Skip to main content
Glama

Trillboards DOOH Advertising

by com.trillboards
OverviewDiscussionsAdmin

get_social_attention

Query measured social attention events within your authorized screen and venue scope. Filter by minimum social amplification factor and time range; results are sorted by social amplification factor descending.

WHEN TO USE:

  • Finding moments where social proof drove collective engagement

  • Identifying which venues or dayparts exhibit highest attention contagion

  • Understanding cascading attention patterns (cascade depth)

  • Correlating social amplification with ad effectiveness (VAS)

RETURNS:

  • data: Array of events with flat fields

    • observation_id, observed_at, screen_mongo_id, venue_type: event context

    • social_amplification_factor (SAF): ratio of actual-to-expected group attention

    • cascade_depth: maximum depth of the attention propagation chain

    • viral_attention_score: composite social attention score

    • primary_attention_anchor_face_index: the event's attention anchor index, or null

    • amplification_evidence: description of the measured attention pattern

    • payload: null

  • metadata: { result_count, time_range, min_saf_filter }

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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden, and it does disclose real behavior: authorization scoping ('authorized screen and venue scope'), sort order (SAF descending), and a detailed field-by-field return shape. It omits rate limits, pagination behavior, and the result-count cap, which keeps it short of a 5 for a fully unannotated tool.

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?

Front-loaded with the core purpose, then cleanly sectioned into WHEN TO USE, RETURNS, and EXAMPLE blocks. The RETURNS detail is earned because no output schema exists, and the two example calls are compact and directly usable.

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 and no annotations, the description compensates fully: it documents return fields, metadata, defaults implied by the schema, auth scope, and sort order, and supplies two realistic invocations. An agent has everything needed to call it correctly without opening the schema.

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 description coverage is 100%, so the baseline is 3, but the description adds genuine meaning: it defines SAF as the 'ratio of actual-to-expected group attention' (making min_saf's semantics concrete) and shows venue_type and time_range in worked examples. It stops short of clarifying interactions between filters or the default 24-hour window.

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

Purpose4/5

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

States a specific verb+resource+scope: 'Query measured social attention events within your authorized screen and venue scope,' plus the filter dimensions and sort order. The word 'events' implicitly separates it from the sibling get_social_contagion_summary, but no sibling is named explicitly, so the differentiation is inferential rather than stated.

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 block gives four concrete scenarios (social proof moments, venue/daypart contagion, cascade patterns, SAF/VAS correlation), which is clear context. However, it offers no when-not conditions and never names an alternative tool such as get_social_contagion_summary or get_attention_metrics, so routing is left to inference.

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

All Trillboards DOOH Advertising toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources