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Trillboards DOOH Advertising

by io.github.snehdhruv

get_social_attention

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 Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results to return. Default: 20, max: 200.
min_safNoMinimum social amplification factor threshold. Default: 1.5. Higher values return only stronger contagion events.
screen_idNoFilter by screen MongoDB ID. Optional.
time_rangeNoTime range filter. Defaults to last 24 hours.
venue_typeNoFilter by venue type (e.g., "bar", "restaurant_qsr", "transit"). Optional.

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It explains the data source (AttentionGraphBuilder on CTV edge devices), the meaning of key metrics (SAF, cascade depth), and the return payload structure. It does not explicitly mention read-only/safety, but the verb 'Query' and operation semantics imply non-destructive use. It lacks rate-limit or auth details, which would push it to a 5.

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

Conciseness5/5

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

The description is well-organized into clear sections (purpose, when to use, returns, examples). Each section adds meaningful information without fluff. The examples are concise and illustrative. It is longer than average but earns its length through density and structure.

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?

With no output schema, the description thoroughly details the return payload, including nested socialAttention metrics and metadata. It also covers the data provenance and typical use cases, making it self-contained. The provided examples cover common query patterns. This is a complete description for an agent to use the tool effectively.

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 covers all 5 parameters with descriptions (100% coverage), so baseline is 3. The description adds value through two representative examples showing how to combine min_saf, venue_type, and time_range, and clarifies that the metadata includes min_saf_filter. It avoids redundant repetition of schema fields.

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 explicitly states the tool 'Query social attention contagion metrics from the observation stream' and specifies the core criterion (SAF > 1). It distinguishes itself from sibling tools like get_attention_metrics and get_social_contagion_summary by focusing on contagion/cascade behavior rather than general attention or aggregate summaries.

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 four concrete use cases, clearly indicating appropriate contexts. However, it does not explicitly mention when not to use the tool or name alternative siblings (e.g., get_social_contagion_summary for summary stats), so exclusion guidance is missing.

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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