Trillboards DOOH Advertising MCP

Import an AI model’s image through native bytes, moderation and brand review. Provide exactly one of asset_url, source_creative_id or image_base64. Inline bytes require content_type. PNG, JPEG and WebP are staged through native ownership, actual MIME, byte-hash, brand review and moderation. The existing data_api/api_calls tariff applies.

Takes no parameters.

Configure your native brand profile and enable its booked reactive campaign. Uses your authenticated native buyer and existing funded placement.

Create an experiment on a funded native media-buy placement. Uses your authenticated native buyer and existing funded placement.

Read your native experiment, policy and latest 500 owned creative recovery records. Uses your authenticated native buyer and existing funded placement.

Register approved native generated creative variants. Uses your authenticated native buyer and existing funded placement.

Replace native creative weights at the expected policy version. Uses your authenticated native buyer and existing funded placement.

Read a metered page of latest measured native play states. Read actual latest native plays with NULL preserved. Dates are YYYY-MM-DD, max 31 days. Poll with the returned cursor; canonical_key plus reward_revision_id identify logical changes; build_stamp is provenance. Each page uses the published data_api/api_calls tariff.

Stop the native experiment at the expected policy version. Uses your authenticated native buyer and existing funded placement.

Generate a native brand creative for explicit review and moderation. The native ad and advertiser come from the experiment. Review and AI moderation are separate. The existing data_api/api_calls tariff applies.

Approve or reject your generated creative using native brand review. Uses your authenticated native buyer and existing funded placement.

Register a new partner organization with Trillboards. WHEN TO USE: - First-time setup for a new partner integration - Creating a new partner account to manage devices RETURNS: - partner_id: Unique partner identifier - api_key: API key for authenticated requests (store securely!) - status: Account status EXAMPLE: User: "Register my vending machine company" register_partner({ company_name: "Acm

Get information about the authenticated partner account. WHEN TO USE: - Checking current partner status and stats - Verifying API key is working - Getting partner account details RETURNS: - partner_id: Partner identifier - company_name: Registered company name - status: Account status (active, suspended, etc.) - device_count: Number of registered devices - total_impressions: Lifetime impression

Takes no parameters.

Register or update a device in the partner's network. WHEN TO USE: - Adding a new screen/kiosk/vending machine to the network - Updating device location or configuration - Re-registering a device after maintenance RETURNS: - device_id: Your internal device ID (echoed back) - trillboards_device_id: Internal Trillboards device ID - fingerprint: Device fingerprint (e.g., "P_abc123") - embed_url: UR

List all devices registered to the partner account. WHEN TO USE: - Getting an overview of all connected devices - Finding devices by status (online/offline) - Auditing the device fleet RETURNS: - devices: Array of device objects - total: Total device count - online_count: Number of online devices - offline_count: Number of offline devices EXAMPLE: User: "Show me all my online devices" list_devi

Get detailed information about a specific device. WHEN TO USE: - Checking status of a single device - Getting device configuration details - Debugging device issues RETURNS: - device_id: Your internal device ID - trillboards_device_id: Internal Trillboards ID - fingerprint: Device fingerprint - name: Device name - status: online/offline - last_seen: Last heartbeat timestamp - location: Location

Soft-delete a device from the partner account. WHEN TO USE: - Removing a device that's been decommissioned - Cleaning up test devices - Removing a device that's been relocated to another partner RETURNS: - success: Boolean indicating success - device_id: The deleted device ID - message: Confirmation message EXAMPLE: User: "Remove the old lobby kiosk" delete_device({ device_id: "lobby-kiosk-ol

Get current ads scheduled for a device (for testing). WHEN TO USE: - Testing device ad delivery - Debugging which ads are being shown - Verifying ad targeting is working RETURNS: - ads: Array of advertisement objects - default_stream: Default content when no ads - schedule: Current ad schedule EXAMPLE: User: "What ads are showing on device P_abc123?" get_device_ads({ fingerprint: "P_abc123" }

Record a single ad impression from a device. WHEN TO USE: - Reporting that an ad was displayed on a device - Recording impression with detailed metadata - Single impression events (for batch, use batch_impressions) RETURNS: - success: Boolean indicating success - impression_id: Unique impression identifier - earnings: Earnings credited for this impression EXAMPLE: User: "Record an impression fo

