params: template or dashboard (custom JSON) — one required. Optional: title (overrides template default), folderUid (target folder), overwrite (default true). Returns: { uid, url, status, message, suggestedNext?: [{ template, reason }], validation?: DashboardValidation }. For template-based dashboards, suggestedNext lists complementary templates to deploy next. For custom JSON dashboards, validation dry-runs each panel's PromQL and reports per-panel health — check validation.panelsError for broken queries.

Choose the right template (3-tier SRE drill-down hierarchy):

Tier 1 → System: Start here for overall health. Tier 2 → Session: Click a session from Tier 1 to investigate. Tier 3 → Deep Dive: Cost, tool, or SRE details.

Template	Tier	Domain	Variables	Use When
llm-command-center	Tier 1	System overview	$prometheus, $loki, $tempo, $provider, $model, $channel	Golden signals, session table with click-to-drill-down, cost, cache, live feeds
session-explorer	Tier 2	Session debug	$prometheus, $loki, $tempo, $session (textbox)	Per-session trace hierarchy, LLM calls, tool calls, conversation flow
cost-intelligence	Tier 3a	Cost analysis	$prometheus, $loki, $provider, $model	Spending trends, model attribution, cache savings, per-session cost table
tool-performance	Tier 3b	Tool analytics	$prometheus, $loki, $tempo, $tool	Tool leaderboard, latency ranking, error rates, tool traces
sre-operations	Tier 3c	SRE operations	$prometheus, $loki	Queue health, webhooks, stuck sessions, tool loops
genai-observability	—	OTel gen_ai standard	$prometheus, $loki, $tempo, $model, $provider	Industry-standard AI monitoring: token analytics, LLM performance, traces, logs, cache efficiency. Works with any gen_ai data.
node-exporter	—	System/DevOps	$datasource, $instance	Server CPU, memory, disk, network

All AI templates have Loki log-to-trace correlation via Tempo + stable UIDs for cross-dashboard navigation.

Example AI health: { "template": "llm-command-center", "title": "My AI Dashboard" } Example session debug: { "template": "session-explorer", "title": "Session Debug" } Example cost analysis: { "template": "cost-intelligence", "title": "My AI Costs" } Example tool analytics: { "template": "tool-performance", "title": "Tool Health" } Example SRE ops: { "template": "sre-operations", "title": "SRE Health" } Example GenAI observability: { "template": "genai-observability", "title": "GenAI Observability" } Example system: { "template": "node-exporter", "title": "Server Health" } Example generic: { "template": "metric-explorer", "title": "Explore My Data" } Example multi-KPI: { "template": "multi-kpi", "title": "Business KPIs" } Example weekly review: { "template": "weekly-review", "title": "My Weekly Review" } Example custom with validation: { "dashboard": { "title": "Model Comparison", "panels": [{ "id": 1, "title": "Cost by Model", "type": "timeseries", "targets": [{ "refId": "A", "expr": "sum by (model) (rate(openclaw_lens_cost_by_token_type[1h]))", "datasource": { "uid": "prometheus" } }] }] } }

Custom dashboard validation (returned only for dashboard param, not templates): validation: { panelsTotal: 3, panelsValid: 1, panelsNoData: 1, panelsError: 1, panelsSkipped: 0, details: [{ panelId: 1, title: "Cost by Model", status: "ok", queries: [{ refId: "A", expr: "...", valid: true, sampleValue: 0.42 }] }, { panelId: 2, title: "Latency", status: "nodata" }, { panelId: 3, title: "Bad Query", status: "error", error: "parse error at char 5" }] } Panel statuses: ok (query returned data), nodata (valid query, no results — metric may not exist yet), error (PromQL syntax error or datasource issue), skipped (no datasource UID found). Dashboard is always created regardless — validation is informational.

grafana_update_dashboard

When: User wants to add a panel, remove a panel, change a query, update dashboard settings, or delete a dashboard. Params: uid (required), operation (required: add_panel, remove_panel, update_panel, update_metadata, delete). add_panel params: panel (object with title, type, targets). Auto-layouts below existing panels. remove_panel / update_panel params: panelId (preferred) or panelTitle (case-insensitive substring fallback). updates (object) for update_panel. update_metadata params: title, description, tags, time (e.g., { "from": "now-7d", "to": "now" }), refresh (e.g., "1m"). delete params: None besides uid — permanently removes the dashboard. Always confirm with user first. Example add: { "uid": "abc123", "operation": "add_panel", "panel": { "title": "Error Rate", "type": "timeseries", "targets": [{ "refId": "A", "expr": "rate(errors_total[5m])", "datasource": { "uid": "prom1" } }] } } Example add (no datasource): { "uid": "abc123", "operation": "add_panel", "panel": { "title": "Latency", "type": "timeseries", "targets": [{ "refId": "A", "expr": "histogram_quantile(0.99, rate(http_duration_bucket[5m]))" }] } } — validation skipped if no datasource UID found, panel still saved. Example remove: { "uid": "abc123", "operation": "remove_panel", "panelId": 3 } Example update panel: { "uid": "abc123", "operation": "update_panel", "panelId": 1, "updates": { "title": "New Title", "targets": [{ "refId": "A", "expr": "new_query" }] } } Example update metadata: { "uid": "abc123", "operation": "update_metadata", "title": "My Dashboard v2", "time": { "from": "now-7d", "to": "now" }, "refresh": "5m" } Example delete: { "uid": "abc123", "operation": "delete" } Returns update: { status: "updated", uid, url, version, operation, panelCount, affectedPanel?: { id, title }, changedFields?: [...], queryValidation?: { validated, results, datasourceUid?, skippedReason? } }. : For and (when targets change), PromQL queries are dry-run against Grafana. Each result: . Panel is always saved — validation is informational. If , check the field for PromQL syntax issues. If is set, no datasource UID was found — include on targets to enable validation. : . : in update_panel replaces entirely — include all targets, not just changed ones. Include on targets for query validation feedback.

