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Trillboards DOOH Advertising

by io.github.snehdhruv

get_content_recommendations

Get best-performing content recommendations for a venue type and optional time context.

WHEN TO USE:

  • Deciding what content to schedule at a specific venue type

  • Finding content that drives the highest audience engagement at a location

  • Optimizing content rotation by daypart (morning, afternoon, evening, overnight)

  • Content programming decisions based on performance data

RETURNS:

  • data: Array of recommended content ranked by performance score

    • videoId, title, contentCategory, durationSeconds

    • totalPlays, uniqueScreens

    • avgAttention (0-1), avgDwellMs

    • performanceScore (composite of attention, replay density, dwell time)

  • meta: { count, venue_type, daypart, limit }

Performance score formula: attention(40%) + replay_density(30%) + dwell_time(30%)

EXAMPLE: User: "What content works best in bars during the evening?" get_content_recommendations({ venue_type: "bar", daypart: "evening", limit: 10 })

User: "Best performing content for transit screens" get_content_recommendations({ venue_type: "transit", limit: 20 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum recommendations to return (default: 10, max: 100)
daypartNoOptional daypart filter
venue_typeYesVenue type to get recommendations for (required)

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 carries the full burden and does a solid job: it details the return structure, lists the performance score formula, and provides examples. It doesn't mention error cases, auth requirements, or edge cases like no matching venue_type, so it's not a 5.

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: overview, when to use, returns, and examples. Every sentence contributes meaningful information, and the examples are compact and useful.

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 lacking an output schema, the description fully specifies the return fields (data array with content details and metrics, meta object). Combined with examples and the performance formula, it gives the agent everything needed to invoke the tool 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 this is already well-documented. The description adds value by explaining daypart as 'time context', giving example calls, and clarifying the limit's role in the output size. This goes beyond the schema's basic 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 clearly states it returns best-performing content recommendations filtered by venue type and optional daypart. It uses a specific verb and resource, and the focus on recommendations distinguishes it from sibling tools like get_content_performance or search_content.

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 gives concrete scenarios: content scheduling, engagement optimization, daypart rotation, and programming decisions. However, it doesn't explicitly name alternatives or state when not to use this tool, stopping short of the highest 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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