Record multiple impressions in a single request (up to 100). WHEN TO USE: - Bulk reporting impressions from offline period - Efficient batch processing of impressions - When device was offline and needs to sync RETURNS: - success: Boolean indicating success - processed: Number of impressions processed - failed: Number of failed impressions - total_earnings: Total earnings credited - errors: Any

Send a heartbeat signal from a device to report its status. WHEN TO USE: - Regular device health monitoring (every 30-60 seconds) - Reporting current playback status - Reporting errors or issues RETURNS: - success: Boolean indicating success - device_status: Current device status in system - next_heartbeat_seconds: Recommended interval for next heartbeat EXAMPLE: User: "Send heartbeat for devic

Get analytics data for the partner account. WHEN TO USE: - Viewing overall performance metrics - Analyzing device performance - Generating reports on impressions and earnings - Comparing performance over time periods RETURNS: - summary: Overall stats (impressions, earnings, active_devices) - time_series: Data points over time - top_devices: Best performing devices - breakdown: Data grouped by re

Create a new webhook subscription for real-time events. WHEN TO USE: - Setting up real-time notifications for device events - Integrating with external systems - Monitoring ad playback and impressions AVAILABLE EVENTS: - device.online: When a device comes online - device.offline: When a device goes offline - impression.recorded: When an impression is logged - campaign.allocated: When a campaign

List all webhook subscriptions for the partner account. WHEN TO USE: - Viewing all configured webhooks - Auditing webhook subscriptions - Finding a webhook to update or delete RETURNS: - webhooks: Array of webhook objects with: - webhook_id: Unique identifier - url: Endpoint URL - events: Subscribed events - enabled: Whether webhook is active - created_at: Creation timestamp - last_d

Takes no parameters.

Update an existing webhook subscription. WHEN TO USE: - Changing the webhook endpoint URL - Adding or removing subscribed events - Enabling or disabling a webhook - Updating the webhook description RETURNS: - webhook_id: The updated webhook ID - url: Updated endpoint URL - events: Updated event subscriptions - enabled: Updated enabled status - updated_at: Update timestamp EXAMPLE: User: "Disabl

Takes no parameters.

Delete a webhook subscription. WHEN TO USE: - Removing a webhook that's no longer needed - Cleaning up old integrations - Removing test webhooks RETURNS: - success: Boolean indicating success - webhook_id: The deleted webhook ID - message: Confirmation message EXAMPLE: User: "Delete the old webhook" delete_webhook({ webhook_id: "wh_mmmpdbvj_8b7c5a59296d" })

Send a test event to a webhook endpoint. WHEN TO USE: - Verifying webhook endpoint is working - Testing integration during development - Debugging webhook delivery issues RETURNS: - success: Boolean indicating delivery success - response_code: HTTP response code from endpoint - response_time_ms: Response time in milliseconds - error: Error message if delivery failed EXAMPLE: User: "Test my webh

Get delivery history for a webhook. WHEN TO USE: - Debugging failed webhook deliveries - Auditing webhook activity - Checking delivery success rates RETURNS: - deliveries: Array of delivery records with: - delivery_id: Unique delivery ID - event: Event type - status: success/failed - response_code: HTTP response code - response_time_ms: Response time - attempted_at: Attempt timestamp

Discover available DOOH screens across the exchange network. WHEN TO USE: - Finding screens by venue type (retail, transit, office, etc.) - Finding screens in a specific city/state or within a radius - Finding screens with a specific audience profile (high income, professionals, etc.) - Getting an overview of available inventory with live audience data RETURNS: - screens: Array of screen objects

Get real-time audience data for a specific screen. WHEN TO USE: - Checking current audience at a screen before buying - Monitoring audience during a live campaign - Getting detailed audience signals (attention, mood, purchase intent, demographics) RETURNS real-time data from edge AI sensors (refreshed every 10 seconds): - face_count: Number of people currently viewing - attention_score: How atte

Predict what the audience will look like at a screen at a specific time. WHEN TO USE: - Planning campaigns for specific time slots - Estimating audience composition before buying - Comparing audience at different times of day Uses historical audience data to predict typical audience patterns. RETURNS: - predicted_face_count: Expected number of viewers - predicted_attention: Expected attention s

