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Trillboards DOOH Advertising

get_network_stats

Get network-wide statistics across all partner screens.

WHEN TO USE:

  • Getting a high-level overview of network performance

  • Checking how many screens are online

  • Reviewing total impressions and revenue estimates

RETURNS:

  • total_screens, online_screens

  • impressions, total_auctions

  • revenue_estimate_usd, avg_cpm, fill_rate

EXAMPLE: User: "How is my network performing this week?" get_network_stats({ time_range: "7d" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
time_rangeNoTime range for stats (default: 7d)

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It lists the return fields and gives an example, which is helpful, but it does not mention any potential side effects, authentication requirements, rate limits, or data freshness. For a simple read tool, the absence of such caveats is acceptable but not deeply 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 tightly structured with sections (WHEN TO USE, RETURNS, EXAMPLE). Every sentence earns its place, with no unnecessary verbiage. It uses clear headings and a realistic example, making it easy to scan.

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 having no output schema, the description explicitly lists all return fields, which is sufficient for an agent to understand what to expect. It also provides usage context and an example, covering the tool's simplicity comprehensively.

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 fully describes the only parameter (time_range) with enum values, default, and description (100% coverage). The description adds an example with time_range, but that is marginal value beyond the schema. Baseline 3 is appropriate.

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: 'Get network-wide statistics across all partner screens.' It specifies the verb 'get', the resource 'network stats', and the scope 'across all partner screens,' which distinguishes it from other get_* tools that target campaigns, devices, or analytics. The WHEN TO USE section further reinforces its purpose.

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 provides three explicit use cases: getting a high-level overview, checking online screens, and reviewing impressions/revenue. It gives clear context for when to invoke the tool but does not mention exclusions or alternative tools, 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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