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Trillboards DOOH Advertising

by io.github.snehdhruv
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get_products

[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 Schema

TableJSON Schema
NameRequiredDescriptionDefault
briefNoNatural language campaign brief for AI-driven inventory matching
marketNoMarket/city to get products for
filtersNoStructured filters for wholesale/refine modes
paginationNo
buying_modeNoAdCP buying mode (default: brief)
venue_typesNoFilter by venue types (legacy, prefer filters.venue_types)
audience_profileNoTarget audience characteristics

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / additionalProperties
      Previous value: -falseNew value: +true
    • removedInput schema / properties / context
      Removed value: -{
-  "additionalProperties": {},
-  "type": "object"
-}
  2. Changed3 schema fields changed
    • removedInput schema / properties / adcp_major_version
      Removed value: -{
-  "description": "Legacy major-precision AdCP version pin, an integer (e.g. 3), carried since 3.0. Prefer adcp_version. A major this agent does not speak is answered with a VERSION_UNSUPPORTED task failure; get_adcp_capabilities.adcp.major_versions lists the supported set.",
-  "type": "integer"
-}
    • removedInput schema / properties / adcp_version
      Removed value: -{
-  "description": "Release-precision AdCP version this request is pinned to, \"MAJOR.RELEASE\" (e.g. \"3.1\"). A release this agent does not speak is answered with a VERSION_UNSUPPORTED task failure listing the ones it does. Read get_adcp_capabilities.adcp.supported_versions to pin correctly.",
-  "type": "string"
-}
    • removedInput schema / properties / context / description
      Removed value: -"Opaque caller-supplied correlation object. Echoed back byte-for-byte, unparsed, on both successful and failed responses; the agent never reads its contents."
  3. Changed3 schema fields changed
    • addedInput schema / properties / adcp_major_version
      Added value: +{
+  "description": "Legacy major-precision AdCP version pin, an integer (e.g. 3), carried since 3.0. Prefer adcp_version. A major this agent does not speak is answered with a VERSION_UNSUPPORTED task failure; get_adcp_capabilities.adcp.major_versions lists the supported set.",
+  "type": "integer"
+}
    • addedInput schema / properties / adcp_version
      Added value: +{
+  "description": "Release-precision AdCP version this request is pinned to, \"MAJOR.RELEASE\" (e.g. \"3.1\"). A release this agent does not speak is answered with a VERSION_UNSUPPORTED task failure listing the ones it does. Read get_adcp_capabilities.adcp.supported_versions to pin correctly.",
+  "type": "string"
+}
    • addedInput schema / properties / context / description
      Added value: +"Opaque caller-supplied correlation object. Echoed back byte-for-byte, unparsed, on both successful and failed responses; the agent never reads its contents."
  4. Changed1 schema field changed
    • addedInput schema / properties / context
      Added value: +{
+  "additionalProperties": {},
+  "type": "object"
+}
  5. Changed1 schema field changed
    • addedInput schema / properties / pagination
      Added value: +{
+  "additionalProperties": false,
+  "properties": {
+    "cursor": {
+      "maxLength": 500,
+      "type": "string"
+    },
+    "max_results": {
+      "maximum": 100,
+      "minimum": 1,
+      "type": "integer"
+    }
+  },
+  "type": "object"
+}
  6. Changed6 schema fields changed
    • addedInput schema / properties / audience_profile / description
      Added value: +"Target audience characteristics"
    • addedInput schema / properties / brief / description
      Added value: +"Natural language campaign brief for AI-driven inventory matching"
    • addedInput schema / properties / buying_mode / description
      Added value: +"AdCP buying mode (default: brief)"
    • addedInput schema / properties / filters / description
      Added value: +"Structured filters for wholesale/refine modes"
    • addedInput schema / properties / market / description
      Added value: +"Market/city to get products for"
    • addedInput schema / properties / venue_types / description
      Added value: +"Filter by venue types (legacy, prefer filters.venue_types)"
  7. First observed

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the full burden and meets it: it states no authentication is required, frames the call as read-only discovery, explains that products are ordered by evidence from `brief`, and discloses that unverifiable words are flagged and `observed` numbers appear only where measured. This gives an agent an accurate safety/behavioral model before invoking.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well structured with WHEN TO USE, RETURNS, and EXAMPLE sections, and the purpose is front-loaded in the first sentence. It is slightly verbose, with the evidence-behavior paragraph partly restating the RETURNS section, so it is not maximally concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 7 parameters, nested objects, and no output schema, the description includes a return-shape outline and an example, plus auth/behavioral context. It does not explain pagination, the wholesale/refine buying modes, or filter semantics, though the schema covers most of those; overall it is adequate but not exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is high (86%), so the baseline is 3. The description adds extra meaning for the key `brief` parameter — explaining how it is interpreted, that products are ranked by evidence, and that `brief_interpretation` is returned for verification — plus a concrete example call. Other parameters rely on schema descriptions, which is acceptable at this coverage level.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a specific verb+resource: 'Get available DOOH advertising products and packages.' It adds the distinctive mechanism — a natural-language `brief` answered from observed screen data — and positions the tool as open catalogue discovery ('read the catalogue first, get a key when you want to transact'), separating it from transaction tools and similar inventory/price siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

A dedicated WHEN TO USE section lists three concrete scenarios: browsing inventory before campaign creation, comparing pricing across venue types/locations, and checking availability by market/time. It gives clear when-to-use context but does not explicitly name alternatives or state when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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