Create a new advertising campaign targeting DOOH screens. WHEN TO USE: - Setting up a new ad campaign on available screens - Targeting specific venues, locations, or audience profiles - Allocating budget for programmatic DOOH buys RETURNS: - campaign_id: Unique campaign identifier (UUID) - name, status, budget, screen_count, dates Campaign starts in "draft" status. Use update_campaign to set st

Get detailed performance metrics for a campaign. WHEN TO USE: - Monitoring active campaign performance - Reviewing completed campaign results - Getting per-screen impression breakdowns RETURNS: - campaign_id, name, status, budget, dates - performance: impressions, spend_estimate_usd, avg_cpm, unique_screens, avg_latency_ms - screen_breakdown: per-screen impressions and CPM EXAMPLE: User: "How i

Get network-wide statistics across all partner screens. WHEN TO USE: - Getting a high-level overview of network performance - Checking how many screens are online - Reviewing total impressions and revenue estimates RETURNS: - total_screens, online_screens - impressions, total_auctions - revenue_estimate_usd, avg_cpm, fill_rate EXAMPLE: User: "How is my network performing this week?" get_network

[AdCP Signals] Get real-time audience signals from DOOH screens. This is an AdCP (Ad Context Protocol) compliant tool. It returns deterministic audience signals captured by edge AI (vision + audio + speech) on available screens. WHEN TO USE: - Discovering available audience signals before buying inventory - Evaluating audience composition at specific venues or locations - Building targeting segm

[AdCP Signals] Activate an audience signal for DSP targeting. Returns an activation_key token for referencing this signal activation. Free-form Trillboards signal labels remain custom parameters. IAB Audience Taxonomy 1.1 segments are emitted only when registered IDs are supplied explicitly. WHEN TO USE: - Converting audience signals into actionable targeting parameters - Activating already-cura

[AdCP] Get the seller agent's AdCP capabilities and supported protocols. Returns the full capability declaration for this AdCP DOOH seller agent. This tool does NOT require authentication. WHEN TO USE: - Discovering what protocols the seller agent supports (Signals, Media Buy) - Understanding available audience signals and data methodology - Getting MCP endpoint and discovery URLs RETURNS: - su

Takes no parameters.

[AdCP Media Buy] Get available DOOH advertising products and packages. NO AUTHENTICATION REQUIRED. Discovery is open — read the catalogue first, get a key when you want to transact. Send a natural-language brief and it is answered from what the screens actually observed: each product's description reports the hours people are really in frame (in the screens' own local time), how long they dw

[AdCP Creative] List the creatives this buyer has on file with us. OUR LIBRARY IS PER-BUY, AND THIS SAYS SO. AdCP's creative library models concepts, variables, assignments and snapshots; ours does not have those. A creative here is the one asset attached to a media buy by sync_creatives, so this is a projection of YOUR OWN buys — never someone else's assets, and never an invented concept_id to l

[AdCP Media Buy] List the creative formats this network actually accepts. Every format is DERIVED from live per-screen capability (panel size, min/max spot length, audio) — not a hand-written list. The set published here is exactly the set sync_creatives accepts: if a creative matches a format returned by this tool, it will not be rejected for dimensions, duration or file size. WHEN TO USE: - Be

[AdCP Media Buy] List media buys with status, budget, flight and optional delivery snapshots. Status, budget and flight are read from the advertisements + placements spine the buy actually books on — not from a stored display string. A buy that its flight ended, or that the pacing cron completed on goal, reports the truth here even though nothing rewrote it. WHEN TO USE: - Polling the buys you h

[AdCP Media Buy] Create a media buy (campaign) from an AdCP buy specification. Creates a campaign that targets DOOH screens based on the provided specification. Returns a media_buy_id for tracking and a creative_deadline for asset submission. WHEN TO USE: - Executing a programmatic DOOH buy via an AI agent - Creating campaigns from DSP trading desk agents - Automated media buying workflows RETU

[AdCP Media Buy] Update an existing media buy (campaign). Modify budget, targeting, schedule, or status of an existing media buy. WHEN TO USE: - Adjusting campaign budget mid-flight - Pausing or resuming a campaign - Changing targeting parameters - Extending campaign dates EXAMPLE: update_media_buy({ media_buy_id: "mbuy_abc123", updates: { status: "paused", budget: { daily_usd: 300 } } })

