grafana-lens

name	grafana-lens
description	Grafana tools for data visualization, monitoring, alerting, security, SRE investigation, and data collection pipeline management via Alloy. 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, grafana_investigate, and alloy_pipeline. 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, collect metrics from, monitor my database, monitor my app, scrape endpoint, set up log collection, collect Docker logs, tail log files, collect Kubernetes logs, receive OTLP, set up trace collection, data collection pipeline, Alloy pipeline, pipeline status, pipeline health, node exporter, system metrics, postgres exporter, mysql exporter, redis exporter, syslog, Grafana Alloy.
metadata	{"openclaw":{"emoji":"🔭","requires":{"config":["grafana.url","grafana.apiKey"]}}}

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)
• Use openclaw_ext_ prefix for custom metrics — grafana_push_metrics auto-prepends it if missing
• Follow statistics-first discipline for log investigation — always run count/rate LogQL before reading individual entries. Use grafana_query_logs with metric-over-logs queries (count_over_time, rate, topk) before switching to raw log entries
• Silence alerts during investigation — use grafana_check_alerts with action silence to prevent repeat notifications while investigating
• Use list_rules for complete alert health — grafana_check_alerts with action list_rules returns all rules with live eval state (normal/firing/pending/nodata/error), health, and lastEvaluation — no need to cross-reference with list action
• Use dashboardUid + panelId to re-run panel queries — don't manually extract PromQL/LogQL from get_dashboard output. Both grafana_query and grafana_query_logs accept these params to auto-resolve the panel's query expression and datasource. The tool handles template variable replacement and datasource routing automatically
• Confirm with user before deleting dashboards or alert rules — grafana_update_dashboard with operation delete and grafana_check_alerts with action delete_rule are permanent and cannot be undone
• Always use alloy_pipeline action recipes first when unsure which pipeline recipe fits the user's request — because recipes provide validation, credential handling, and sample queries that raw config does not
• Always call alloy_pipeline action status after creating a pipeline — because data takes 15-20s to flow through the pipeline, and components may fail silently after reload
• Never guess Alloy component names — use recipes for known patterns, or raw config only when the user explicitly provides Alloy syntax
• Prefer recipes over raw config when a recipe exists — recipes provide validation, sample queries, credential handling, dashboard templates, and automatic export target wiring
• Never write credentials into raw config — when the user provides a connection string, DSN, password, or API key, ALWAYS use the matching recipe (which routes credentials through sys.env(), keeping secrets off disk). If you must use raw config, wrap sensitive values in sys.env("MY_VAR_NAME") and tell the user to set that env var where Alloy runs
• Read envVarsRequired from every pipeline create response — credential recipes may return pending_credentials status when env vars aren't set yet. Tell the user the exact var names and that they must set them where Alloy runs, then verify with action status
• Warn users before creating credential-required pipelines — Alloy config reload is atomic: if a credential recipe's env vars aren't set, the reload failure blocks ALL managed pipelines (not just the new one) until the env vars are set or the pipeline is deleted. Always ask: "Do you have the credentials ready to set as env vars on the Alloy host?"
• Chain pipeline creation into existing tools — after pipeline is active: grafana_list_metrics or grafana_query_logs to discover data, grafana_create_dashboard to visualize, grafana_create_alert to monitor
• Use alloy_pipeline action diagnose as first step when user reports pipeline issues — because it checks Alloy connectivity, all pipeline health, config file drift, and limits in one call
• Confirm with user before deleting pipelines — alloy_pipeline with action delete removes the config and data stops flowing
• All log recipes accept processing params — don't create separate "processing" pipelines. Add jsonExpressions, labelFields, structuredMetadata, tenantValue, matchRoutes, etc. directly to any log recipe (docker-logs, file-logs, syslog, etc.)
• Use samplingPolicies for multi-policy tail sampling — don't create raw config when application-traces can handle it. sampleRate is for simple probabilistic, samplingPolicies is for intelligent multi-policy (keep errors, keep slow, sample rest)
• Use log processing params for multi-tenant routing — tenantValue/tenantSource/matchRoutes work on ALL log recipes. Don't create separate "routing" pipelines
• Read references/alloy-components.md before composing raw config — it has copy-pasteable snippets for all common Alloy components