grafana_get_dashboard

When: Need to inspect a dashboard's panels — find panel IDs for sharing, verify structure, scan multiple dashboards for an overview, or audit which panels are returning data. Params: uid (required). Optional: compact (boolean, default false) — return panel titles and types only, no queries or metadata (~70% smaller). audit (boolean, default false) — dry-run each panel's query and add health status. Example (full): { "uid": "abc123" } Example (compact overview): { "uid": "abc123", "compact": true } Example (audit): { "uid": "abc123", "audit": true } Returns (full): { uid, title, description?, url, tags, time?, refresh?, panelCount, panels: [{ id, title, type, queries: [{ refId, expr }] }], folderUid, created?, updated? }. Returns (compact): { uid, title, url, tags, panelCount, panels: [{ id, title, type }] }. Returns (audit): Same as full, plus each panel gets health: { status: "ok"|"nodata"|"error"|"skipped", error?, sampleValue? } and the response includes auditSummary: { ok, nodata, error, skipped }. Resolves template variable datasources ($prometheus, $loki) and replaces expression template vars with wildcards. Tip: Use audit: true when the user asks "which panels are broken?" or "audit my dashboard" — it replaces N separate grafana_query calls with one tool call. Use compact: true for lightweight overview scans. Omit both when you need query details (before update or share).

grafana_search

When: User mentions a dashboard by name, before creating one (check duplicates), or for reporting/audit workflows. Params: query (required). Optional: tags (array — filter by tags), starred (boolean — only starred), sort ("alpha-asc"/"alpha-desc"), limit (number, default 100), enrich (boolean — add updatedAt + panelCount per result, default false). Example: { "query": "cost" } Example with tags: { "query": "", "tags": ["production"] } Example starred: { "query": "", "starred": true, "limit": 10 } Example enriched: { "query": "", "enrich": true } Returns: { count, enriched, dashboards: [{ uid, title, url, tags, folderTitle?, folderUid?, updatedAt?, panelCount? }] }. folderTitle/folderUid always included when dashboard is in a folder. updatedAt (ISO 8601) and panelCount only present when enrich: true — enables staleness detection and reporting without per-dashboard get_dashboard calls. Tip: Use enrich: true for reporting workflows ("which dashboards are stale?", "give me a summary of all dashboards"). Skip enrichment for simple lookups. After finding a dashboard, use grafana_get_dashboard to inspect panels, grafana_share_dashboard to render a chart, or grafana_update_dashboard to modify it.

grafana_share_dashboard

When: User says "show me" or "send me" a chart/dashboard. Params: dashboardUid, panelId (required). Optional: from (default "now-6h"), to (default "now"), width (default 1000), height (default 500), theme ("light"/"dark", default "dark"). Example: { "dashboardUid": "abc123", "panelId": 2, "from": "now-6h", "to": "now" } Returns: Image rendered inline (tier 1), or snapshot URL (tier 2), or deep link (tier 3). Always delivers something. Includes deliveryTier ("image" | "snapshot" | "link"), rendererAvailable (boolean — false when Image Renderer plugin is missing), renderFailureReason (why image rendering failed), and remediation (how to fix it). Tier 3 also includes snapshotFailureReason. Tip: Use grafana_get_dashboard first to find panel IDs. If rendererAvailable is false, tell the user to install the grafana-image-renderer plugin.

grafana_create_alert

When: User wants notifications when a metric crosses a threshold. Params: title, datasourceUid, expr (PromQL), threshold (all required). Optional: evaluation ("instant"/"rate"/"increase", default "instant"), evaluationWindow (default "5m", used with rate/increase), condition (gt/lt/gte/lte, default gt), for (duration, default 5m), folderUid, labels (e.g., { "severity": "warning" }), annotations (e.g., { "summary": "Cost too high" }), noDataState (NoData/Alerting/OK, default NoData). IMPORTANT: For counter metrics (*_total), always use evaluation: "rate" (per-second rate) or evaluation: "increase" (total change over window). Raw counter values always increase and will immediately breach any threshold. Use "instant" (default) only for gauges. Example gauge alert: { "title": "High Cost Alert", "datasourceUid": "prom1", "expr": "openclaw_lens_daily_cost_usd", "threshold": 5, "condition": "gt" } Example rate alert: { "title": "High Error Rate", "datasourceUid": "prom1", "expr": "openclaw_lens_webhook_error_total", "threshold": 0.1, "evaluation": "rate" } Example increase alert: { "title": "Token Burst", "datasourceUid": "prom1", "expr": "openclaw_lens_tokens_total", "threshold": 10000, "evaluation": "increase", "evaluationWindow": "1h" } Returns: { uid, title, status: "created", datasourceUid, url, evaluation?: { mode, window, evaluatedExpr }, metricValidation: { valid, error?, sampleValue? }, message }. The datasourceUid echoes back which datasource the rule targets (verify correctness). metricValidation dry-runs the expression before creation — + confirms data exists; + warns of typos/missing metrics. Alert is always created regardless (metric may not have data yet). When is or , validation runs the wrapped expression. : Auto-creates a "Grafana Lens Alerts" folder if no is specified.