[AdCP Media Buy] Validate and sync creative assets for a media buy. Validates creative assets (resolution, duration, format) against screen specifications. Returns compatibility status for each screen in the campaign. WHEN TO USE: - Submitting creative assets before campaign launch - Checking if a creative meets screen requirements - Validating VAST tags EXAMPLE: sync_creatives({ media_buy_id

[AdCP Media Buy] Get delivery/performance report for a media buy. Returns campaign performance with breakdowns by screen, venue, hour, and audience segment. WHEN TO USE: - Monitoring campaign delivery in real-time - Getting performance breakdowns for optimization - Reporting on campaign results RETURNS: - delivery: impressions, spend, avg_cpm, unique_screens, fill_rate - breakdowns: by_screen,

[AdCP Media Buy] Provide optimization signals from buyer agent. Accepts feedback from buyer agents for floor price adjustment and inventory optimization. Enables closed-loop optimization between buyer and seller agents. WHEN TO USE: - Sending bid response feedback to optimize future pricing - Providing conversion data for bid price calibration - Adjusting floor prices based on demand signals EX

[AdCP Media Buy] Record a conversion or attribution event. Records conversion events for post-campaign attribution analysis. Events are deduplicated by event_id + event_type combination. WHEN TO USE: - Recording offline conversions (store visits, purchases) - Tracking post-view attribution events - Logging custom KPI events EXAMPLE: log_event({ media_buy_id: "mbuy_abc123", event: { even

[AdCP Accounts] List the accounts this credential can transact on. This seller's account model is 'explicit': one API key IS one account, so this returns exactly one account — the partner behind the key. Use it to discover your account_id before any account-scoped call, and to confirm the account's status before you buy. WHEN TO USE: - Discovering the account_id to pass to account-scoped tasks -

[AdCP Accounts] Establish or confirm the account behind this credential. IMPORTANT — what this does NOT do: it does not provision a new account. This seller's namespace is one account per API key, so a provisioning-mode entry (brand + operator + billing) is LINKED to the account your key already owns and the response says so in warnings[]. Two different brands on one key resolve to the SAME accou

[AdCP Protocol] Get the status of a previously issued AdCP task. Every AdCP task Trillboards serves for an AUTHENTICATED caller is recorded and returned a task_id. Poll that id here to read the task's terminal state and, with include_result: true, its completion payload. Trillboards answers every AdCP task in-process, so a task is already completed by the time you hold its id — this tool e

[AdCP Protocol] Get the status of a previously issued AdCP task. Every AdCP task Trillboards serves for an AUTHENTICATED caller is recorded and returned a task_id. Poll that id here to read the task's terminal state and, with include_result: true, its completion payload. Trillboards answers every AdCP task in-process, so a task is already completed by the time you hold its id — this tool e

[AdCP Protocol] List AdCP tasks belonging to your account, newest first. Returns query_summary (totals and a status breakdown), tasks, and pagination. Filter by status or task type. Scoped to the calling account — an unauthenticated call returns an empty page rather than another account's tasks.

[AdCP Protocol] List AdCP tasks belonging to your account, newest first. Returns query_summary (totals and a status breakdown), tasks, and pagination. Filter by status or task type. Scoped to the calling account — an unauthenticated call returns an empty page rather than another account's tasks. LEGACY NAME. Identical to list_tasks. Prefer list_tasks in new code.

Get machine-readable pricing for all Trillboards products. Returns graduated usage-based pricing, free tier thresholds, and committed-use discount tiers. No authentication required — use this to evaluate costs before integrating.

Get your current billing period usage summary with per-product breakdown and costs. Shows free tier consumption, paid usage, and total cost.

Takes no parameters.

Set up pay-per-use billing with a Stripe payment method. A stored card grants no API credit; fund usage with credits or explicitly authorized API postpay allowance. Pass a Stripe payment method token (pm_xxx) obtained from Stripe.js or Stripe Elements.

Purchase committed-use credits at a discount. Three tiers: tier_500 ($500 → $625 credit, 25% bonus), tier_2000 ($2,000 → $3,100 credit, 55% bonus), tier_5000 ($5,000 → $10,000 credit, 100% bonus). Requires an active payment method.