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 suggestedNext chain through remaining templates
• "GenAI observability" / "OTel gen_ai metrics" / "Standard AI monitoring" → grafana_create_dashboard with genai-observability template
• "What happened in session X?" / "Debug this session" → grafana_create_dashboard with session-explorer template → paste session ID
• "Show me LLM traces" / "Show agent logs" → grafana_create_dashboard with llm-command-center template (Loki + Tempo)
• "How much am I spending?" / "Cost analysis" → grafana_create_dashboard with cost-intelligence template
• "Which tools are slow?" / "Tool errors" → grafana_create_dashboard with tool-performance template
• "Queue health" / "Webhook issues" / "Stuck sessions" → grafana_create_dashboard with sre-operations template
• "System health check" / "Status report" / "Review all dashboards" → grafana_explore_datasources → grafana_check_alerts (list + list_rules) → grafana_search → grafana_get_dashboard (audit=true for each) → summarize
• "Audit my dashboard" / "Which panels are broken?" → grafana_get_dashboard (audit=true) → review auditSummary + per-panel health
• "Am I being attacked?" / "Security check" / "Security status" → grafana_security_check
• "Set up security monitoring" → grafana_check_alerts (setup) → grafana_create_dashboard (security-overview) → grafana_create_alert (webhook error burst, cost spike, tool loops, injection signals)
• "Investigate security alert" → grafana_security_check → grafana_query_logs (correlate) → grafana_annotate (mark investigation) → grafana_check_alerts (silence)
• "Investigate this alert" / "Why is X broken?" / "Debug this issue" / "Triage" / "Root cause" → grafana_investigate (multi-signal triage) → follow suggestedHypotheses.testWith for deep-dives
• "Is this metric normal?" / "Is there an anomaly?" → grafana_explain_metric (returns anomaly z-score + seasonality vs 1d/7d ago for 24h period)
• "RED analysis" / "What's the error rate?" / "Service health" → RED Method queries (see sre-investigation.md §2)
• "Alert fatigue" / "Which alerts are noisy?" / "Alert health" → grafana_check_alerts with action analyze — fatigue report
• "Postmortem" / "Incident summary" / "What happened?" → grafana_investigate → 5-Phase methodology → postmortem template (see sre-investigation.md §9)
• "Compare before/after deployment" → grafana_annotate (list, tags: ["deploy"]) → grafana_explain_metric (compareWith: "previous")

Data Collection Pipelines (Alloy)

• "Monitor a service/database/app" → alloy_pipeline action recipes (filter by category) → select recipe → create → status → query → dashboard → alert
• "Scrape metrics from [endpoint]" / "My app exposes /metrics" → alloy_pipeline with recipe scrape-endpoint + params { url }
• "Monitor PostgreSQL/MySQL/Redis/MongoDB/Memcached" → alloy_pipeline with recipe [db]-exporter + params { connectionString }
• "Collect and parse logs with JSON extraction" → alloy_pipeline (log recipe + processing params: jsonExpressions, labelFields, structuredMetadata)
• "Collect Docker logs" / "See container logs in Grafana" → alloy_pipeline with recipe docker-logs
• "Tail log files" / "Collect app logs from /var/log" → alloy_pipeline with recipe file-logs + params { paths }
• "Accept logs via HTTP push API" / "Centralized log gateway" → alloy_pipeline with recipe loki-push-api
• "Consume logs from Kafka" → alloy_pipeline with recipe kafka-logs + params { brokers, topics }
• "Set up syslog collection" → alloy_pipeline with recipe syslog
• "Monitor endpoint availability" / "Synthetic probing" / "HTTP health checks" → alloy_pipeline with recipe blackbox-exporter + params { targets }
• "Kubernetes monitoring" / "Monitor my K8s cluster" → alloy_pipeline with recipe kubernetes-pods + kubernetes-services + kubernetes-logs (3 pipelines)
• "Receive OTLP data" / "Set up trace collection" → alloy_pipeline with recipe otlp-receiver
• "Generate RED metrics from traces" / "Span metrics" → alloy_pipeline with recipe span-metrics
• "Service dependency graph from traces" → alloy_pipeline with recipe service-graph
• "Monitor Alloy itself" / "Self-monitoring" → alloy_pipeline with recipe self-monitoring
• "Redact secrets from logs" / "Compliance logging" → alloy_pipeline with recipe secret-filter-logs + params { paths }
• "Monitor Elasticsearch/Kafka" → alloy_pipeline with recipe elasticsearch-exporter / kafka-exporter
• "System metrics" / "Node monitoring" / "CPU/memory/disk" → alloy_pipeline with recipe node-exporter
• "Docker container metrics" / "Container resource usage" → alloy_pipeline with recipe docker-metrics
• "Reduce trace costs" / "Keep only error traces" / "Smart trace sampling" / "Tail sampling" → alloy_pipeline with recipe application-traces + samplingPolicies array (keep errors, keep slow, filter health checks, sample rest)
• "Multi-tenant Loki" / "Route logs by tenant" / "Different tenants for different apps" → any log recipe + tenantValue or matchRoutes processing param
• "Profile my app" / "CPU profiling" / "Memory profiling" / "Continuous profiling" / "Go pprof" → alloy_pipeline with recipe continuous-profiling + targets
• "Frontend observability" / "Browser RUM" / "Web vitals" / "Faro SDK" → alloy_pipeline with recipe faro-frontend
• "GELF logs" / "Graylog" / "Docker GELF driver" → alloy_pipeline with recipe gelf-logs
• "Custom Alloy pattern" / "Advanced pipeline" → Read references/alloy-components.md → alloy_pipeline with raw config + optional sampleQueries
• "What data collection recipes are available?" → alloy_pipeline with action recipes
• "What pipelines do I have?" / "Pipeline list" → alloy_pipeline with action list
• "Is my pipeline working?" / "Pipeline health" → alloy_pipeline with action status + name
• "Pipeline problems" / "Why isn't data showing up?" → alloy_pipeline with action diagnose → follow remediation
• "Delete pipeline" / "Remove monitoring for..." → alloy_pipeline with action delete + name (confirm with user first)

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
grafana_security_check	Run 6 parallel security checks and return threat-level assessment (green/yellow/red) — "Am I being attacked?"
grafana_investigate	Multi-signal investigation triage — gathers metrics, logs, traces, and context in parallel, generates hypotheses with specific tool+params for follow-up
alloy_pipeline	Create and manage Alloy data collection pipelines — 29 recipes for metrics, logs, traces, profiles from any infrastructure (databases, K8s, Docker, apps, profiling, frontend RUM)

