# Trillboards DOOH Advertising (remote · api.trillboards.com)

DOOH advertising via AI agents. 5,000+ screens with edge AI audience intelligence.

- Trust score: 65/100 (medium)
- Change this week: +6
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `api.trillboards.com`: 65/100 (this document), [markdown](https://verifymcp.io/servers/snehdhruv-trillboards-dooh/api.md), [page](https://verifymcp.io/servers/snehdhruv-trillboards-dooh/api)

## Channel facts

- Endpoint: `https://api.trillboards.com/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.1.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Endpoint Security**: 63/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation not fully verified: no authorisation is required to call this server, and 78 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 71/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 17709 tokens (~221/item across 80 items; 78 tools + 2 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 67/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 0% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add --transport http snehdhruv-trillboards-dooh https://api.trillboards.com/mcp
```

### Codex

```toml
[mcp_servers.snehdhruv-trillboards-dooh]
url = "https://api.trillboards.com/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "snehdhruv-trillboards-dooh": {
      "type": "remote",
      "url": "https://api.trillboards.com/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add snehdhruv-trillboards-dooh --url https://api.trillboards.com/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  snehdhruv-trillboards-dooh:
    url: "https://api.trillboards.com/mcp"
```

### Other

```json
{
  "mcpServers": {
    "snehdhruv-trillboards-dooh": {
      "type": "http",
      "url": "https://api.trillboards.com/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-02 (score 65, +1)

No change was recorded against any check on this day. Stability & Change Management went from 20 to 23. That category is still filling its 30-day observation window: 6 days of observed history at the previous scan, 7 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-31 (score 64, +3)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 61, +2)

- [security] Tool “sync_accounts” rewrote its description, which is the text the model reads

### 2026-07-27 (score 59, 0)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-26 (score 59)

First indexed and scored.

## MCP tools (78)

### `register_partner` (~178 tokens)

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: "Acme Vending Co",
  email: "tech@acmevending.com",
  industry: "vending",
  expected_devices: 50
})

Input parameters:

- `company_name` (string, required)
- `contact_name` (string)
- `contact_phone` (string)
- `email` (string, required)
- `expected_devices` (integer)
- `industry` (string)
- `website` (string)

### `get_partner_info` (~113 tokens)

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 count
\- earnings: Earnings summary

EXAMPLE:
User: "What's my partner account status?"
get_partner_info({})

### `register_device` (~255 tokens)

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: URL to load in the device's WebView
\- status: Device status

EXAMPLE:
User: "Register a vending machine in NYC"
register_device({
  device_id: "vending-001-nyc",
  name: "NYC Office Lobby Vending",
  device_type: "vending_machine",
  location: {
    lat: 40.7128,
    lng: -74.0060,
    city: "New York",
    state: "NY",
    venue_type: "office"
  }
})

Input parameters:

- `device_id` (string, required)
- `device_type` (string)
- `location` (object)
- `metadata` (object)
- `name` (string)
- `specs` (object)

### `list_devices` (~141 tokens)

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_devices({
  status: "online",
  limit: 50
})

Input parameters:

- `device_type` (string)
- `limit` (integer)
- `offset` (integer)
- `status` (string)

### `get_device` (~150 tokens)

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 details
\- specs: Device specifications
\- stats: Impression and earnings stats

EXAMPLE:
User: "Get details for vending machine 001"
get_device({
  device_id: "vending-001-nyc"
})

Input parameters:

- `device_id` (string, required)

### `delete_device` (~108 tokens)

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-old"
})

Input parameters:

- `device_id` (string, required)

### `get_device_ads` (~113 tokens)

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"
})

Input parameters:

- `fingerprint` (string, required)

### `record_impression` (~181 tokens)

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 for ad 507f1f77bcf86cd799439011"
record_impression({
  fingerprint: "P_abc123",
  ad_id: "507f1f77bcf86cd799439011",
  duration_seconds: 15
})

Input parameters:

- `ad_id` (string, required)
- `duration_seconds` (number)
- `fingerprint` (string, required)
- `metadata` (object)
- `timestamp` (string)

### `batch_impressions` (~194 tokens)

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 error details for failed impressions

EXAMPLE:
User: "Sync the last hour of impressions"
batch_impressions({
  impressions: [
    { fingerprint: "P_abc123", ad_id: "507f1f77bcf86cd799439011", duration_seconds: 15 },
    { fingerprint: "P_abc123", ad_id: "507f1f77bcf86cd799439012", duration_seconds: 10 }
  ]
})

Input parameters:

- `impressions` (array, required)

### `device_heartbeat` (~183 tokens)

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 device P_abc123"
device_heartbeat({
  fingerprint: "P_abc123",
  status: "playing",
  current_ad_id: "507f1f77bcf86cd799439011",
  uptime_seconds: 3600
})

Input parameters:

- `current_ad_id` (string)
- `error_message` (string)
- `fingerprint` (string, required)
- `status` (string)
- `uptime_seconds` (integer)

### `get_analytics` (~219 tokens)

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 requested dimension

EXAMPLE:
User: "Show me last week's analytics by device"
get_analytics({
  start_date: "2026-01-01",
  end_date: "2026-01-07",
  group_by: "device"
})

User: "Get monthly performance breakdown"
get_analytics({
  start_date: "2025-12-01",
  end_date: "2025-12-31",
  group_by: "day"
})

Input parameters:

- `device_id` (string)
- `end_date` (string)
- `group_by` (string)
- `start_date` (string)

### `create_webhook` (~301 tokens)

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 is allocated to a device
\- payout.processed: When a payout is processed
\- programmatic.ad_started: When a programmatic ad begins playing
\- programmatic.ad_ended: When a programmatic ad finishes playing
\- programmatic.no_fill: When a programmatic ad request gets no fill
\- programmatic.error: When a programmatic ad request errors

RETURNS:
\- webhook_id: Unique webhook identifier
\- url: The webhook endpoint URL
\- events: Subscribed events
\- secret: HMAC signing secret (if provided)
\- status: enabled/disabled

EXAMPLE:
User: "Set up a webhook for device status changes"
create_webhook({
  url: "https://api.mycompany.com/trillboards/webhooks",
  events: ["device.online", "device.offline", "programmatic.error"],
  secret: "my-signing-secret-123"
})

Input parameters:

