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Trillboards DOOH Advertising

by io.github.snehdhruv

predictive_query

Generate predictive insights from observation patterns. Predict whether a venue is likely to see increased foot traffic based on current patterns.

Uses historical observation_stream data to compute trend analysis via linear regression on time-bucketed metrics. Generates predictions with confidence intervals based on the observed trend, variance, and sample size.

WHEN TO USE:

  • Predicting future audience patterns at a venue or screen

  • Forecasting foot traffic trends for campaign planning

  • Understanding whether metrics are trending up, down, or stable

  • Making data-driven decisions about inventory and pricing

RETURNS:

  • prediction: The predicted trend and expected values

    • trend: 'increasing' | 'decreasing' | 'stable'

    • current_avg: Current average metric value

    • predicted_avg: Predicted average over the time horizon

    • change_pct: Expected percentage change

    • confidence_interval: { lower, upper } bounds

  • confidence: Overall prediction confidence (0-1)

  • supporting_data: Recent data points that inform the prediction

    • data_points: Array of { bucket, avg_value, sample_count }

    • total_observations: Total observations analyzed

  • methodology: Description of the prediction approach

  • suggested_next_queries: Follow-up queries to refine the prediction

EXAMPLE: User: "Will this QSR venue see more foot traffic next week?" predictive_query({ question: "Will foot traffic increase at QSR venues?", venue_type: "restaurant_qsr", time_horizon: "7d" })

User: "Predict audience attention trends for this screen" predictive_query({ question: "What will audience attention look like?", screen_id: "507f1f77bcf86cd799439011", time_horizon: "3d" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesNatural language question about the predicted trend or outcome
screen_idNoFilter predictions to a specific screen (mongo ID). Optional.
venue_typeNoFilter predictions to a specific venue type. Optional.
time_horizonNoHow far ahead to predict (e.g., "1d", "3d", "7d", "14d"). Default: "7d", max: "30d"

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 fully carries transparency. It explains the underlying methodology (linear regression on time-bucketed metrics), describes confidence intervals, supporting data, and return structure, and notes it uses historical observation_stream data. It doesn't mention limitations like minimum data requirements, but for a non-destructive query tool this is sufficient.

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-structured with Purpose, WHEN TO USE, RETURNS, and EXAMPLE sections. Every section adds distinct value, and the content is front-loaded with the core purpose. While lengthy, it is appropriately detailed for a predictive tool with no output schema.

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 no output schema, the RETURNS section thoroughly enumerates the response structure, including nested objects like confidence_interval and supporting_data. The examples cover both venue_type and screen_id filters. This makes the description highly complete for an agent to use correctly.

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 100%, so the baseline is 3. The description adds value by showing two realistic example calls that illustrate how to phrase 'question' and combine optional filters. It also clarifies the 'time_horizon' format in the example even though the schema already documents it.

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 opens with a specific verb 'Generate predictive insights from observation patterns' and explicitly states it predicts whether a venue will see increased foot traffic. The 'WHEN TO USE' section further distinguishes it from sibling prediction tools by focusing on trend forecasting for audience patterns and campaign planning.

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 scenarios, making the intended use clear. However, it does not explicitly name alternative tools or state when not to use this tool, so it stops short of the highest standard.

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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