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 openclaw_* metrics). Purpose maps: performance → session + tools, cost → cost + usage, reliability → webhook + messaging + agent, capacity → queue + session. metadataSource: "prometheus" when Prometheus metadata endpoint has data, "synthetic" when OTLP-only (metadata synthesized from known metric registry — histogram sub-metrics deduplicated, type/help from Grafana Lens definitions). On OTLP stacks, includes hint explaining why metadata is synthetic. source: "synthetic" on individual entries from the registry; source: "custom" on entries from the custom metrics store. Returns compact: { metadataSource, categorySummary: {...}, metrics: [{ name, type, category? }, ...] }. Same as metadata but drops help, source, labelNames — use in multi-tool chains where you need metric names and types but not full descriptions. Example label: { "datasourceUid": "prom1", "label": "job" } Returns label: { label, count, totalCount, values: ["value1", "value2", ...] }. 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? } — healthContext is included for well-known openclaw_lens_* gauge metrics, providing SRE-grade health assessment: status ("healthy"/"warning"/"critical"), thresholds (warning/critical values), description (what the metric means), direction ("higher_is_worse"/"lower_is_worse"). Omitted for unknown metrics. Capped at 50 results; when exceeded includes truncated: true, totalResults, and truncationHint advising to narrow the query. Returns range: { series: [{ metric: {...}, values: [{ time, value }...] }], datasourceUid, resultCount, warnings?, hint? } — truncated to 20 points per series and 50 series max. When series are truncated includes truncated: true, totalSeries, and truncationHint. When step is auto-calculated, includes step: { value: "288s", display: "5m", auto: true }. Returns (panel re-run): Includes resolvedFrom: "panel", panelTitle, panelType, templateVarsReplaced alongside normal query results. If the panel uses a Loki datasource, returns an error directing you to use grafana_query_logs instead. Returns (warnings): When Prometheus flags a non-fatal issue (e.g., rate() on a gauge), warnings: [{ cause, suggestion, example? }] is included. Example: rate() 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. Returns (hint): When the query returns zero results, hint: { cause, suggestion } explains why (metric may not exist, label filters may not match) and suggests using grafana_list_metrics to verify. Returns (error with guidance): On query failure, includes guidance: { cause, suggestion, example? } 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. Tip (chaining): Both instant and range responses include datasourceUid — pass it directly to grafana_create_alert or other tools without re-calling grafana_explore_datasources. 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, openclaw_ prefix stripped from field keys, numeric values auto-converted. Also parses JSON log bodies if present. Returns streams (traceCorrelation): When extractFields: true and entries contain trace_id, includes traceCorrelation: { traceIds: [...], tool: "grafana_query_traces", tip } — up to 5 unique trace IDs ready for grafana_query_traces with queryType: "get". Returns metric: Same shape as grafana_query range/instant results (matrix capped at 50 series, vector capped at 50 results — includes datasourceUid, truncated, totalSeries/totalResults, and truncationHint when exceeded). Returns (panel re-run): Includes resolvedFrom: "panel", panelTitle, panelType, templateVarsReplaced alongside normal results. If the panel uses a Prometheus datasource, returns an error directing you to use grafana_query instead. Returns (error with guidance): On query failure, includes guidance: { cause, suggestion, example? } 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. Tip: LogQL: {label="value"} selects streams, |= substring filter, |~ regex, != exclude. Metric wrappers: rate(), count_over_time(), bytes_rate(). Use extractFields: true when investigating OTel/lifecycle logs — it surfaces trace_id, session_id, event_name, model, and other attributes as first-class fields instead of buried in raw labels. Tip (panel re-run): Same as grafana_query — set dashboardUid + panelId to auto-resolve LogQL and datasource. The tool routes Prometheus panels to grafana_query 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, templateVarsReplaced alongside normal results. If the panel uses a Prometheus or Loki datasource, returns an error directing you to use the correct tool. Returns (error with guidance): On query failure, includes guidance: { cause, suggestion, example? } 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. Returns (no results): When search returns zero traces, includes hint: { cause, suggestion } suggesting to broaden the query or check the datasource. Tip: TraceQL: { } matches all traces, resource.service.name for service filter, span.http.status_code for HTTP spans, name for operation name, duration for span duration, status for error/ok filtering. Use minDuration/maxDuration to find performance outliers. Trace-to-Log: search and get results include correlationHint.logQuery — pass it directly to grafana_query_logs to find correlated logs. Log-to-Trace: grafana_query_logs results (with extractFields: true) include traceCorrelation.traceIds — pass any ID to grafana_query_traces with queryType: "get". Tip (panel re-run): Same as grafana_query — set dashboardUid + panelId 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
http-service	—	Web/DevOps	$datasource, $job	HTTP request rate, errors, latency (RED signals)
metric-explorer	—	Any domain	$datasource, $metric	Deep-dive into any single metric from a dropdown
multi-kpi	—	Any domain	$datasource, $metric1..$metric4	4-metric KPI overview (business, fitness, finance, IoT)
weekly-review	—	Any domain	$datasource, $metric1, $metric2	Weekly overview of 2 external metrics with trends + all openclaw_ext_* table