grafana_annotate

When: User deploys, changes config, or wants to mark an event for correlation. Params: action ("create" default, or "list"). Create params: text (required), tags, dashboardUid, panelId, time (epoch ms or relative like "now-2h", default now), timeEnd (epoch ms or relative). List params: from, to (epoch ms or relative like "now-7d", "now-24h", "now"), tags, limit (default 20). Time formats: All time params accept epoch ms (e.g., 1700000000000) OR Grafana-style relative strings ("now", "now-1h", "now-7d", "now-30m"). Prefer relative strings — they're simpler and avoid arithmetic errors. Example create: { "text": "Deployed v2.1.0", "tags": ["deploy", "production"] } Example create past: { "text": "Incident started", "time": "now-2h", "timeEnd": "now-30m", "tags": ["incident"] } Example list recent: { "action": "list", "from": "now-7d", "to": "now", "tags": ["deploy"] } Example list: { "action": "list", "tags": ["deploy"], "limit": 10 } Returns create: { status: "created", id, message, time, comparisonHint: { beforeWindow: { from, to }, afterWindow: { from, to }, suggestion } }. The comparisonHint provides ready-to-use ISO 8601 time ranges (30-min windows) for before/after comparison via grafana_query — no manual time math needed. For region annotations (with timeEnd), afterWindow starts at timeEnd. Returns list: { annotations: [{ id, text, tags, time, timeEnd?, dashboardUID?, panelId? }] }.

grafana_check_alerts

When: Prompt context shows "GRAFANA ALERTS", need to manage alert rules (list/delete), set up the alert webhook, silence alerts during investigation, or acknowledge an investigated alert. Params: action ("list" default, "acknowledge", "list_rules", "delete_rule", "silence", "unsilence", "setup"). List params: None — returns all pending (unacknowledged) alerts. Instances capped at 5 per alert. Acknowledge params: alertId (required) — marks an alert as investigated. List rules params: compact (boolean, default false — returns only uid/title/state/condition). Full mode returns all configured alert rules from Grafana with UID, title, condition (PromQL), folder, labels, annotations, AND live evaluation state (normal/firing/pending/nodata/error), health, and lastEvaluation. One call gives the complete alert health picture. Delete rule params: ruleUid (required) — permanently deletes an alert rule. Get UIDs from list_rules. Silence params: matchers (required — array of { name, value, isRegex? } from alert's commonLabels), duration (default "2h"), comment (optional). Unsilence params: silenceId (required) — removes a silence so alerts resume notifying. Setup params: webhookUrl (optional, auto-detected) — creates webhook contact point and notification policy route in Grafana. Example list: {} Example acknowledge: { "action": "acknowledge", "alertId": "alert-1" } Example list rules: { "action": "list_rules" } Example list rules compact: { "action": "list_rules", "compact": true } Example delete rule: { "action": "delete_rule", "ruleUid": "abc123-def456" } Example silence: { "action": "silence", "matchers": [{ "name": "alertname", "value": "HighCost" }], "duration": "2h", "comment": "Investigating cost spike" } Example unsilence: { "action": "unsilence", "silenceId": "silence-uuid-123" } : : . is auto-enriched by matching the alert to its rule — provides the PromQL/LogQL expression, datasource, and tool to use for immediate investigation (eliminates the need for separate + calls). : . : . is the live evaluation state: (not firing), , (within for duration), , or . Falls back to if the eval state API is unavailable. is , , , or . is the extracted PromQL expression from the rule's data queries. : . Minimal fields for multi-tool chains — use when you need a quick overview of all rules without details. : . : . : . : or . : Setup is idempotent — safe to call multiple times. Only alerts with label route to the webhook (auto-added by ). Use → for full alert lifecycle management (create via , list/delete via ).

grafana_push_metrics

When: User wants to track custom data (calendar events, git commits, fitness stats, financial data) in Grafana. Params: action ("push" default, "register", "list", "delete"). Push params: metrics (required array) — each: { name, value, labels?, type?, help?, timestamp? }. Names auto-get openclaw_ext_ prefix. timestamp is optional ISO 8601 for historical data (gauge only). Register params: name (required), type ("gauge"/"counter", default "gauge"), help, labelNames (array), ttlDays. List params: None — returns all custom metric definitions. Delete params: name (required) — removes a custom metric. Example push: { "metrics": [{ "name": "steps_today", "value": 8000 }, { "name": "meetings", "value": 3, "labels": { "type": "standup" } }] } Example backfill: { "metrics": [{ "name": "steps", "value": 8000, "timestamp": "2025-01-15" }, { "name": "steps", "value": 10500, "timestamp": "2025-01-16" }] } Example mixed: { "metrics": [{ "name": "steps", "value": 9000, "timestamp": "2025-01-17" }, { "name": "heart_rate", "value": 72 }] } Example register: { "action": "register", "name": "weight_kg", "type": "gauge", "help": "Body weight", "labelNames": ["person"], "ttlDays": 90 } Example list: { "action": "list" } Example delete: { "action": "delete", "name": "old_metric" } Returns push: { status: "ok", accepted: 2, queryNames: { "openclaw_ext_steps": "openclaw_ext_steps", "openclaw_ext_events": "openclaw_ext_events_total" }, suggestedWorkflow: [{ tool, action, example }], message: "..." }. suggestedWorkflow contains concrete next-step examples using the actual pushed metric names — verify (grafana_query), visualize (grafana_create_dashboard with metric-explorer template), and alert (grafana_create_alert, single-metric only). Partial success supported. Timestamped and real-time points in the same batch are both accepted. Returns register: . shows how to push data and query the registered metric (with wrapping for counters). : . : . : Push auto-registers unknown metrics. Response includes with exact PromQL names and with concrete next steps. Follow to complete the push→visualize pipeline. Timestamped pushes are gauge-only — counters with timestamps are rejected. See for naming conventions and backfill patterns.