Check current billing status including whether billing is set up, credit balance, Stripe customer ID, and payment method status. Use this to determine if billing setup is needed before making paid API calls.

Takes no parameters.

Get comprehensive attribution summary for a DOOH campaign. WHEN TO USE: - Measuring overall campaign effectiveness (reach, footfall, sales lift) - Getting a high-level view of campaign attribution metrics - Checking statistical significance of attribution results RETURNS: - reach: uniqueDevices, totalImpressions, avgFrequency - footfall: exposedVisitors, controlVisitors, incrementalLiftPct, incr

Get daily attribution timeseries for a campaign. WHEN TO USE: - Tracking attribution trends over time - Identifying which days had the strongest lift - Building attribution dashboards with daily granularity RETURNS: Array of daily data points, each with: - date, uniqueDevices, totalExposures, avgFrequency - exposedVisitors, controlVisitors, liftPct, incrementalVisits - costPerVisit, totalMediaCo

Get edge AI attention metrics for a campaign (FEIN-powered). This is what makes DOOH attribution better than digital: Trillboards MEASURES viewability via FEIN edge AI instead of estimating it. WHEN TO USE: - Measuring actual human attention to ads (not just impressions) - Comparing attention-adjusted CPM (aCPM) vs standard CPM - Getting face count, dwell time, and emotion engagement data RETUR

Get per-creative attention breakdown for a campaign. WHEN TO USE: - A/B testing creative variants by attention score - Identifying which creative drives the most engagement - Comparing aCPM across creative assets RETURNS: Array of creatives ranked by attention score, each with: - creativeId, totalImpressions, uniqueDevices - avgAttentionScore (0-1), avgDwellSeconds, avgFaceCount - attentionCpm,

Get multi-touch attribution model results for a campaign. Supported models: time_decay, position_based, attention_weighted. WHEN TO USE: - Understanding how DOOH fits into the full marketing funnel - Seeing credit allocation across DOOH, mobile, web, and store channels - Quantifying DOOH's contribution to conversions RETURNS: - totalChains: number of multi-touch journeys found - avgTouchpoints:

Get cross-channel customer journey data (Sankey flow) for a campaign. Shows how users flow between channels: DOOH -> mobile -> web -> store. WHEN TO USE: - Visualizing the customer journey across DOOH and digital channels - Understanding channel transition patterns - Building Sankey diagrams of marketing funnels RETURNS: - flows: Array of { source, target, count } transitions between channels -

Get incrementality/lift test results for a campaign. Uses Bayesian (Beta-Binomial with 10K Monte Carlo samples) and frequentist (chi-square with Yates correction) methods for causal measurement. WHEN TO USE: - Proving causal DOOH advertising effectiveness - Getting both Bayesian and frequentist significance measures - Seeing treatment vs control group visit rates and lift RETURNS: Array of expe

Create an incrementality experiment for a campaign. Sets up a geo-holdout, ghost ads, or propensity score matching experiment to causally measure DOOH advertising lift. WHEN TO USE: - Setting up a new A/B test before or during a campaign - Defining treatment and control DMAs for geo-holdout tests - Configuring experiment parameters (holdout %, MDE, power) RETURNS: The created experiment object

Get attribution performance by individual creative variant. Links creative execution to attribution outcomes: which creative variant drove the most store visits? WHEN TO USE: - Comparing creative A/B/C test performance on attribution outcomes - Finding the optimal creative x venue_type x daypart x weather combination - Identifying the creative with the highest visit rate RETURNS: Array of creat

Get Return on Ad Spend (ROAS) with transaction attribution data. Closes the ROAS loop: matches purchase events to DOOH exposures with time-decay weighting, and computes attributed revenue and incremental ROAS. WHEN TO USE: - Measuring revenue directly attributable to DOOH advertising - Getting ROAS and incremental ROAS (iROAS) figures - Seeing sales lift between exposed and control groups RETUR