- `description` (string)
- `events` (array, required)
- `secret` (string)
- `url` (string, required)

### `list_webhooks` (~124 tokens)

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_delivery: Last successful delivery time

EXAMPLE:
User: "Show me all my webhooks"
list_webhooks({})

### `update_webhook` (~184 tokens)

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: "Disable the webhook for maintenance"
update_webhook({
  webhook_id: "wh_mmmpdbvj_8b7c5a59296d",
  enabled: false
})

User: "Add impression events to my webhook"
update_webhook({
  webhook_id: "wh_mmmpdbvj_8b7c5a59296d",
  events: ["device.online", "device.offline", "impression.recorded"]
})

### `delete_webhook` (~109 tokens)

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"
})

Input parameters:

- `webhook_id` (string, required)

### `test_webhook` (~146 tokens)

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 webhook with a device.online event"
test_webhook({
  webhook_id: "wh_mmmpdbvj_8b7c5a59296d",
  event: "device.online"
})

Input parameters:

- `event` (string)
- `webhook_id` (string, required)

### `get_webhook_deliveries` (~207 tokens)

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
  \- error: Error message (if failed)
\- total: Total delivery count
\- success_rate: Percentage of successful deliveries

EXAMPLE:
User: "Show me failed deliveries for this webhook"
get_webhook_deliveries({
  webhook_id: "wh_mmmpdbvj_8b7c5a59296d",
  status: "failed",
  limit: 20
})

Input parameters:

- `limit` (integer)
- `status` (string)
- `webhook_id` (string, required)

### `discover_inventory` (~252 tokens)

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 with location, venue type, online status, and live audience data
\- total: Total matching screens
\- online_count: Number of currently online screens

Each screen includes real-time audience data when available:
\- face_count, attention_score, income_level, mood, lifestyle
\- purchase_intent, crowd_density, ad_receptivity, dwell_time

EXAMPLE:
User: "Find retail screens in New York with high-income audience"
discover_inventory({
  venue_types: ["retail"],
  location: { city: "New York", state: "NY" },
  audience_profile: { income: "high" },
  limit: 20
})

Input parameters:

- `audience_profile` (object)
- `limit` (integer)
- `location` (object)
- `venue_types` (array)

### `get_live_audience` (~262 tokens)

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 attentively the audience is watching (0-1)
\- income_level: Estimated income bracket (from Gemini Vision)
\- mood: Current audience mood
\- lifestyle: Primary lifestyle segment
\- purchase_intent: Purchase intent level
\- crowd_density: Estimated venue occupancy
\- ad_receptivity: How receptive the audience is to ads (0-1)
\- emotional_engagement: Emotional engagement score (0-1)
\- group_composition: Solo/couples/families/friends/work groups
\- signals_age_ms: How fresh the data is in milliseconds

EXAMPLE:
User: "What's the current audience at screen 507f1f77bcf86cd799439011?"
get_live_audience({ screen_id: "507f1f77bcf86cd799439011" })

Input parameters:

- `screen_id` (string, required)

### `get_audience_forecast` (~234 tokens)

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 score
\- typical_income: Most common income level at that time
\- typical_lifestyle: Most common lifestyle segment at that time
\- confidence: Prediction confidence (0-1, based on sample count)
\- sample_count: Number of historical data points used

EXAMPLE:
User: "What's the typical audience at this screen on Monday at 3pm?"
get_audience_forecast({
  screen_id: "507f1f77bcf86cd799439011",
  hour: 15,
  day: 1,
  lookback_days: 30
})

Input parameters:

- `day` (integer, required)
- `hour` (integer, required)
- `lookback_days` (integer)
- `screen_id` (string, required)

### `create_campaign` (~316 tokens)

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 status to "active".

EXAMPLE:
User: "Create a campaign targeting retail screens in NYC at $5 CPM"
create_campaign({
  name: "NYC Retail Q1",
  budget_cpm: 5.0,
  daily_budget_usd: 100,
  venue_types: ["retail"],
  targeting: { geo: { city: "New York", state: "NY" } },
  creative_url: "https://cdn.example.com/ad.mp4",
  start_date: "2026-03-01",
  end_date: "2026-03-31"
})

Input parameters:

- `budget_cpm` (number)
- `creative_duration` (integer)
- `creative_type` (string)
- `creative_url` (string)
- `daily_budget_usd` (number)
- `end_date` (string)
- `name` (string, required)
- `screen_ids` (array)
- `start_date` (string)
- `targeting` (object)
- `total_budget_usd` (number)
- `venue_types` (array)

### `get_campaign_performance` (~142 tokens)

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 is my NYC retail campaign performing?"
get_campaign_performance({ campaign_id: "550e8400-e29b-41d4-a716-446655440000" })

Input parameters:

- `campaign_id` (string, required)

### `get_network_stats` (~116 tokens)

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_stats({ time_range: "7d" })

Input parameters:

- `time_range` (string)

### `get_signals` (~219 tokens)

[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 segments based on real-time audience data

Unlike probabilistic data, these signals are DETERMINISTIC — captured by
on-device cameras and microphones, analyzed by ML Kit and Gemini Vision.

RETURNS:
\- signals: Array of per-screen signal objects with demographics, venue, behavior, geo
\- metadata: total_screens, matching_screens, screens_with_live_data

EXAMPLE:
User: "What audience signals are available at retail locations?"
get_signals({
  signal_spec: {
    signal_types: ["demographics", "behavior"],
    filters: { venue_type: "retail" }
  }
})

Input parameters:

- `signal_spec` (object)

### `activate_signal` (~244 tokens)

[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-curated, registered IAB segment IDs for programmatic requests
\- Creating reusable targeting configurations

RETURNS:
\- activation_key: Token for referencing this activation (24h expiry)
\- targeting: { iab_segments, iab_taxonomy_version, custom_params }
\- screen_count, provider, data_source, methodology

EXAMPLE:
User: "Activate the registered $100k-$149k household-income segment on my screens"
activate_signal({
  signal_type: "audience",
  parameters: { iab_audience_segment_ids: ["68"] },
  screen_ids: ["507f1f77bcf86cd799439011"]
})

Input parameters:

- `destinations` (array)
- `parameters` (object, required)
- `screen_ids` (array)
- `signal_type` (string, required)

### `get_adcp_capabilities` (~143 tokens)

[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:
\- supported_protocols: ['signals', 'media_buy']
\- inventory: DOOH format details, network size
\- audience_data: signal list, methodology, refresh rate
\- pricing: model, currency, floor CPM
\- discovery: well_known_url, mcp_endpoint

### `get_products` (~343 tokens)

[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 dwell, the mood
/ movement / gaze the on-device sensors reported, what the speech layer heard
people shopping for — and, explicitly, which of your words we cannot evidence.
Products are ordered by that evidence.