grafana_explain_metric

When: User asks "what does this metric mean?", "why did it spike?", "is this normal?", or "show me the trend". Params: datasourceUid (required), expr (PromQL or plain metric name, required), period (24h/7d/30d, default 24h), compareWith ("previous" — compare current period with the same-length window immediately before it). Example: { "datasourceUid": "prom1", "expr": "openclaw_lens_daily_cost_usd" } Example counter: { "datasourceUid": "prom1", "expr": "openclaw_lens_tokens_total" } Example 7d: { "datasourceUid": "prom1", "expr": "openclaw_lens_daily_cost_usd", "period": "7d" } Example comparison: { "datasourceUid": "prom1", "expr": "openclaw_lens_daily_cost_usd", "period": "7d", "compareWith": "previous" } Example PromQL: { "datasourceUid": "prom1", "expr": "rate(http_requests_total[5m])", "period": "24h" } Returns: { metricType?, trendQuery?, current: { value, timestamp }, healthContext?: { status, thresholds, description, direction }, trend: { changePercent, direction, first, last }, stats: { min, max, avg, samples }, comparison?: { previousPeriod: { from, to, avg, min, max, samples }, change: { absolute, percentage, direction } }, metadata: { type, help, unit }, suggestedQueries?: [{ query, description }], suggestedBreakdowns?: string[] }. Sections omitted when data unavailable. changePercent is null when first value is zero. healthContext is included for well-known openclaw_lens_* gauge metrics — same as grafana_query. Counter-aware: Auto-detects counter metrics (via metadata type or _total suffix) and wraps the trend query in rate(expr[5m]). The current value stays raw (cumulative total), but trend and stats show rate of change. metricType field tells you the detected type (counter/gauge/histogram). trendQuery shows the actual PromQL used for trend (only present when different from expr). Drill-down: For multi-dimensional metrics (metrics with labels like , , ), the response includes — ready-to-use PromQL queries for that break down the metric by each label. Counter metrics get wrapping automatically. Use these to investigate cost attribution, identify top contributors, or decompose aggregates. : provides label names for decomposition — always available for known OpenClaw metrics (cost, session, queue, webhook families) even when the metric has no data yet. For unknown metrics, falls back to labels discovered from the instant query. Use these labels with to build queries for root-cause analysis. : Use for period-over-period analysis (e.g., this week vs. last week). Returns a object with the previous period's stats and the change (absolute, percentage, direction). Works with counters too (compares rates). Eliminates the need for manual multi-query workflows. : For simple trend context, call with just . For "did things improve?" questions, add . Metadata only available for plain metric names (not complex PromQL). No need to manually wrap counters in — the tool does it automatically.

grafana_security_check

When: User asks "am I being attacked?", "security status", "security audit", "security check", or wants a comprehensive threat assessment. Params: lookback (time window, default "1h". Use "24h" for daily review, "7d" for weekly). Example: {} Example weekly review: { "lookback": "7d" } Returns: { overallThreatLevel: "green"|"yellow"|"red", summary, checks: [{ name, status, value, detail }], securityEventLogs, limitations: [...], suggestedActions: [...], dashboardTemplate: "security-overview" }. Checks (6 parallel, via Promise.allSettled — partial failures return partial results):

1. webhook_error_ratio — webhook error rate vs received rate. Warning 20%, critical 50%.
2. cost_anomaly — daily cost. Warning $10, critical $50.
3. tool_loops — active tool loops. Warning 1, critical 3.
4. injection_signals — prompt injection patterns detected. Warning 1, critical 5.
5. session_enumeration — unique sessions in 1h. Warning 50, critical 200.
6. stuck_sessions — stuck sessions. Warning 1, critical 3. Limitations: Auth failures are NOT observable — openclaw's auth middleware emits no diagnostic events. The tool monitors observable signals (webhook errors, cost spikes, prompt injection patterns, session anomalies) but cannot detect silent auth-layer attacks (bad tokens, brute-force, rate-limiter lockouts). Always includes limitations in response. Auto-discovery: No datasource UID needed — auto-discovers first Prometheus datasource. Loki is optional (skips log check if absent). Follow-up: Use dashboardTemplate to create a persistent security dashboard. Use suggestedActions for investigation steps.

