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Trillboards DOOH Advertising

by io.github.snehdhruv

get_audience_lookalike

Find screens with similar audience profiles using pgvector similarity.

Uses 64-dimensional audience vectors with HNSW cosine similarity index to find screens whose audience demographics, attention, and behavioral patterns match a target screen.

WHEN TO USE:

  • Expanding campaign reach to screens with similar audiences

  • Finding new inventory that matches a high-performing screen

  • Building lookalike audience segments for targeting

RETURNS: Array of similar screens ranked by cosine similarity, each with:

  • screen_id, similarity (0-1), metadata (face_count, attention, income, lifestyle), last_seen

EXAMPLE: get_audience_lookalike({ screen_id: "scr_abc123", limit: 10, min_similarity: 0.8 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results (default: 20, max: 100)
countryNoFilter by country (optional)
screen_idYesSource screen ID to find lookalikes for
venue_typeNoFilter by venue type (optional)
min_similarityNoMinimum cosine similarity threshold 0-1 (default: 0.7)

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It explains the underlying algorithm (pgvector, HNSW cosine similarity) and return format, but it does not disclose edge-case behavior, error handling, or side effects. Since this is a read-only lookup, it is acceptable but not fully transparent.

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 clear sections for purpose, technical details, usage scenarios, return fields, and an example. Every sentence adds value, and the key functionality is front-loaded in the first line.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is quite complete for a read tool with 5 parameters and no output schema. It explains the return structure, similarity semantics, and gives a usage example. It lacks detail on filtering behavior for country/venue_type, but the schema descriptions cover that.

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 input schema already covers 100% of parameters with descriptions, so the baseline is 3. The description adds an example that demonstrates a typical invocation, but it does not enrich parameter semantics beyond what the schema provides.

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 function: finding screens with similar audience profiles using pgvector similarity. It distinguishes itself from sibling tools like semantic_audience_search by focusing on lookalike audience matching for campaign expansion and targeting.

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 clear scenarios (expanding reach, finding similar inventory, building lookalike segments). It does not explicitly mention when not to use or name alternative tools, but the guidance is concrete and actionable.

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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