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Trillboards DOOH Advertising

by com.trillboards
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get_audience_forecast

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 })

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "screen_id": "<screen_id>",
      "hour": "<hour>",
      "day": "<day>"
    },
    "name": "get_audience_forecast"
  }
}

Input Schema

NameRequiredDescriptionDefault
dayYesDay of week (0=Sunday, 1=Monday, ..., 6=Saturday)
hourYesHour of day (0-23)
screen_idYesScreen ID to forecast
lookback_daysNoDays of historical data to use (default: 30)

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?

With no annotations, the description carries the full burden of behavioral disclosure. It explains that the tool uses historical audience data to predict typical patterns, and it discloses details about confidence based on sample count and the meaning of sample_count. This gives the agent useful context about how the prediction works and its reliability.

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 clear sections (purpose, when to use, returns, example). It is longer than minimal, but every section adds value, including the example that ties parameters to a realistic use case. No unnecessary repetition.

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 lack of an output schema, the description thoroughly explains the return fields (predicted_face_count, predicted_attention, typical_income, typical_lifestyle, confidence, sample_count). Combined with the example and the schema's parameter coverage, the description gives the agent everything needed to invoke the tool correctly and interpret results.

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?

The schema already documents all four parameters with clear descriptions (day, hour, screen_id, lookback_days) and 100% coverage. The description adds no additional parameter meaning beyond a concrete example, which is helpful but not beyond the schema's baseline.

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 tool's specific function: 'Predict what the audience will look like at a screen at a specific time.' It uses a precise verb (predict) and resource (audience at a screen/time), and the mention of historical data distinguishes it from related tools like get_live_audience.

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 use cases: planning campaigns, estimating audience composition, and comparing times of day. It gives explicit context for when to use the tool, though it does not mention when not to use it or directly name alternative tools.

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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