WHEN TO USE:
\- Browsing available inventory before creating a campaign
\- Comparing pricing across venue types and locations
\- Understanding what's available in a specific market, at a specific time of day

RETURNS:
\- products: Array of product packages with pricing, reach, and observed audience
\- Each product includes: name, description (free text answering your brief),
  venue_type, screen_count, pricing_options, and `observed` — the numbers
  behind the prose, present only where we measured something
\- brief_interpretation: how we read your brief, so you can see if we read it right

EXAMPLE:
User: "commuters who are bored and hungry around lunchtime"
get_products({
  brief: "commuters who are bored and hungry around lunchtime"
})

Input parameters:

- `audience_profile` (object)
- `brief` (string)
- `buying_mode` (string)
- `filters` (object)
- `market` (string)
- `venue_types` (array)

### `list_creative_formats` (~440 tokens)

[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:
\- Before building creative, to size it to the panels you are buying
\- To check whether an existing asset can run on this network
\- To find the panel sizes with the most reach (results are ordered by live screen count)

RETURNS:
\- formats: AdCP Format objects (format_id, name, renders[].dimensions, assets[].requirements)
\- pagination: cursor-based; total_count is the full catalogue size
\- Each format carries ext.trillboards with the live screen count, the share of the
  network, how many of those screens have audio, and — for video — duration_coverage:
  how many screens accept a spot of at most 10/15/20/30/60/120/300 seconds. A long
  ceiling does not mean every screen at that size can play it, and this says so.

EXAMPLE:
User: "What sizes and lengths does this network take?"
list_creative_formats({ pagination: { max_results: 20 } })

User: "Can I run a 1080x1920 portrait video?"
list_creative_formats({ format_ids: [{ agent_url: "https://api.trillboards.com/mcp", id: "dooh_video_1080x1920" }] })

Input parameters:

- `asset_types` (array)
- `context` (object)
- `format_ids` (array)
- `is_responsive` (boolean)
- `max_height` (number)
- `max_width` (number)
- `min_height` (number)
- `min_width` (number)
- `name_search` (string)
- `pagination` (object)
- `publisher_domain` (string)

### `get_media_buys` (~278 tokens)

[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 have open on this account
\- Confirming a buy left pending_creatives after sync_creatives
\- Getting a near-real-time delivery snapshot without a full delivery report

RETURNS:
\- media_buys: each with media_buy_id, status, currency, total_budget, confirmed_at,
  revision and packages[]. status is the AdCP media-buy-status enum; the accepted
  values are listed on the status_filter parameter below.
\- pagination: cursor-based

EXAMPLE:
get_media_buys({ status_filter: ["active", "pending_creatives"], include_snapshot: true })
get_media_buys({ media_buy_ids: ["mbuy_1750000000000_ab12cd34"] })

Input parameters:

- `context` (object)
- `include_snapshot` (boolean)
- `media_buy_ids` (array)
- `pagination` (object)
- `status_filter`

### `create_media_buy` (~359 tokens)

[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

RETURNS:
\- media_buy_id: Unique identifier for this media buy
\- campaign_id: Internal campaign identifier
\- creative_deadline: Deadline for creative asset submission
\- targeting_summary: What was targeted
\- budget_summary: Budget allocation details

EXAMPLE:
User: "Buy retail screens in NYC at $5 CPM for next week"
create_media_buy({
  name: "NYC Retail Week 12",
  buy_spec: {
    venue_types: ["retail"],
    geo: { city: "New York", state: "NY" },
    budget: { daily_usd: 500, bid_cpm: 5.0 },
    schedule: { start_date: "2026-03-16", end_date: "2026-03-22" }
  },
  creative: {
    url: "https://cdn.example.com/creative.mp4",
    type: "video",
    duration_seconds: 15
  },
  buyer_ref: "agency-order-12345"
})

Input parameters:

- `brand` (object)
- `buy_spec` (object)
- `buyer_ref` (string)
- `creative` (object)
- `end_time` (string)
- `name` (string, required)
- `packages` (array)
- `start_time` (string)

### `update_media_buy` (~126 tokens)

[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 } }
})

Input parameters:

- `media_buy_id` (string, required)
- `updates` (object, required)

### `sync_creatives` (~169 tokens)

[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: "mbuy_abc123",
  creatives: [{
    url: "https://cdn.example.com/ad.mp4",
    type: "video",
    width: 1920,
    height: 1080,
    duration_seconds: 15,
    file_size_mb: 12
  }]
})

Input parameters:

- `creatives` (array, required)
- `media_buy_id` (string, required)

### `get_media_buy_delivery` (~151 tokens)

[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, by_venue, by_hour (top performers)

EXAMPLE:
get_media_buy_delivery({ media_buy_id: "mbuy_abc123" })

Input parameters:

- `breakdown_by` (array)
- `dimensions` (array)
- `media_buy_id` (string, required)

### `provide_performance_feedback` (~180 tokens)

[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

EXAMPLE:
provide_performance_feedback({
  media_buy_id: "mbuy_abc123",
  feedback: {
    type: "bid_response",
    avg_bid_price_cpm: 6.5,
    fill_rate_percent: 72,
    preferred_hours: [8, 9, 10, 17, 18],
    quality_score: 0.85
  }
})

Input parameters:

- `feedback` (object, required)
- `media_buy_id` (string, required)

### `log_event` (~175 tokens)

[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: {
    event_id: "conv_12345",
    event_type: "store_visit",
    value_cents: 5000,
    screen_id: "507f1f77bcf86cd799439011",
    metadata: { store: "NYC-001", dwell_minutes: 12 }
  }
})

Input parameters:

- `event` (object, required)
- `media_buy_id` (string, required)

### `list_accounts` (~234 tokens)

[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
\- Checking your account is 'active' before creating a media buy
\- Introspecting what your key is permitted to do (accounts[].authorization.allowed_tasks)

RETURNS:
\- accounts: AdCP Account objects (account_id, name, status, operator, brand, billing,
  account_scope) plus an authorization object naming the tasks this key may invoke
\- pagination: has_more is always false — one credential, one account

EXAMPLE:
list_accounts({})
list_accounts({ status: "active" })

Input parameters:

- `account` (object)
- `context` (object)
- `pagination` (object)
- `sandbox` (boolean)
- `status` (string)

### `sync_accounts` (~506 tokens)

[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 account_id. Register one
agent per brand at https://api.trillboards.com/v1/partner/agent/register if you need
per-brand separation.

BILLING IS THE ONE SETTING THAT IS APPLIED. Send billing: 'operator' (we invoice you,
buying direct) or 'agent' (you are a buying agent consolidating across the brands you
front, and we invoice you for all of them — the marketplace-clearing model). The value is
stored on the account, reported back by list_accounts, and reflected in action 'updated'.
The set we accept is exactly account.supported_billing from get_adcp_capabilities;
'advertiser' is refused, with the reason, because we hold no billing relationship with a
third-party advertiser. One key is one account with one invoiced party, so a request
declaring two different billing values applies neither and says so.

Everything else is read-only and reports 'unchanged': payment terms, billing entity and
notification subscriptions are not per-account state on this platform, and anything sent
that was not applied is named in warnings[] rather than silently swallowed.

WHEN TO USE:
\- The account-setup step at the start of a buying flow
\- Declaring how you want to be invoiced, before create_media_buy
\- Confirming your account_id and status before create_media_buy

RETURNS:
\- accounts: per-entry result with account_id, action ('updated' | 'unchanged' | 'failed'),
  status, billing, account_scope, and warnings naming anything not applied

EXAMPLE:
sync_accounts({
  idempotency_key: "8f1c...",
  accounts: [{ brand: { domain: "acme.example" }, operator: "agency.example", billing: "agent" }]
})

Input parameters:

- `accounts` (array, required)
- `context` (object)
- `delete_missing` (boolean)
- `dry_run` (boolean)
- `idempotency_key` (string, required)
- `push_notification_config` (object)

### `get_pricing` (~55 tokens)

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.

Input parameters:

- `product` (string)

### `get_usage_summary` (~35 tokens)

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

### `setup_billing` (~56 tokens)

Set up pay-per-use billing with a Stripe payment method. Required after exceeding free tier limits. Pass a Stripe payment method token (pm_xxx) obtained from Stripe.js or Stripe Elements.

Input parameters:

- `payment_method_id` (string, required)

### `purchase_credits` (~88 tokens)

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.

Input parameters:

- `tier` (string, required)

### `get_billing_status` (~48 tokens)

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.

### `get_campaign_attribution` (~157 tokens)

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, incrementalVisits
\- cost: totalMediaCost, costPerUniqueReach, costPerIncrementalVisit
\- quality: avgMatchConfidence, statisticalSignificance, isSignificant
\- dataFreshness: latestOutcomeAt, provisionalCount, finalizedCount

Returns null if no attribution data exists for the campaign.

Input parameters:

- `campaign_id` (string, required)

### `get_attribution_timeseries` (~170 tokens)

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, totalMediaCost, isSignificant

EXAMPLE:
get_attribution_timeseries({
  campaign_id: "camp_abc123",
  start_date: "2026-03-01",
  end_date: "2026-03-10"
})

Input parameters:

- `campaign_id` (string, required)
- `end_date` (string)
- `start_date` (string)

### `get_attention_metrics` (~203 tokens)

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

RETURNS:
\- impressions: total, uniqueDevices
\- attention: avgScore (0-1), medianScore, p90Score, avgDwellSeconds, avgFaceCount, qualifiedPct
\- economics: standardCpm, attentionCpm (aCPM), costPerAttentiveReach
\- emotion: avgEngagement (0-1), positiveEmotionPct

aCPM = total_media_cost / (SUM(attention_score * face_count) / 1000)

Input parameters:

- `campaign_id` (string, required)

### `get_creative_attention` (~122 tokens)

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, avgEmotionEngagement, positiveEmotionPct, attentionQualifiedPct

Input parameters:

- `campaign_id` (string, required)

### `get_multi_touch_attribution` (~175 tokens)

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: average touchpoints per chain
\- channelAttribution: { dooh, mobile, web, store } (each 0-1, sums to 1)
\- conversions: total conversion events
\- totalConversionValue: sum of conversion values (cents)
\- avgConfidence: average match confidence across chains

Returns null if no multi-touch chains exist.

Input parameters:

- `campaign_id` (string, required)

### `get_cross_channel_journey` (~118 tokens)

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
\- channels: Array of { channel, touchpoints, uniqueDevices } distribution

Input parameters:

- `campaign_id` (string, required)

### `get_incrementality` (~171 tokens)

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 experiments, each with:
\- experimentId, type (geo_holdout/ghost_ads/psm), status
\- treatmentDmas, controlDmas
\- latestResult: treatment/control rates, lift%, incrementalVisits,
  pValue, posteriorProbPositive, expectedUplift, credibleInterval

Returns empty array if no experiments exist for this campaign.

Input parameters:

- `campaign_id` (string, required)

### `create_experiment` (~245 tokens)

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 with experiment_id, status, and all parameters.

EXAMPLE:
create_experiment({
  campaign_id: "camp_abc123",
  experiment_type: "geo_holdout",
  treatment_dmas: ["501", "504"],
  control_dmas: ["503", "505"],
  holdout_pct: 0.15,
  target_mde: 0.10
})

Input parameters:

- `campaign_id` (string, required)
- `control_dmas` (array)
- `experiment_type` (string, required)
- `holdout_pct` (number)
- `target_alpha` (number)
- `target_mde` (number)
- `target_power` (number)
- `treatment_dmas` (array)

### `get_creative_attribution` (~165 tokens)

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 creatives ranked by store visits, each with:
\- creativeId, variant, totalVisits, avgVisitRate
\- attention: avgScore, avgDwell, avgEmotion, dominantEmotion
\- avgLiftPct, avgCostPerVisit
\- bestContext: { venueType, daypart, weather }
\- dateRange: { first, last, daysMeasured }

Input parameters:

- `campaign_id` (string, required)

### `get_roas` (~194 tokens)

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

RETURNS:
\- transactions: total, uniquePurchasers, totalRevenueCents, avgBasketCents
\- attribution: attributedTransactions, attributedRevenueCents, totalMediaCostCents, roas, iroas
\- salesLift: exposedPurchasers, controlPurchasers, incrementalTransactions, incrementalRevenueCents, salesLiftPct, posteriorProbPositive
\- timing: avgHoursToPurchase, medianHoursToPurchase

Returns null if no transaction data exists.

