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Trillboards DOOH Advertising

by io.github.snehdhruv

query_observations

Query the universal observation stream using natural language or structured filters. Returns multi-modal sensing data (audience, vehicle, environment, commerce) from physical-world observations across the screen network.

WHEN TO USE:

  • Exploring raw observation data from edge AI sensors on screens

  • Filtering observations by venue type, device, time range, or geography

  • Getting audience, vehicle, environment, or commerce observation data

  • Answering natural language questions about what screens are sensing

RETURNS:

  • data: Array of observation objects with device, venue, payload, confidence, model versions

  • metadata: { observation_count, time_range, coverage_pct, model_versions }

  • suggested_next_queries: Contextual follow-up queries

Each observation includes:

  • observation_id, device_id, screen_mongo_id, venue_type

  • observed_at: Timestamp of the observation

  • observation_family: audience | vehicle | environment | commerce

  • payload: JSONB with model outputs (face_count, emotion, vehicle_count, etc.)

  • confidence: Model confidence score (0-1)

  • evidence_grade: Quality grade of the observation

  • model_versions: Which ML models produced this data

EXAMPLE: User: "Show me audience observations at QSR venues in the last hour" query_observations({ query: "audience observations at QSR venues", filters: { observation_family: ["audience"], venue_type: ["restaurant_qsr"], time_range: { start: "2026-03-16T14:00:00Z", end: "2026-03-16T15:00:00Z" } }, limit: 50 })

User: "What are screens sensing right now?" query_observations({ query: "latest observations from all screens", limit: 20 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum observations to return (default: 100, max: 1000)
queryYesNatural language query describing what observations to find
filtersNoStructured filters to narrow results

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 and does well by disclosing the return structure (data array, metadata, suggested_next_queries) and the content of each observation. It implies a read-only query operation but does not explicitly state non-mutating behavior or potential costs, so it earns a 4 rather than 5.

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 (WHEN TO USE, RETURNS, EXAMPLE) and uses bullet points for readability. It is relatively long but every section contributes value; it could be trimmed slightly without losing information, so a 4 is appropriate.

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 no output schema, the description thoroughly explains return values including observation fields, metadata, and suggested queries. It also provides concrete examples covering both natural language and structured filter usage, making the tool's behavior understandable in context.

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?

Schema coverage is 100%, so all parameters already have meaningful descriptions. The description adds examples that demonstrate parameter usage but no new semantic detail beyond what the schema provides, aligning with the baseline of 3.

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 queries the universal observation stream using natural language or structured filters, returning multi-modal sensing data. This distinctively differentiates it from siblings like semantic_search_observations and get_signals by emphasizing the raw observation stream and broad sensor families.

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 explicitly lists four scenarios, providing clear guidance on when to invoke this tool. However, it does not explicitly mention when not to use it or name alternatives, just missing the full 'when-not/alternatives' bar.

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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