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? } }. Returns queryValidation: For add_panel and update_panel (when targets change), PromQL queries are dry-run against Grafana. Each result: { refId, expr, valid: boolean, error?: string, sampleValue?: number }. Panel is always saved — validation is informational. If valid: false, check the error field for PromQL syntax issues. If skippedReason is set, no datasource UID was found — include datasource: { uid: "..." } on targets to enable validation. Returns delete: { status: "deleted", uid, title, message }. Tip: targets in update_panel replaces entirely — include all targets, not just changed ones. Include datasource.uid 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 — valid: true + sampleValue confirms data exists; valid: false + error warns of typos/missing metrics. Alert is always created regardless (metric may not have data yet). When evaluation is "rate" or "increase", validation runs the wrapped expression. Note: Auto-creates a "Grafana Lens Alerts" folder if no folderUid 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" } Example setup: { "action": "setup" } Returns list: { status: "success", alertCount, alerts: [{ id, status, title, message, receivedAt, commonLabels, totalInstances, truncated?, suggestedInvestigation?: { datasourceUid, condition, tool, queryLanguage, hint }, instances: [{ status, labels, annotations, startsAt, values }] }] }. suggestedInvestigation 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 list_rules + explore_datasources calls). Returns acknowledge: { status: "acknowledged", alertId }. Returns list_rules: { status: "success", ruleCount, rules: [{ uid, title, folder, ruleGroup, state, health, lastEvaluation, for, labels, annotations, condition, updated }] }. state is the live evaluation state: "normal" (not firing), "firing", "pending" (within for duration), "nodata", or "error". Falls back to "unknown" if the eval state API is unavailable. health is "ok", "nodata", "error", or "unknown". condition is the extracted PromQL expression from the rule's data queries. Returns list_rules (compact): { status: "success", ruleCount, rules: [{ uid, title, state, condition }] }. Minimal fields for multi-tool chains — use when you need a quick overview of all rules without details. Returns delete_rule: { status: "deleted", ruleUid, message }. Returns silence: { status: "silenced", silenceId, duration, matchers, message }. Returns unsilence: { status: "unsilenced", silenceId, message }. Returns setup: { status: "created", contactPointUid, webhookUrl } or { status: "already_exists", contactPointUid }. Note: Setup is idempotent — safe to call multiple times. Only alerts with managed_by=openclaw label route to the webhook (auto-added by grafana_create_alert). Use list_rules → delete_rule for full alert lifecycle management (create via grafana_create_alert, list/delete via grafana_check_alerts).

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: { status: "registered", metric: { name, type, help, labelNames, ttlMs }, queryName: "openclaw_ext_events_total", suggestedWorkflow: [{ tool, action, example }] }. suggestedWorkflow shows how to push data and query the registered metric (with rate() wrapping for counters). Returns list: { count, metrics: [{ name, type, queryName, help, labelNames, createdAt, updatedAt }] }. Returns delete: { status: "deleted", name }. Note: Push auto-registers unknown metrics. Response includes queryNames with exact PromQL names and suggestedWorkflow with concrete next steps. Follow suggestedWorkflow to complete the push→visualize pipeline. Timestamped pushes are gauge-only — counters with timestamps are rejected. See external-data.md 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 model, token_type, provider), the response includes suggestedQueries — ready-to-use PromQL queries for grafana_query that break down the metric by each label. Counter metrics get rate() wrapping automatically. Use these to investigate cost attribution, identify top contributors, or decompose aggregates. Breakdowns: suggestedBreakdowns 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 grafana_query to build sum by (label) (...) queries for root-cause analysis. Period comparison: Use compareWith: "previous" for period-over-period analysis (e.g., this week vs. last week). Returns a comparison 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. Tip: For simple trend context, call with just period. For "did things improve?" questions, add compareWith: "previous". Metadata only available for plain metric names (not complex PromQL). No need to manually wrap counters in rate() — 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.