Input parameters:

- `campaign_id` (string, required)

### `export_cohort` (~227 tokens)

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:
\- status: 'synced', 'no_cohort', 'credentials_missing', or 'empty_cohort'
\- destination: the DSP name
\- segmentId: internal segment ID
\- externalSegmentId: DSP-side segment ID
\- maidCount: number of MAIDs uploaded
\- accepted: number accepted by DSP

Supported destinations: ttd, dv360, meta, cadent, mediaocean

EXAMPLE:
export_cohort({
  campaign_id: "camp_abc123",
  destination: "ttd"
})

Input parameters:

- `campaign_id` (string, required)
- `destination` (string, required)

### `get_audience_lookalike` (~206 tokens)

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 lookalike audience segments for targeting

RETURNS:
Array of similar screens ranked by cosine similarity, each with:
\- screen_id, similarity (0-1), metadata (face_count, attention, income, lifestyle), last_seen

EXAMPLE:
get_audience_lookalike({
  screen_id: "scr_abc123",
  limit: 10,
  min_similarity: 0.8
})

Input parameters:

- `country` (string)
- `limit` (integer)
- `min_similarity` (number)
- `screen_id` (string, required)
- `venue_type` (string)

### `semantic_audience_search` (~180 tokens)

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 specific audience scenario

RETURNS:
Array of matching screens ranked by semantic similarity, each with:
\- screen_id, mongo_screen_id, scene_description, contextual_relevance, similarity, created_at

EXAMPLE:
semantic_audience_search({
  query: "young professionals in a coffee shop looking at phones",
  limit: 10
})

Input parameters:

- `limit` (integer)
- `min_similarity` (number)
- `query` (string, required)
- `since` (string)

### `get_campaign_heatmap` (~127 tokens)

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, lng (rounded to 3 decimal places)
\- uniqueDevices, totalExposures
\- avgConfidence (match confidence score)

Input parameters:

- `campaign_id` (string, required)

### `verify_proof_of_play` (~391 tokens)

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 Trillboards public key.

WHEN TO USE:
\- Verifying that ads were actually delivered to screens
\- Exporting cryptographically signed proof records for auditors
\- Getting proof-of-play data for campaign transparency reports

RETURNS (verify mode):
\- valid: boolean, reason: string if invalid, version: 'v1' or 'v2'

RETURNS (get proofs mode):
\- campaignId, totalImpressions, proofsReturned
\- proofs: Array of signed impression proofs
\- pagination: { limit, hasMore, nextCursor }
\- signatureVersion, publicKeyUrl

EXAMPLE (verify):
verify_proof_of_play({
  proof_payload: {
    signature: "ed25519=abc123...",
    timestamp: "2026-03-10T15:30:00Z",
    adId: "ad_123",
    impressionId: "imp_456",
    screenId: "scr_789",
    deviceId: "dev_012"
  }
})

EXAMPLE (get proofs):
verify_proof_of_play({
  campaign_id: "camp_abc123",
  start_date: "2026-03-01",
  end_date: "2026-03-10"
})

Input parameters:

- `campaign_id` (string)
- `cursor` (string)
- `end_date` (string)
- `limit` (integer)
- `proof_payload` (object)
- `start_date` (string)

### `query_observations` (~401 tokens)

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, environment, or commerce observation data
\- Answering natural language questions about what screens are sensing

RETURNS:
\- data: Array of observation objects with device, venue, payload, confidence, model versions
\- metadata: { observation_count, time_range, coverage_pct, model_versions }
\- suggested_next_queries: Contextual follow-up queries

Each observation includes:
\- observation_id, device_id, screen_mongo_id, venue_type
\- observed_at: Timestamp of the observation
\- observation_family: audience | vehicle | environment | commerce
\- payload: JSONB with model outputs (face_count, emotion, vehicle_count, etc.)
\- confidence: Model confidence score (0-1)
\- evidence_grade: Quality grade of the observation
\- model_versions: Which ML models produced this data

EXAMPLE:
User: "Show me audience observations at QSR venues in the last hour"
query_observations({
  query: "audience observations at QSR venues",
  filters: {
    observation_family: ["audience"],
    venue_type: ["restaurant_qsr"],
    time_range: { start: "2026-03-16T14:00:00Z", end: "2026-03-16T15:00:00Z" }
  },
  limit: 50
})

User: "What are screens sensing right now?"
query_observations({
  query: "latest observations from all screens",
  limit: 20
})

Input parameters:

- `filters` (object)
- `limit` (integer)
- `query` (string, required)

### `semantic_search_observations` (~381 tokens)

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 pgvector
cosine similarity search.

TIME BOUND: searches the last 30 days by default. Pass filters.time_range to
widen or narrow it; the window actually applied is echoed in
metadata.time_range. Observations are retained for 90 days.

WHEN TO USE:
\- Finding observations that match a conceptual description
\- Discovering contextual moments across the screen network
\- Searching for audience situations ("families waiting in line", "professionals on coffee break")
\- Finding commerce patterns ("high purchase intent near checkout")

RETURNS:
\- data: Array of matching observations ranked by semantic similarity, each with:
  \- observation_id, device_id, venue_type, observation_family
  \- observed_at, payload, confidence, evidence_grade
  \- similarity: Cosine similarity score (0-1, higher = more relevant)
\- metadata: { result_count, query_embedding_model, search_scope, time_range }
\- suggested_next_queries: Related semantic queries to explore

EXAMPLE:
User: "Find lunch rush moments at fast casual restaurants"
semantic_search_observations({
  query: "lunch rush at fast casual restaurants with high foot traffic",
  filters: { venue_type: ["restaurant_qsr"] },
  limit: 20
})

User: "Find moments with high emotional engagement"
semantic_search_observations({
  query: "audience showing strong positive emotional reactions",
  filters: { observation_family: ["audience"] },
  limit: 10
})

Input parameters:

- `filters` (object)
- `limit` (integer)
- `query` (string, required)