grafana_investigate

When: First step for "investigate this", "what's wrong?", "triage", "root cause analysis", "debug this issue", or any multi-signal investigation. Params: focus (required — alert UID, metric name, or symptom text), timeWindow (optional, default "1h", options: "1h", "6h", "24h"), service (optional, default "openclaw"). Example: { "focus": "high error rate" } Example with alert: { "focus": "alert-abc", "timeWindow": "6h" } Example with metric: { "focus": "openclaw_lens_daily_cost_usd", "timeWindow": "24h" } Returns: { timeWindow, focus, metricSignals: { focus: { current, trend, anomalyScore, anomalySeverity }, red: { rate, errorRate, p95Latency } }, logSignals: { totalVolume, errorCount, bySeverity, topPatterns, sampleErrors }, traceSignals: { errorTraces, slowTraces }, contextSignals: { recentAnnotations, alertsActive }, suggestedHypotheses: [{ hypothesis, evidence, confidence, testWith: { tool, params } }], limitations }. Auto-discovery: No datasource UID needed — auto-discovers Prometheus, Loki, and Tempo datasources. Loki and Tempo are optional (graceful degradation with limitations noted). Follow-up: Use suggestedHypotheses[].testWith for deep-dives with specific tools. Use grafana_annotate to mark findings. Use grafana_check_alerts to acknowledge investigated alerts.

Security Monitoring

Honest limitations: OpenClaw's auth middleware does not emit diagnostic events for failed authentication attempts (bad tokens, brute-force, rate-limiter lockouts). Gateway-level auth failures are invisible to Grafana Lens. The security-check tool monitors observable signals (webhook errors, cost spikes, prompt injection patterns, session anomalies) but cannot detect silent auth-layer attacks.

Security PromQL queries:

• Webhook error spike: rate(openclaw_lens_webhook_error_total[5m]) > 0.5
• Webhook error ratio: rate(openclaw_lens_webhook_error_total[5m]) / (rate(openclaw_lens_webhook_received_total[5m]) + 0.001) > 0.3
• Active tool loops: sum(openclaw_lens_tool_loops_active) > 0
• Prompt injection signals: sum(increase(openclaw_lens_prompt_injection_signals_total[1h])) > 0
• Cost spike: openclaw_lens_daily_cost_usd > 10
• Unique session anomaly: openclaw_lens_unique_sessions_1h > 50
• Gateway restarts: sum(increase(openclaw_lens_gateway_restarts_total[24h])) > 3
• Tool error classification: sum by (error_class) (rate(openclaw_lens_tool_error_classes_total[5m]))

Security LogQL queries:

• Security events: {service_name="openclaw"} | json | event_name=~"prompt_injection.detected|gateway.start|gateway.stop"
• Tool loops: {service_name="openclaw"} | json | component="diagnostic" | event_name="tool.loop"
• Session resets: {service_name="openclaw"} | json | event_name="session.reset"

Composed Workflow Examples

"How's my agent doing?"

1. grafana_query with sum(increase(openclaw_lens_cost_by_model_total[1d])) or vector(0) and openclaw_lens_tokens_total for quick numbers
2. Or grafana_create_dashboard with llm-command-center template + grafana_share_dashboard for a visual

"Set up monitoring for my agent" / "What dashboards should I create?"

1. grafana_search query: "LLM Command Center" — check for existing agent dashboards
2. grafana_create_dashboard template: "llm-command-center" — single-pane health: cost, sessions, errors, latency, cache, live feeds
3. Follow suggestedNext from the response — it chains you through the remaining templates (session-explorer → cost-intelligence → tool-performance → sre-operations → genai-observability)
4. Share dashboard URLs with user
5. Optional: grafana_share_dashboard to show a key panel inline

The 6 AI observability templates provide comprehensive OpenClaw monitoring with Loki log-to-trace correlation:

• llm-command-center: Single-pane health view (daily cost, sessions, error rate, P95 latency, cache hit rate, token flow, live error/session feeds, per-model latency, message type breakdown)
• session-explorer: Per-session deep-dive (trace hierarchy, LLM calls, tool calls, conversation flow, cost/token breakdown, TraceQL session spans, latency stats). THE killer feature.
• cost-intelligence: Financial analysis (daily/weekly/monthly trends, model/provider/token attribution pie charts, cache savings, per-session cost table, projected monthly cost, token efficiency)
• tool-performance: Tool reliability (leaderboard bar gauges, p95 latency ranking, error rates, tool traces, tool calls by model, error traces, duration heatmap)
• sre-operations: Infrastructure health (queue depth, webhooks, stuck sessions, tool loops, gen_ai error rate, context window pressure)
• genai-observability: Industry-standard AI monitoring using OTel gen_ai semantic conventions — token analytics, LLM latency heatmap, trace explorer, log intelligence, cache efficiency. Works with any gen_ai data, not just OpenClaw.

"Find error logs in the last hour"

1. grafana_explore_datasources — find Loki datasource UID
2. grafana_query_logs with {job="api"} |= "error"

"Why did my service crash?"

1. grafana_explore_datasources — find Loki UID
2. grafana_query_logs with {job="myservice"} |~ "fatal|panic|crash"
3. grafana_query — check related metrics around the same time window

"Investigate alert: correlate metrics with logs"

1. grafana_check_alerts — get alert details with suggestedInvestigation (includes ready-to-use query, datasource, and tool)
2. grafana_query / grafana_query_logs — use suggestedInvestigation.tool with suggestedInvestigation.condition and suggestedInvestigation.datasourceUid
3. grafana_query_logs — search logs around alert time for root cause (if PromQL alert)
4. grafana_annotate — mark findings on dashboard
5. grafana_check_alerts with action: "acknowledge"

"What data do I have in Grafana?"