Export exposed audience cohort to a DSP for retargeting. Pushes MAID hashes from the campaign's exposed cohort to the specified DSP (The Trade Desk, DV360, or Meta). Creates or reuses a DSP segment. WHEN TO USE: - Activating DOOH-exposed audiences for retargeting on digital channels - Pushing cohorts to TTD, DV360, or Meta Custom Audiences - Measuring cross-channel retargeting lift RETURNS: - s

Find screens with similar audience profiles using pgvector similarity. Uses 64-dimensional audience vectors with HNSW cosine similarity index to find screens whose audience demographics, attention, and behavioral patterns match a target screen. WHEN TO USE: - Expanding campaign reach to screens with similar audiences - Finding new inventory that matches a high-performing screen - Building lookal

Search screens by natural language scene description using pgvector. Uses 768-dimensional Gemini embeddings on scene descriptions from FEIN edge AI to find screens matching a natural language query. WHEN TO USE: - Finding screens by audience context ("families eating lunch in a food court") - Contextual ad placement based on real-time scene understanding - Discovering inventory matching a specif

Get geographic exposure heatmap data for a campaign. Returns lat/lng clusters with exposure counts and device reach, useful for visualizing where ads were shown on a map. WHEN TO USE: - Visualizing campaign geographic coverage - Identifying hotspots of ad exposure - Analyzing geographic distribution of attributed foot traffic RETURNS: Array of geographic clusters (max 500), each with: - lat, ln

Verify cryptographic proof of ad delivery or get campaign proofs. Requires either campaign_id or proof_payload (at least one must be provided). Two modes: 1. Verify a proof: pass proof_payload with signature fields to verify 2. Get proofs: pass campaign_id to get Ed25519-signed proofs for a campaign Uses Ed25519 signatures (v2) that can be independently verified by third parties using the Trill

Query the universal observation stream using natural language or structured filters. Returns multi-modal sensing data (audience, vehicle, environment, commerce) from physical-world observations across the screen network. WHEN TO USE: - Exploring raw observation data from edge AI sensors on screens - Filtering observations by venue type, device, time range, or geography - Getting audience, vehicle

Search observations by semantic similarity. Find moments that match a description like "lunch rush at fast casual restaurants" using vector embeddings. Uses 768-dimensional Gemini embeddings on observation payloads to find promoted observations matching a natural language query via approximate nearest-neighbour (ANN) cosine similarity search over a Lance IVF_PQ index. CONSISTENCY: results are AP

Detect anomalies in observation patterns. Alert when metrics deviate significantly from trailing averages. Computes trailing mean and standard deviation for a given metric from the observation_stream, then identifies observations that fall beyond the configured sigma threshold (z-score based anomaly detection). WHEN TO USE: - Monitoring for unusual audience patterns (sudden spikes or drops in fa

Discover correlations between different signal types. Example: relationship between ad fill rate and audience attention for QSR venues. Queries the cross_signal_insights table for pre-computed correlations, or computes ad-hoc correlations from the observation_stream when no pre-computed insight exists. WHEN TO USE: - Understanding relationships between different sensing signals - Finding which a

Configure what a screen should sense using natural language. Generates and optionally pushes a sensing profile to the device. Uses Gemini AI to interpret a natural language sensing intent and generate a sensing profile that maps to available on-device ML models (BlazeFace, AgeGender, FER+, MoveNet, YAMNet, WhisperTiny, EfficientDet, YOLOv8-nano). WHEN TO USE: - Setting up a new screen to sense s

Export observation data as a structured dataset. Supports filtering by time, geography, venue type, and observation family. Queries the relevant table based on the selected dataset type, applies filters, and returns every matching row as structured data, a page at a time: up to 10,000 observation rows or 1,000 cross-signal insights per call, newest first. When more rows match, metadata.truncated

Generate predictive insights from observation patterns. Predict whether a venue is likely to see increased foot traffic based on current patterns. Uses historical observation_stream data to compute trend analysis via linear regression on time-bucketed metrics. Generates predictions with confidence intervals based on the observed trend, variance, and sample size. WHEN TO USE: - Predicting future

Query measured social attention events within your authorized screen and venue scope. Filter by minimum social amplification factor and time range; results are sorted by social amplification factor descending. WHEN TO USE: - Finding moments where social proof drove collective engagement - Identifying which venues or dayparts exhibit highest attention contagion - Understanding cascading attention