### `anomaly_detect` (~370 tokens)

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 face count)
\- Detecting equipment anomalies (confidence drops indicating sensor issues)
\- Identifying unusual commerce or vehicle patterns
\- Finding outlier moments that may indicate events, incidents, or opportunities

RETURNS:
\- anomalies: Array of anomalous observations with:
  \- observation_id, device_id, venue_type, observed_at
  \- metric_value: The observed value
  \- z_score: How many standard deviations from the mean
  \- direction: 'above' or 'below' the mean
  \- payload: Full observation payload for context
\- baseline: { mean, stddev, sample_count, lookback_hours }
\- suggested_next_queries: Follow-up queries to investigate anomalies

EXAMPLE:
User: "Are there any unusual audience patterns at retail venues?"
anomaly_detect({
  metric: "face_count",
  venue_type: "retail",
  lookback_hours: 24,
  threshold_sigma: 2.0
})

User: "Detect anomalies in vehicle counts at this screen"
anomaly_detect({
  metric: "vehicle_count",
  screen_id: "507f1f77bcf86cd799439011",
  lookback_hours: 48,
  threshold_sigma: 2.5
})

Input parameters:

- `lookback_hours` (number)
- `metric` (string, required)
- `screen_id` (string)
- `threshold_sigma` (number)
- `venue_type` (string)

### `cross_signal_correlate` (~384 tokens)

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 audience behaviors correlate with business outcomes
\- Discovering hidden patterns (e.g., crowd_energy vs purchase_intent)
\- Validating hypotheses about audience-venue-time relationships

RETURNS:
\- data: Correlation analysis with:
  \- signal_a, signal_b: The two signals being correlated
  \- correlation_r: Pearson correlation coefficient (-1 to +1)
  \- correlation_r2: R-squared (proportion of variance explained)
  \- p_value: Statistical significance
  \- sample_count: Number of data points used
  \- effect_size: Cohen's d effect size
  \- confidence_interval_lower, confidence_interval_upper: 95% CI bounds
  \- insight_summary: Human-readable interpretation
\- metadata: { computation_method, window, filters_applied }
\- suggested_next_queries: Related correlation analyses to explore

EXAMPLE:
User: "Is there a correlation between audience attention and ad fill rate at QSR venues?"
cross_signal_correlate({
  signal_a: "attention_score",
  signal_b: "ad_fill_rate",
  filters: { venue_type: "restaurant_qsr" }
})

User: "How does crowd energy relate to purchase intent during lunch hours?"
cross_signal_correlate({
  signal_a: "crowd_energy",
  signal_b: "purchase_intent",
  filters: { daypart: "lunch" }
})

Input parameters:

- `filters` (object)
- `signal_a` (string, required)
- `signal_b` (string, required)

### `configure_sensing` (~460 tokens)

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 specific things (faces, vehicles, emotions, etc.)
\- Changing what a screen detects based on venue type or business needs
\- Configuring custom sensing for special events or campaigns
\- Translating business intent into ML model configuration

RETURNS:
\- data: The generated sensing profile with:
  \- profile_name, profile_type, description
  \- models: Array of ML model IDs to activate
  \- classes: COCO classes to detect (for object detection models)
  \- thresholds: Confidence and alert thresholds
  \- observation_families: What types of observations will be produced
  \- capture_interval_ms, report_interval_ms: Timing configuration
  \- estimated_fps_impact: CPU cost estimate
  \- data_fields_produced: All data fields the profile will generate
  \- reasoning: Why these models/classes were chosen
  \- deployment_status: 'generated' | 'pushed' | 'push_failed'
\- metadata: { screen_id, auto_deploy, profile_id }
\- suggested_next_queries: Follow-up actions

EXAMPLE:
User: "Set up the lobby screen to detect foot traffic and emotions"
configure_sensing({
  screen_id: "507f1f77bcf86cd799439011",
  intent: "Detect foot traffic patterns, count people, and measure emotional reactions to displayed content",
  auto_deploy: false
})

User: "Configure this drive-through screen for vehicle counting"
configure_sensing({
  screen_id: "507f1f77bcf86cd799439011",
  intent: "Count vehicles in drive-through lane, detect vehicle types, measure queue length",
  auto_deploy: true
})

Input parameters:

- `auto_deploy` (boolean)
- `intent` (string, required)
- `screen_id` (string, required)

### `export_dataset` (~394 tokens)

Export observation data as a structured dataset. Supports filtering by time, geography, venue type, and observation family. Applies k-anonymity (k=5) to protect individual privacy.

Queries the relevant table based on the selected dataset type, applies
filters, enforces k-anonymity by suppressing groups with fewer than 5
observations, and returns structured data.

WHEN TO USE:
\- Exporting audience data for external analysis
\- Building datasets for machine learning or reporting
\- Getting structured vehicle or commerce data for a specific time/place
\- Creating cross-signal datasets for correlation analysis

RETURNS:
\- data: Array of dataset rows (schema varies by dataset type)
\- metadata: { row_count, k_anonymity_applied, export_id, dataset, filters_applied, time_range }
\- suggested_next_queries: Related exports or analyses

Dataset types:
\- observations: Raw observation stream data (all families)
\- audience: Audience-specific data (face_count, demographics, attention, emotion)
\- vehicle: Vehicle counting and classification data
\- cross_signal: Pre-computed cross-signal correlation insights

EXAMPLE:
User: "Export audience data from retail venues last week"
export_dataset({
  dataset: "audience",
  filters: {
    time_range: { start: "2026-03-09", end: "2026-03-16" },
    venue_type: ["retail"]
  },
  format: "json"
})

User: "Get vehicle data near geohash 9q8yy"
export_dataset({
  dataset: "vehicle",
  filters: {
    time_range: { start: "2026-03-15", end: "2026-03-16" },
    geo: "9q8yy"
  }
})

Input parameters:

- `dataset` (string, required)
- `filters` (object, required)
- `format` (string)

### `predictive_query` (~404 tokens)

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 audience patterns at a venue or screen
\- Forecasting foot traffic trends for campaign planning
\- Understanding whether metrics are trending up, down, or stable
\- Making data-driven decisions about inventory and pricing

RETURNS:
\- prediction: The predicted trend and expected values
  \- trend: 'increasing' | 'decreasing' | 'stable'
  \- current_avg: Current average metric value
  \- predicted_avg: Predicted average over the time horizon
  \- change_pct: Expected percentage change
  \- confidence_interval: { lower, upper } bounds
\- confidence: Overall prediction confidence (0-1)
\- supporting_data: Recent data points that inform the prediction
  \- data_points: Array of { bucket, avg_value, sample_count }
  \- total_observations: Total observations analyzed
\- methodology: Description of the prediction approach
\- suggested_next_queries: Follow-up queries to refine the prediction

EXAMPLE:
User: "Will this QSR venue see more foot traffic next week?"
predictive_query({
  question: "Will foot traffic increase at QSR venues?",
  venue_type: "restaurant_qsr",
  time_horizon: "7d"
})

User: "Predict audience attention trends for this screen"
predictive_query({
  question: "What will audience attention look like?",
  screen_id: "507f1f77bcf86cd799439011",
  time_horizon: "3d"
})

Input parameters:

- `question` (string, required)
- `screen_id` (string)
- `time_horizon` (string)
- `venue_type` (string)

### `get_social_attention` (~412 tokens)

Query social attention contagion metrics from the observation stream. Returns windows where attention propagated between viewers (social amplification factor > 1).

Social attention data is produced by the AttentionGraphBuilder running on CTV
edge devices, which models viewer attention as a directed graph and detects
when one viewer looking at the screen triggers nearby viewers to also look
(attention contagion / social amplification).

WHEN TO USE:
\- Finding moments where social proof drove collective engagement
\- Identifying which venues or dayparts exhibit highest attention contagion
\- Understanding cascading attention patterns (cascade depth)
\- Correlating social amplification with ad effectiveness (VAS)

RETURNS:
\- data: Array of observation_stream rows with socialAttention payload
  \- payload.socialAttention.socialAmplificationFactor (SAF): ratio of actual-to-expected group attention (>1 = contagion detected)
  \- payload.socialAttention.cascadeDepth: max depth of attention propagation chain
  \- payload.socialAttention.viralAttentionScore: composite metric combining SAF and cascade depth
  \- payload.socialAttention.contagionWindowMs: time window over which cascade occurred
  \- payload.socialAttention.triggerViewerIndex: which viewer initiated the cascade
\- metadata: { result_count, time_range, min_saf_filter }
\- suggested_next_queries: Follow-up queries

EXAMPLE:
User: "Show me moments where attention went viral in bar venues"
get_social_attention({ min_saf: 2.0, venue_type: "bar" })

User: "Find the strongest social amplification events this week"
get_social_attention({ min_saf: 3.0, time_range: { start: "2026-03-09T00:00:00Z", end: "2026-03-16T00:00:00Z" } })

Input parameters:

- `limit` (integer)
- `min_saf` (number)
- `screen_id` (string)
- `time_range` (object)
- `venue_type` (string)

### `find_similar_moments` (~337 tokens)

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 pgvector
cosine similarity.

WHEN TO USE:
\- Searching for historical moments similar to a target scenario
\- Finding "moments like this one" across different venues/times
\- Discovering when similar audience compositions or behaviors occurred
\- Planning ad placements based on past similar contexts

RETURNS:
\- data: Array of matching observations with similarity scores
  \- observation_id, observed_at, venue_type, device_id, screen_mongo_id
  \- payload: full observation data
  \- evidence_grade: quality of observation
  \- similarity: cosine similarity score (0-1, higher = more similar)
\- metadata: { result_count, embedding_model, min_similarity_threshold }
\- suggested_next_queries: Follow-up queries

EXAMPLE:
User: "Find moments with high engagement in evening restaurants with families"
find_similar_moments({ query: "evening restaurant venue with families present, high emotional engagement and attention" })

User: "When did we see young adults highly engaged at transit screens?"
find_similar_moments({ query: "transit venue morning commute young adults high attention" })

Input parameters:

- `limit` (integer)
- `min_similarity` (number)
- `query` (string, required)
- `venue_type` (string)

### `predict_moment_quality` (~310 tokens)

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 generative prediction
4\. Always decomposes the prediction into causal factors

WHEN TO USE:
\- Evaluating whether a creative will perform well in a specific context
\- A/B testing creative placement hypotheses before committing budget
\- Understanding which causal factors drive VAS for a creative
\- Comparing expected performance across different moment types

RETURNS:
\- prediction: { predictedVAS (0-1), confidence (0-1), method ('historical'|'model'), sampleSize }
\- causal_factors: { audienceMatch, contextMatch, attentionState, socialPotential } (each 0-1)
\- metadata: { creative_id, moment_description }
\- suggested_next_queries: Follow-up queries

EXAMPLE:
User: "How would a coffee ad perform at a transit station during morning rush?"
predict_moment_quality({
  moment_description: "transit venue, morning commute, 12 viewers, high attention, mostly 25-34 age range",
  creative_id: "coffee-brand-morning-30s"
})

Input parameters:

- `creative_id` (string, required)
- `moment_description` (string, required)

### `recommend_creative` (~263 tokens)

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 creatives for a campaign across different contexts
\- Optimizing creative rotation for maximum VAS
\- Pre-campaign creative selection based on audience and venue

RETURNS:
\- rankings: Array sorted by predicted VAS (descending)
  \- creativeId, predictedVAS (0-1), confidence (0-1), rank (1-N)
\- metadata: { candidate_count, moment_description }
\- suggested_next_queries: Follow-up queries

EXAMPLE:
User: "Which of these 3 creatives will perform best at a gym in the evening?"
recommend_creative({
  moment_description: "gym venue, evening, 6 viewers, high attention, mostly male 18-34",
  creative_ids: ["fitness-brand-30s", "energy-drink-15s", "tech-gadget-20s"]
})

Input parameters:

- `creative_ids` (array, required)
- `moment_description` (string, required)

### `get_social_contagion_summary` (~327 tokens)

Aggregate social attention metrics across screens and time periods. Shows which venues and dayparts have the highest social amplification.