alloy_pipeline

When: Trigger whenever the user mentions data collection, metrics pipeline, log forwarding, trace collection, infrastructure monitoring, database monitoring, observability setup, scraping endpoints, collecting Docker/container logs, Kubernetes monitoring, syslog, or any mention of getting data into Grafana. Also trigger for managing existing pipelines (status, health, diagnostics). 26 recipes covering metrics (11), logs (8), traces (4), profiles (1), and infrastructure (3). This is the bridge between "I have a data source" and "I can see it in Grafana" — if the user has data somewhere and wants it in Grafana, this tool is the answer. Actions: create (deploy pipeline from recipe), list (show managed pipelines), update (change params), delete (remove pipeline), recipes (browse catalog), status (check health), diagnose (Alloy connectivity + all pipelines). Params (create with recipe): recipe (required), params (recipe-specific), name (optional — auto-generated from recipe name with numeric suffix for uniqueness, e.g., syslog, syslog-2, syslog-3; always read the name field from the response to get the actual assigned name). Log recipes also accept optional processing params: jsonExpressions (object — JSON path extractions), labelFields (object — promote fields to labels), structuredMetadata (object — Loki structured metadata), staticLabels (object), timestampSource (string), outputSource (string), regexExpression (string). Params (create with raw config): config (raw Alloy River syntax), name (required), signal (optional — "metrics", "logs", "traces", or "profiles", auto-detected from component IDs if omitted), sampleQueries (optional — object with metrics/logs/traces keys containing ready-to-use queries). Response includes exportTargets showing where data is being sent. Params (update): name (required), params (fields to change — merges with existing). Raw-config pipelines support update via full config replacement — pass config param with the complete new config. Recipe-based pipelines use params for partial updates (merges with existing). Response includes sampleQueries and suggestedWorkflow for chaining into query/dashboard tools. Params (status): name (required). Params (recipes): category (optional filter). Example create (scrape): { "recipe": "scrape-endpoint", "params": { "url": "http://myapp:8080/metrics" } } Example create (database): { "recipe": "postgres-exporter", "params": { "connectionString": "postgres://user:pass@db:5432/mydb" }, "name": "analytics-db" } Example create (logs): { "recipe": "docker-logs", "params": { "containerNames": ["myapp", "nginx"] } } Example create (files): { "recipe": "file-logs", "params": { "paths": ["/var/log/app/*.log"] } } Example create (K8s): { "recipe": "kubernetes-pods", "params": { "namespaces": ["production"] } } Example create (OTLP): { "recipe": "otlp-receiver" } Example create (traces with sampling): { "recipe": "application-traces", "params": { "environment": "staging", "sampleRate": 0.5 } } Example create (syslog UDP): { "recipe": "syslog", "params": { "protocol": "udp", "listenAddress": "0.0.0.0:5514" } } Example create (raw config): { "config": "prometheus.scrape ...", "name": "custom-pipeline", "signal": "metrics" } Example recipes: { "action": "recipes", "category": "logs" } Example status: { "action": "status", "name": "analytics-db" } Example diagnose: { "action": "diagnose" } Returns (create): { status, name, recipe, signal, configFile, reloaded, envVarsRequired, warnings, sampleQueries: { metrics|logs|traces: {...} }, suggestedWorkflow }. status is "created" (active) or "pending_credentials" (env vars not set yet). sampleQueries are ready-to-use PromQL/LogQL/TraceQL — pass directly to query tools. envVarsRequired lists env vars for credential-bearing recipes. warnings contains advisory notes about unrecognized params. Raw-config pipelines may also include credentialWarnings if plaintext credentials were detected. Returns (recipes): { categories, recipes: [{ name, category, signal, summary, requiredParams, optionalParams, hasCredentials, dashboardTemplate }] }. Use summary to match user intent. Returns (status): { name, status, components: [{ id, health, detail }], dataVerification: { verifyQuery, tool }, remediation }. Pending pipelines auto-promote to active when components become healthy. Returns (diagnose): { alloyConnectivity, pipelineHealth, driftDetected, orphanFiles, limits }. Tip (credential lifecycle): Credential recipes (postgres-exporter, mysql-exporter, mongodb-exporter, redis-exporter, scrape-endpoint with auth, kafka-logs) may return pending_credentials status when env vars aren't set. Config stays on disk — tell user the exact env var names from envVarsRequired, then verify with action status. Pipeline auto-activates once Alloy can connect. Tip (chaining): sampleQueries → grafana_query/grafana_query_logs. dashboardTemplate → grafana_create_dashboard. Zero-friction pipeline→query→dashboard→alert chains. Tip (multi-pipeline): For K8s: kubernetes-pods + kubernetes-logs + otlp-receiver (3 separate creates). Tip (raw config escape hatch): Use config param for patterns not covered by recipes. Signal is auto-detected from component IDs (or set signal explicitly). Add sampleQueries so chaining tools know what to query. Component IDs are auto-extracted for health checking. Supports update via full config replacement. See references/alloy-components.md for copy-pasteable component snippets. Tip (port conflicts): Trace recipes (otlp-receiver, application-traces, span-metrics, service-graph) default to ports 4317/4318 — creating multiple on the same ports will fail. Set grpcPort/httpPort params to different values for each. Tip (cross-pipeline isolation): Pipelines are self-contained — each .alloy file has no cross-file references. For combined patterns (e.g., OTLP receiver + span-metrics + service-graph in one config), use raw config param with the full combined pipeline. You cannot wire one pipeline's output into another pipeline.

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