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Trillboards DOOH Advertising

by io.github.snehdhruv

configure_sensing

Configure what a screen should sense using natural language. Generates and optionally pushes a sensing profile to the device.

Uses Gemini AI to interpret a natural language sensing intent and generate a sensing profile that maps to available on-device ML models (BlazeFace, AgeGender, FER+, MoveNet, YAMNet, WhisperTiny, EfficientDet, YOLOv8-nano).

WHEN TO USE:

  • Setting up a new screen to sense specific things (faces, vehicles, emotions, etc.)

  • Changing what a screen detects based on venue type or business needs

  • Configuring custom sensing for special events or campaigns

  • Translating business intent into ML model configuration

RETURNS:

  • data: The generated sensing profile with:

    • profile_name, profile_type, description

    • models: Array of ML model IDs to activate

    • classes: COCO classes to detect (for object detection models)

    • thresholds: Confidence and alert thresholds

    • observation_families: What types of observations will be produced

    • capture_interval_ms, report_interval_ms: Timing configuration

    • estimated_fps_impact: CPU cost estimate

    • data_fields_produced: All data fields the profile will generate

    • reasoning: Why these models/classes were chosen

    • deployment_status: 'generated' | 'pushed' | 'push_failed'

  • metadata: { screen_id, auto_deploy, profile_id }

  • suggested_next_queries: Follow-up actions

EXAMPLE: User: "Set up the lobby screen to detect foot traffic and emotions" configure_sensing({ screen_id: "507f1f77bcf86cd799439011", intent: "Detect foot traffic patterns, count people, and measure emotional reactions to displayed content", auto_deploy: false })

User: "Configure this drive-through screen for vehicle counting" configure_sensing({ screen_id: "507f1f77bcf86cd799439011", intent: "Count vehicles in drive-through lane, detect vehicle types, measure queue length", auto_deploy: true })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYesNatural language description of what the screen should sense/detect/measure
screen_idYesScreen ID (mongo ID) to configure sensing for
auto_deployNoIf true, automatically push the profile to the device. If false (default), generate only for review.

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the full burden. It does disclose the key mutation behavior: 'optionally pushes a sensing profile to the device' and includes deployment_status values (generated, pushed, push_failed). However, it stops short of explaining consequences like overwriting existing profiles, permission requirements, or reversibility of a push.

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 (overview, when to use, returns, examples) and front-loaded with a concise first sentence. While longer than the get_calls example, the length is justified by the tool's complexity and the detailed return fields. No extraneous fluff.

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?

Despite having no output schema, the description enumerates all return fields (profile details, metadata, deployment status, suggested queries) and provides two examples covering both auto_deploy outcomes. This gives the agent sufficient understanding of expected output and side effects for a complete interaction.

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% with each parameter described. The description adds value by providing concrete example intent strings and clarifying auto_deploy behavior through examples (false for review, true for direct push). This goes beyond the bare schema descriptions.

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 uses a specific verb 'configure' with a clear resource: 'what a screen should sense using natural language' and specifies the outcome (generates and optionally pushes a sensing profile). This clearly distinguishes it from the many read-oriented sibling tools like get_signals or get_device.

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 four concrete scenarios (setting up a new screen, changing detection, custom events, translating business intent), giving solid context for when to invoke this tool. However, it does not explicitly state when not to use it or mention alternatives, which prevents a top score.

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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