Queries observation_stream for social attention data and aggregates 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 venues generate the most social amplification
\- Comparing daypart effectiveness for social contagion
\- Identifying top-performing screens for attention cascading
\- Planning campaigns that leverage social proof

RETURNS:
\- data: Array of aggregated rows, sorted by avg SAF descending
  \- group_key: the dimension value (venue type, daypart, or screen ID)
  \- avg_saf: average social amplification factor
  \- avg_cascade_depth: average attention cascade depth
  \- avg_viral_attention_score: average viral attention score
  \- event_count: number of social attention events in the group
\- metadata: { group_by, time_range, total_events }
\- suggested_next_queries: Follow-up queries

EXAMPLE:
User: "Which venues have the highest social amplification this week?"
get_social_contagion_summary({ group_by: "venue", time_range: { start: "2026-03-09", end: "2026-03-16" } })

User: "Show me social attention by daypart over the last 7 days"
get_social_contagion_summary({ group_by: "daypart" })

Input parameters:

- `group_by` (string)
- `time_range` (object)

### `get_dataset_stats` (~231 tokens)

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 diversity of training data (creatives, venues, time range)
\- Monitoring causal dataset health and growth
\- Planning data collection strategies

RETURNS:
\- data: Dataset statistics
  \- total_tuples: number of context-action-outcome records
  \- unique_creatives: number of distinct creatives with VAS data
  \- unique_venue_types: number of distinct venue types represented
  \- date_range: { start, end } of available data
  \- observations_per_creative: { min, max, mean, median } distribution
\- metadata: { query_window_days }
\- suggested_next_queries: Follow-up queries

EXAMPLE:
User: "How much causal training data do we have?"
get_dataset_stats({})

### `get_content_performance` (~289 tokens)

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, totalPlays, uniqueScreens
\- avgAttention (0-1), avgAudienceSize, avgDwellMs
\- venueDistribution: Array of { venue_type, plays }
\- daypartDistribution: Array of { daypart, plays }
\- topScreens: Top 10 screens by play count with attention scores
\- period: { start, end } date range

EXAMPLE:
User: "How is video dQw4w9WgXcQ performing on retail screens?"
get_content_performance({
  video_id: "dQw4w9WgXcQ",
  venue_type: "retail",
  days: 30
})

User: "Show me the last 7 days of performance for this video"
get_content_performance({
  video_id: "abc123xyz",
  days: 7
})

Input parameters:

- `days` (integer)
- `venue_type` (string)
- `video_id` (string, required)

### `search_content` (~246 tokens)

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 matching content with similarity scores
  \- videoId, title, contentCategory, description, durationSeconds
  \- reviewStatus (approved/pending/rejected)
  \- similarity (0-1, cosine similarity against query embedding)
\- meta: { count, query, limit, minSimilarity }

EXAMPLE:
User: "Find fitness and workout content"
search_content({
  query: "fitness workout exercise gym",
  limit: 10,
  min_similarity: 0.6
})

User: "Search for calming nature content suitable for medical offices"
search_content({
  query: "calming nature scenes peaceful landscapes meditation",
  min_similarity: 0.5
})

Input parameters:

- `limit` (integer)
- `min_similarity` (number)
- `query` (string, required)

### `get_content_recommendations` (~281 tokens)

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: Array of recommended content ranked by performance score
  \- videoId, title, contentCategory, durationSeconds
  \- totalPlays, uniqueScreens
  \- avgAttention (0-1), avgDwellMs
  \- performanceScore (composite of attention, replay density, dwell time)
\- meta: { count, venue_type, daypart, limit }

Performance score formula: attention(40%) + replay_density(30%) + dwell_time(30%)

EXAMPLE:
User: "What content works best in bars during the evening?"
get_content_recommendations({
  venue_type: "bar",
  daypart: "evening",
  limit: 10
})

User: "Best performing content for transit screens"
get_content_recommendations({
  venue_type: "transit",
  limit: 20
})

Input parameters:

- `daypart` (string)
- `limit` (integer)
- `venue_type` (string, required)

### `list_endpoints` (~182 tokens)

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,
  description, agent_safe, idempotent, cost_tier, tags, doc_url,
  example_request }
\- total_operations: Total count.
\- surfaces: Known surface identifiers.

EXAMPLE:
Agent: "What read-only endpoints can I call?"
list_endpoints({ agent_safe: true })

Input parameters:

- `agent_safe` (boolean)
- `idempotent` (boolean)
- `surface` (string)

### `describe_endpoint` (~165 tokens)

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).
\- responses: Map of status code → description/schema.
\- linked_error_codes: Error catalog entries the endpoint can emit.

EXAMPLE:
Agent: "How do I call the screen audience endpoint?"
describe_endpoint({
  path: "/v1/data/screens/{screenId}/audience",
  method: "GET"
})

Input parameters:

- `method` (string, required)
- `path` (string, required)

### `validate_request` (~171 tokens)

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, code, message, param,
  doc_url, details[] } } envelope is included under `error`.

EXAMPLE:
validate_request({
  path: "/v1/data/query",
  method: "POST",
  payload: { dataset: "inference_outcomes", limit: 9999 }
})

Input parameters:

- `method` (string, required)
- `path` (string, required)
- `payload`

### `list_error_codes` (~132 tokens)

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 GET /v1/errors but executed in-process (no HTTP round-trip).

EXAMPLE:
Agent: "What error codes can the API return?"
list_error_codes()

### `query_changelog` (~243 tokens)

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 always included.
\- type (string, optional): Filter by change category. Accepts:
    "breaking"    → changed + removed entries
    "additive"    → added entries
    "deprecation" → deprecated entries
    "fix"         → fixed entries
  Can be comma-separated: "breaking,deprecation"

RETURNS:
\- object: "list"
\- data: Array of { version, date, type, surface, description }
\- total: Number of matching entries.

EXAMPLE:
Agent: "What broke since April 1st?"
query_changelog({ since: "2026-04-01", type: "breaking" })

Input parameters:

- `since` (string)
- `type` (string)

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/snehdhruv-trillboards-dooh/api#diagnostics

## Score history

- 2026-08-03: 65
- 2026-08-02: 65
- 2026-08-01: 64
- 2026-07-31: 64
- 2026-07-30: 61
- 2026-07-28: 59
- 2026-07-27: 59
- 2026-07-26: 59

## Links

- Remote endpoint: https://api.trillboards.com/mcp
- Repository: https://github.com/trillboards/packages
- Website: https://trillboards.com/
- Changelog RSS feed: https://verifymcp.io/servers/snehdhruv-trillboards-dooh/api/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/snehdhruv-trillboards-dooh/api/changelog.json
- HTML version of this page: https://verifymcp.io/servers/snehdhruv-trillboards-dooh/api