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Trillboards DOOH Advertising

by io.github.snehdhruv

get_attention_metrics

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 Schema

TableJSON Schema
NameRequiredDescriptionDefault
campaign_idYesCampaign identifier

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the burden of explaining behavior. It discloses output structure, defines key metrics, and even provides the aCPM formula. It implies read-only behavior via 'Get' but does not explicitly state side-effect-free operation or any rate limits, though these are less critical for a data retrieval tool.

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 uses clear section headers (WHEN TO USE, RETURNS, formula) and is well-organized. The opening includes a slightly promotional sentence about FEIN edge AI, which adds some fluff but is not waste; overall it is efficient and scannable.

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

Completeness5/5

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

Given the tool has a single parameter and no output schema, the description is thorough: it explains the return structure with nested fields, defines the units (0-1 scores, seconds), and provides the aCPM formula. This is complete enough for an agent to understand what to expect.

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

Parameters3/5

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

Schema coverage is 100% for the single parameter campaign_id, with a basic description 'Campaign identifier'. The tool description adds no extra semantic detail about the parameter beyond what the schema already provides, so the baseline of 3 applies.

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?

The description clearly states the action ('Get edge AI attention metrics for a campaign') and identifies the unique FEIN-powered aspect. It distinguishes itself from siblings like get_campaign_performance or get_social_attention by specifying the type of attention metrics and the FEIN technology.

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?

The WHEN TO USE section provides clear contexts such as measuring actual attention, comparing aCPM vs CPM, and getting face/dwell/emotion data. However, it does not explicitly mention when not to use this tool or name alternatives, so it stops short of full exclusions.

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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TDQS

B3.3/5.0
Disambiguation2/5

There are exact duplicates (get_task_status/tasks_get, list_tasks/tasks_list) and several overlapping analytics, attribution, and semantic search clusters (get_attention_metrics vs get_creative_attention vs get_social_attention; find_similar_moments vs semantic_search_observations; get_campaign_attribution vs get_multi_touch_attribution vs get_roas). Detailed descriptions help, but with 83 tools an agent will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern (list_devices, create_campaign, delete_webhook), but there are notable inconsistencies: list_* and get_* are used interchangeably for list operations, attention tools mix conventions (get_attention_metrics vs get_creative_attention vs get_social_attention), and the legacy tasks_get/tasks_list names break the established get_task_status/list_tasks pattern.

Tool Count1/5

83 tools is an extreme count for a single MCP server, spanning device management, sensing, campaigns, media buys, attribution, webhooks, billing, API discovery, and AdCP protocol concerns. This is a broad API surface dump rather than a focused tool set, and it would be far better split into several coherent servers.

Completeness2/5

Despite the enormous surface, core campaign lifecycle is incomplete: create_campaign explicitly tells the agent to use update_campaign to activate a campaign, but no update_campaign tool exists, and there are no list/delete campaign tools. Significant capabilities exist for analytics, attribution, and webhooks, but the primary advertising workflow has a dead end.

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