1. grafana_explore_datasources — find Prometheus datasources
2. grafana_list_metrics with datasourceUid and metadata: true — list available metrics grouped by category
3. Summarize what's available using categorySummary for quick overview (e.g., "5 cost metrics, 8 session metrics, 3 queue metrics")

"My agent feels slow — what performance metrics can I look at?"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_list_metrics with purpose: "performance", metadata: true — returns only session + tool metrics (latency, duration, tool calls)
3. grafana_explain_metric on key metrics (e.g., openclaw_lens_session_latency_avg_ms, openclaw_lens_tool_duration_ms) — get current values and trends

"Alert me if costs exceed $10/day"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_create_alert with expr: "openclaw_lens_daily_cost_usd", threshold: 10, condition: "gt"

"Query error rate and alert if it's above 1%" (query→alert chaining)

1. grafana_query with expr: "rate(http_errors_total[5m])" — response includes datasourceUid
2. grafana_create_alert with datasourceUid from the query response, same expr, threshold: 0.01, condition: "gt" — no extra datasource discovery needed

"Show me my cost trends"

1. grafana_search with query: "cost" — check for existing dashboard
2. If not found: grafana_create_dashboard with cost-intelligence template
3. grafana_get_dashboard — find the right panel ID
4. grafana_share_dashboard — deliver the chart inline

"Re-run the latency panel from my Command Center for the last 7 days"

1. grafana_search with query: "Command Center" — get the dashboard UID
2. grafana_get_dashboard with uid — find the latency panel ID
3. grafana_query with dashboardUid, panelId, queryType: "range", start: "now-7d" — auto-resolves PromQL and datasource from the panel

"Mark that I deployed version 2"

1. grafana_annotate with text: "Deployed v2", tags: ["deploy"]

"I deployed v2.3.0 — compare error rates before and after"

1. grafana_annotate with text: "Deployed v2.3.0", tags: ["deploy"] — response includes comparisonHint with before/after time windows
2. grafana_query with comparisonHint.beforeWindow time range — get pre-deploy error rate
3. grafana_query with comparisonHint.afterWindow time range — get post-deploy error rate
4. Compare and report the difference to the user

"What happened around 3pm?"

1. grafana_annotate with action: "list", from/to relative times (e.g., "now-6h") or epoch ms, or specific tags

"Monitor my server's health"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_search with query: "node" — check for existing
3. grafana_create_dashboard with template: "node-exporter" — user picks instance from dropdown

"Show me my e-commerce metrics"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_list_metrics with prefix: "orders_" — discover available metrics
3. grafana_create_dashboard with template: "multi-kpi" — user picks 4 business metrics from dropdowns

"I want to explore my fitness data"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_create_dashboard with template: "metric-explorer" — user browses all fitness_* metrics from dropdown

"Set up HTTP API monitoring with alerts"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_create_dashboard with template: "http-service"
3. grafana_create_alert with expr: "http_requests_total{code=~\"5..\"}", threshold: 0.1, evaluation: "rate" — the tool wraps in rate() automatically

"Build a custom Redis dashboard"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_list_metrics with metadata: true, prefix: "redis_" — learn metric types
3. Compose custom dashboard JSON using panel snippets from the composition guide
4. grafana_create_dashboard with the custom JSON — check validation.panelsError for broken queries, fix any errors with grafana_update_dashboard

"Set up full alert monitoring loop"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_check_alerts with action: "setup" — create webhook contact point
3. grafana_create_alert with PromQL condition — rule auto-routes to webhook via managed_by=openclaw label
4. When alert fires, agent sees it in prompt context and can investigate

"List all my alert rules and delete one"

1. grafana_check_alerts with action: "list_rules" — get all configured rules with UIDs, titles, conditions, AND live eval state (normal/firing/nodata/error)
2. Identify the target rule by title, condition, or state
3. grafana_check_alerts with action: "delete_rule", ruleUid — permanently remove the rule

"Investigate a firing alert" (triggered by "GRAFANA ALERTS" in prompt context)

1. grafana_investigate with focus = alert UID or symptom, timeWindow: "1h" — parallel multi-signal triage (metrics, logs, traces, context)
2. grafana_check_alerts with action: "silence", matchers from alert's commonLabels — prevent repeat notifications
3. Follow suggestedHypotheses[].testWith from investigate response — deep-dive with specific tools
4. grafana_query_logs — search logs around alert time for root cause (statistics first: count_over_time, then samples)
5. grafana_query_traces — inspect error/slow traces for span-level detail
6. grafana_annotate — mark findings on dashboard
7. grafana_check_alerts with action: "acknowledge" — mark as investigated
8. grafana_check_alerts with action: "unsilence", silenceId — restore notifications
9. Report findings using Evidence Presentation Format (see sre-investigation.md §10)

"Is this metric anomalous?" / "Statistical assessment"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_explain_metric with period: "24h" — response includes anomaly (z-score, severity) and seasonality (vs 1d/7d ago)
3. If anomaly.severity >= "significant": grafana_query with z-score PromQL for time series view
4. grafana_query with predict_linear(METRIC[6h], 3600) — where is it heading?
5. Interpret: report σ-level, compare with yesterday/last week, forecast trajectory

"Alert fatigue analysis — which alerts are noisy?"

