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Trillboards DOOH Advertising

by io.github.snehdhruv

get_content_performance

Get performance metrics for a video across the Trillboards DOOH network.

WHEN TO USE:

  • Checking how a specific video performs across screens (plays, attention, audience size)

  • Analyzing which venue types and dayparts a video resonates best in

  • Finding the top-performing screens for a piece of content

  • Comparing content performance over different time windows

RETURNS:

  • videoId, title, totalPlays, uniqueScreens

  • avgAttention (0-1), avgAudienceSize, avgDwellMs

  • venueDistribution: Array of { venue_type, plays }

  • daypartDistribution: Array of { daypart, plays }

  • topScreens: Top 10 screens by play count with attention scores

  • period: { start, end } date range

EXAMPLE: User: "How is video dQw4w9WgXcQ performing on retail screens?" get_content_performance({ video_id: "dQw4w9WgXcQ", venue_type: "retail", days: 30 })

User: "Show me the last 7 days of performance for this video" get_content_performance({ video_id: "abc123xyz", days: 7 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLookback window in days (default: 30, max: 90)
video_idYesYouTube video ID to query performance for
venue_typeNoOptional venue type filter (e.g., "retail", "transit", "bar")

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 transparency burden and does well by detailing the exact return fields, including venueDistribution, daypartDistribution, and topScreens, plus examples of calls. It does not disclose edge-case behavior or potential errors, but the output structure is thoroughly specified.

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 when to use, return fields, and examples. Every sentence serves a purpose, and the main verb and resource are front-loaded. It is appropriately sized for the tool's complexity.

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?

Given there is no output schema, the description compensates well by listing all return fields and providing two examples. It lacks some edge-case details (e.g., behavior for unknown video IDs) but is largely complete for a read-only analytics tool with three parameters.

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% with descriptions for all three parameters, so the baseline is 3. The description's examples illustrate parameter usage (e.g., venue_type: 'retail', days: 30) and add a bit of context, but it does not significantly deepen the semantic understanding beyond the schema.

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 gets performance metrics for a specific video across the Trillboards DOOH network, listing specific metrics like plays, attention, and audience size. It distinguishes itself from sibling tools by focusing on video-level performance with venue/daypart breakdowns and top screens.

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 lists four concrete scenarios, such as checking video performance across screens and analyzing venue/daypart resonance. It provides clear context for when to use the tool, though it does not explicitly mention when not to use it or name direct alternatives.

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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