Get project timeline events as context for interpreting Visibility trends. Sources are external, team, deterministic, or detected. Detected facts use citation_share_change and return stable aggregate evidence { platform, domain, direction, currentShare, baselineShare, absoluteChange, relativeChange, observationStart, observationEnd, projectCount, projectFraction } inside detectedEvidence; relativeChange is null when the domain has no baseline citations. Project detected evidence uses the same platform, domain, direction, shares and observation dates, plus { scope: "project", domainRole, brandName, incidentId, baselineStart, baselineEnd, cohortPromptCount, activePromptCount, status, resolvedAt }, without projectCount or projectFraction. domainRole is own, competitor or other; brandName is null for other. Project incidents must meet their detector configuration absolute-share floor, currently 0.01 (one percentage point). Shares are fractions. Status is active or resolved; resolvedAt is null while active. High-confidence active incidents and movement-ended history surface automatically, unless an overlapping pending or active citation exclusion hides them. External facts use platform_model_change, platform_outage, data_collection_gap, search_algorithm_update, or devtune_methodology_change with evidence { title, description, sourceUrl }. Prompt additions, edits (prompts_edited), switches off (prompts_paused) and on (prompts_resumed), removals, and restores return summary counts and an optional shared topic, never prompt names; edits and switches are one event per kind per day, counting each prompt once. Tracked-URL changes use kind tracked_urls_changed, one per day, with evidence { changeCount, brands, brandCount, requestedBy, requestedByCredential }: the number of tracked URL, brand or classification-rule changes that day and up to five affected brand names. requestedByCredential (the API key reference and name, or MCP connection reference, when every change has the same credential) identifies the shared credential. Tracking changes apply to citations collected from that day on; earlier citations keep the labels they were recorded with. Measured-action facts use source measured_action and kinds change_detected, change_claimed, measurement_opened and outcome_measured. Their measuredAction evidence includes actionId and actionTitle when attributed. Agent Picks events require agent_picks and include structured agentPicks rationale for localized titles, with reason and cells for repeated reasons. It also includes nullable interventionId and url, changeEventId for changes and results, measurementOpenedAt for openings and results, and windowClosedAt, result and method (v1 or v2) for results only; an Agent Picks result adds agentPicksMethod, v2 (decided by the 90% interval on the difference from its baseline) or v1 (by 95% ranges), so say which when comparing results. Results are improved, mixed, no_clear_change, declined, still_measuring or unmeasured; an outcome_measured event requires the canonical measured_at timestamp from Results. These facts ignore topic filters and have no platform keys. Detected AI Traffic changes use source detected, kind ai_traffic_change and evidence trafficChange: { incidentId, platform, classGroup, direction, observedEvents, baselineEvents, expectedEvents, relativeChange, observationStart, observationEnd, baselineStart, baselineEnd, status, resolvedAt }. platform is the AI Traffic provider key (for example openai or anthropic); classGroup is answer_fetches or crawls (training and index crawlers). observedEvents counts the observation window, baselineEvents the baseline window, and expectedEvents is the baseline scaled to the observation window length; relativeChange is null when the baseline has no events. They require the AI Traffic feature, ignore topic filters and have no platform keys. Only high-confidence changes measured with complete sensor coverage surface; a sensor outage is never reported as a drop. No causal link to metric movements is implied.