1. grafana_check_alerts with action: "analyze" — fatigue report with always-firing, flapping, and healthy classifications
2. For always-firing rules: suggest raising thresholds or adding for: duration
3. For flapping rules: suggest adding hysteresis or widening threshold bands
4. grafana_check_alerts with action: "silence" for rules being tuned

"Generate a postmortem for the last incident"

1. grafana_investigate with focus = incident symptom, timeWindow: "6h" — gather all evidence
2. grafana_check_alerts (list) — find resolved alerts with timeline
3. grafana_annotate (list, tags: ["investigation"]) — build event timeline
4. grafana_explain_metric for key metrics — trend/stats for incident period
5. grafana_query_logs — error patterns (statistics first)
6. grafana_query_traces — error traces for root cause evidence
7. Synthesize into blameless postmortem (see sre-investigation.md §9)

"Add an error rate panel to my API dashboard"

1. grafana_search with query: "API" — find the dashboard
2. grafana_get_dashboard — get current panels and structure
3. grafana_update_dashboard with operation: "add_panel", panel: { title: "Error Rate", type: "timeseries", targets: [...] }

"Remove the old latency panel and add a new one"

1. grafana_get_dashboard — find panel IDs
2. grafana_update_dashboard with operation: "remove_panel", panelId: <old_id>
3. grafana_update_dashboard with operation: "add_panel", panel: { ... }

"Change my dashboard time range to 7 days"

1. grafana_update_dashboard with operation: "update_metadata", time: { "from": "now-7d", "to": "now" }

"Send my team the weekly dashboard"

1. grafana_search with the dashboard name
2. grafana_get_dashboard — get panel IDs
3. grafana_share_dashboard for each key panel with from: "now-7d"

"Show me my weekly work review"

1. grafana_push_metrics — push work data (commits, hours, meetings)
2. grafana_create_dashboard with template: "weekly-review" — weekly trends + all custom metrics table
3. grafana_share_dashboard — deliver key panels inline

"Track my daily fitness data"

1. grafana_push_metrics — push fitness metrics (steps, weight, calories). Response has queryNames with exact PromQL names.
2. grafana_create_dashboard with template: "weekly-review" — weekly trends
3. grafana_create_alert using queryNames from step 1 for exact PromQL — e.g., threshold: 5000, condition: "lt"

"Backfill my step count for the past week"

1. grafana_push_metrics — push 7 data points with timestamp on each: { "metrics": [{ "name": "steps", "value": 8000, "timestamp": "2025-01-13" }, { "name": "steps", "value": 10500, "timestamp": "2025-01-14" }, ...] }
2. grafana_create_dashboard with template: "metric-explorer" — visualize the trend
3. grafana_share_dashboard with from: "now-7d" — deliver the chart

"What custom data have I pushed?"

1. grafana_push_metrics with action: "list" — see all custom metric definitions with queryName per metric
2. grafana_query using queryName values — see current values
3. Or grafana_list_metrics with search: "ext" — server-side discovery from Prometheus

"System health check — give me a full status report"

1. grafana_explore_datasources — discover available datasources
2. grafana_check_alerts — check pending alerts + action: "list_rules" for configured rules
3. grafana_search with query: "" and enrich: true — find all dashboards with updatedAt and panelCount for staleness triage
4. grafana_get_dashboard with audit: true for stale or suspicious dashboards — check which panels have data vs broken
5. Summarize: datasources available, alerts firing, dashboard health (auditSummary), stale dashboards, broken panels

"Audit my dashboard — which panels are broken?"

1. grafana_get_dashboard with audit: true — dry-runs every panel's query
2. Review auditSummary for counts (ok, nodata, error, skipped)
3. For error panels, explain the issue; for nodata panels, suggest fixes (missing metrics, wrong datasource)

"What is this metric doing?"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_explain_metric with metric name — get current value, trend, stats, metadata
3. Interpret results for the user — the tool provides data, you provide narrative

"Compare costs this week vs. last week" / "Did the new model help?"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_explain_metric with period: "7d", compareWith: "previous" — one call gives this week's stats + last week's stats + percentage change
3. Interpret the comparison.change object — "costs are down 25% week-over-week, the model switch is working"

"Compare metric over different time scales"

1. grafana_explain_metric with period: "24h" — recent trend
2. grafana_explain_metric with period: "7d" — weekly context
3. Compare and explain — "up 15% today but down 5% over the week"

"Where are my costs going?" / "Why did my costs spike?"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_explain_metric with openclaw_lens_cost_by_token_type — get trend + suggestedBreakdowns (always: ["model", "token_type", "provider"]) + suggestedQueries
3. Use suggestedBreakdowns to know which dimensions matter, or pick from suggestedQueries for ready-to-use PromQL
4. grafana_query with sum by (model) (rate(openclaw_lens_cost_by_token_type[5m])) — get concrete breakdown numbers
5. Explain to user: "Opus accounts for 80% of costs, mainly from output tokens"

"Set up AI observability" / "Show me LLM traces and logs"

