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Trillboards DOOH Advertising

export_dataset

Export observation data as a structured dataset. Supports filtering by time, geography, venue type, and observation family. Applies k-anonymity (k=5) to protect individual privacy.

Queries the relevant table based on the selected dataset type, applies filters, enforces k-anonymity by suppressing groups with fewer than 5 observations, and returns structured data.

WHEN TO USE:

  • Exporting audience data for external analysis

  • Building datasets for machine learning or reporting

  • Getting structured vehicle or commerce data for a specific time/place

  • Creating cross-signal datasets for correlation analysis

RETURNS:

  • data: Array of dataset rows (schema varies by dataset type)

  • metadata: { row_count, k_anonymity_applied, export_id, dataset, filters_applied, time_range }

  • suggested_next_queries: Related exports or analyses

Dataset types:

  • observations: Raw observation stream data (all families)

  • audience: Audience-specific data (face_count, demographics, attention, emotion)

  • vehicle: Vehicle counting and classification data

  • cross_signal: Pre-computed cross-signal correlation insights

EXAMPLE: User: "Export audience data from retail venues last week" export_dataset({ dataset: "audience", filters: { time_range: { start: "2026-03-09", end: "2026-03-16" }, venue_type: ["retail"] }, format: "json" })

User: "Get vehicle data near geohash 9q8yy" export_dataset({ dataset: "vehicle", filters: { time_range: { start: "2026-03-15", end: "2026-03-16" }, geo: "9q8yy" } })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoExport format (default: json). Currently only JSON is supported.
datasetYesType of dataset to export
filtersYesFilters to apply to the export

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations available, the description carries the full burden and does well by disclosing k-anonymity enforcement (suppressing groups under 5), the query process, and the return structure. It does not mention potential rate limits or permission requirements, but the provided behavioral details are substantial and valuable.

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 and front-loaded purpose. It is somewhat long, but every section (filters, when-to-use, returns, dataset types, examples) adds necessary value. No filler, though slightly more verbose than the absolute minimum.

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 there is no output schema and no annotations, the description fully compensates by explaining return fields (data, metadata, suggested_next_queries), dataset type semantics, filtering capabilities, and k-anonymity behavior. It also provides examples for common use cases, making it complete and self-contained.

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 baseline is 3. The description adds meaningful context beyond the schema by explaining each dataset type, giving filter usage examples, and noting that time_range is required for certain dataset types. This enriches parameter understanding and justifies a 4.

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 exports observation data as a structured dataset, with a specific verb and resource. It also lists filtering dimensions and dataset types, effectively distinguishing it from sibling tools like query_observations or get_analytics.

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 provides explicit contexts like exporting audience data for external analysis, building ML datasets, or creating cross-signal datasets. However, it does not explicitly name alternative tools or state when NOT to use this tool, so it falls short of a 5.

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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