Find historically similar audience moments across the screen network using embedding similarity search. Input a natural-language description of the target moment. Moment embeddings are 768-D vectors generated from multi-modal observation data (visual, audio, environmental, social) via the MomentEmbeddingService. This tool embeds your query text and finds the closest real-world moments via approxi

Predict the VAS (Viewability Attention Score) a specific creative would achieve at a given moment, based on historical data and causal modeling. Uses the CausalPredictionService which: 1. Embeds the moment description to find historically similar moments 2. If >= 5 similar moments exist with the same creative, uses weighted-average prediction 3. If insufficient data, falls back to Gemini generati

Given a moment description, rank candidate creatives by predicted VAS performance. Evaluates each creative candidate against the described moment context using historical similarity and causal prediction. Returns a ranked list sorted by predicted VAS score, with confidence levels for each prediction. WHEN TO USE: - Choosing which creative to show at a specific moment/venue - Comparing multiple c

Aggregate social attention metrics across screens and time periods. Shows which venues and dayparts have the highest social amplification. Summarizes measured social attention events within your authorized scope by the requested dimension (venue, daypart, or screen), computing average SAF, average cascade depth, average viral attention score, and event count. WHEN TO USE: - Understanding which v

Get statistics about available causal training data: total tuples, unique creatives, venue diversity, date range. Queries observation_stream for rows that have both a creative ID and a VAS outcome recorded, giving a picture of how much training data is available for the causal prediction engine. WHEN TO USE: - Checking if enough data exists for reliable causal predictions - Understanding the div

Takes no parameters.

Get performance metrics for a video across the Trillboards DOOH network. WHEN TO USE: - Checking how a specific video performs across screens (plays, attention, audience size) - Analyzing which venue types and dayparts a video resonates best in - Finding the top-performing screens for a piece of content - Comparing content performance over different time windows RETURNS: - videoId, title, totalP

Semantic search over content library using natural language queries and 768-D pgvector embeddings. WHEN TO USE: - Finding content by description or theme ("upbeat music videos", "cooking shows") - Discovering content similar to a concept or mood - Searching the content library without knowing exact titles or IDs - Content discovery for programmatic content scheduling RETURNS: - data: Array of ma

Get best-performing content recommendations for a venue type and optional time context. WHEN TO USE: - Deciding what content to schedule at a specific venue type - Finding content that drives the highest audience engagement at a location - Optimizing content rotation by daypart (morning, afternoon, evening, overnight) - Content programming decisions based on performance data RETURNS: - data: Arr

List every registered Trillboards API operation. WHEN TO USE: - First call in an agent session to learn what the API offers. - Filter to agent_safe=true to list only side-effect-free endpoints. - Narrow to a single surface (data-api, sdk-api, device-api, sensing-api, partner-api-generated, dsp-api-generated). RETURNS: - operations: Array of { surface, method, path, operation_id, summary, des

Describe a single API operation including its parameters, response shape, and error codes. WHEN TO USE: - Inspecting an endpoint's full contract before calling it. - Discovering which error codes an endpoint can return and how to recover. RETURNS: - operation: Full discovery record for the endpoint. - parameters: Raw OpenAPI parameter definitions. - request_body: Body schema (when applicable). -

Validate a proposed request payload against the registered Zod schema for an operation, returning the exact canonical error envelope the HTTP surface would emit. WHEN TO USE: - Before calling a write endpoint, to catch payload bugs locally. - Debugging 400 validation_error responses. RETURNS: - valid: true when the payload would pass Zod validation. - When invalid, the canonical { error: { type,

List every error code in the Trillboards API error catalog. WHEN TO USE: - Understanding what error codes the API can return. - Building a client-side error handler that covers all cases. - Looking up error types, HTTP statuses, and documentation URLs. RETURNS: - object: "list" - data: Array of { code, type, http_status, description, doc_url } - total: Total number of error codes. Equivalent to

Takes no parameters.

Query the Trillboards API changelog for recent changes, breaking changes, deprecations, and fixes. WHEN TO USE: - Check what has changed in the API before upgrading an integration. - Find breaking changes since a specific date. - Discover new features added to a specific API surface. PARAMETERS: - since (YYYY-MM-DD, optional): Only entries dated on or after this date. Unreleased entries are al