1. grafana_explore_datasources — verify Prometheus, Loki, and Tempo datasources are available
2. grafana_search with query: "LLM Command Center" — check for existing
3. grafana_create_dashboard with template: "llm-command-center" — health overview with live error/session feeds
4. grafana_create_dashboard with template: "session-explorer" — per-session trace drill-down
5. grafana_share_dashboard — deliver an LLM latency or token usage panel inline

"Why is my LLM slow?" (multi-signal investigation)

1. grafana_explain_metric with gen_ai_client_operation_duration_seconds — check latency trend
2. grafana_query_logs with {service_name="openclaw"} | json | component="lifecycle" |= "llm.output", extractFields: true — find slow LLM calls with fields.duration_s and fields.model surfaced directly
3. grafana_query_traces with queryType: "get" and fields.trace_id from step 2 — inspect spans; response includes correlationHint.logQuery for correlated logs
4. grafana_query with model-specific duration breakdown — identify which model is slow

"Debug a slow or failing session"

1. grafana_query_traces with { resource.service.name = "openclaw" && status = error } or minDuration: "10s" — find problematic traces
2. grafana_query_traces with queryType: "get" and the trace ID — inspect span hierarchy (response includes correlationHint)
3. grafana_query_logs with the correlationHint.logQuery from step 2, extractFields: true — correlated logs
4. grafana_query with relevant PromQL — check metrics around the same time window
5. grafana_annotate with text: "Debug: [findings]", tags: ["debug"] — mark investigation on dashboards

"Which dashboards are stale?" / "Give me a dashboard summary report"

1. grafana_search with query: "" and enrich: true — gets updatedAt, panelCount, folderTitle for every dashboard
2. Sort by updatedAt to identify stale dashboards (e.g., not updated in 30+ days)
3. Summarize: total dashboards, by folder, stale vs active, panel counts

"Am I being attacked?" / "Security status" / "Security audit"

1. grafana_security_check — runs 6 parallel security checks, returns threat level (green/yellow/red)
2. If non-green: follow suggestedActions from the response
3. If dashboardTemplate is present: grafana_create_dashboard with template: "security-overview" — persistent monitoring
4. grafana_create_alert using suggested thresholds for ongoing monitoring

"Set up security monitoring"

1. grafana_check_alerts with action: "setup" — create webhook contact point (if not already done)
2. grafana_create_dashboard with template: "security-overview" — 15-panel security dashboard
3. grafana_create_alert with expr: "rate(openclaw_lens_webhook_error_total[5m]) / (rate(openclaw_lens_webhook_received_total[5m]) + 0.001)", threshold: 0.3, condition: "gt" — webhook error burst
4. grafana_create_alert with expr: "openclaw_lens_daily_cost_usd", threshold: 10, condition: "gt" — cost spike
5. grafana_create_alert with expr: "sum(increase(openclaw_lens_prompt_injection_signals_total[1h]))", threshold: 3, condition: "gt" — prompt injection signals

"Investigate security alert"

1. grafana_security_check — get current threat assessment
2. grafana_query_logs with {service_name="openclaw"} | json | component="lifecycle" | event_name=~"prompt_injection.detected|gateway.start|gateway.stop" — security event logs
3. grafana_query with specific PromQL for the affected check (e.g., rate(openclaw_lens_tool_error_classes_total[5m]))
4. grafana_annotate with text: "Security investigation", tags: ["security"] — mark investigation on dashboards
5. grafana_check_alerts with action: "acknowledge" — mark alerts as investigated

"Clean up old test dashboards"

1. grafana_search with query: "test" or relevant tags and enrich: true — includes updatedAt for staleness check
2. Confirm with user which dashboards to delete
3. grafana_update_dashboard with operation: "delete" for each confirmed dashboard

"Find my production dashboards"

1. grafana_search with tags: ["production"] or starred: true

"Why is the queue backing up?"

1. grafana_explore_datasources — get Prometheus UID
2. grafana_query with openclaw_lens_queue_depth — current queue depth
3. grafana_query with sum(rate(openclaw_message_queued_total[5m])) by (openclaw_source) — message inflow by source
4. grafana_query with histogram_quantile(0.95, rate(openclaw_queue_wait_ms_milliseconds_bucket[5m])) — p95 queue wait time
5. grafana_query with sum(rate(openclaw_queue_lane_enqueue_total[5m])) vs sum(rate(openclaw_queue_lane_dequeue_total[5m])) — enqueue vs dequeue rate
6. grafana_query with openclaw_lens_sessions_stuck — check for stuck sessions blocking the queue
7. Diagnose: if inflow > drain → scale issue; if stuck sessions → investigate with grafana_explain_metric

Dashboard Composition

For composing custom dashboards from discovered metrics — template selection, metric-to-panel mapping, JSON snippets, worked examples across domains — see references/dashboard-composition.md.

Agent Metrics

For metric names, types, labels, and common PromQL — covering both openclaw_* (from diagnostics-otel) and openclaw_lens_* (from grafana-lens) — see references/agent-metrics.md.

External Data

For external data naming conventions and integration patterns, see references/external-data.md.

SRE Investigation Patterns

For 5-phase investigation methodology, anomaly detection PromQL, RED/USE method queries, LogQL investigation discipline, TraceQL debugging, SLI/SLO burn rates, cross-signal correlation, and composed investigation workflows — see references/sre-investigation.md.