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Trillboards DOOH Advertising

list_devices

List all devices registered to the partner account.

WHEN TO USE:

  • Getting an overview of all connected devices

  • Finding devices by status (online/offline)

  • Auditing the device fleet

RETURNS:

  • devices: Array of device objects

  • total: Total device count

  • online_count: Number of online devices

  • offline_count: Number of offline devices

EXAMPLE: User: "Show me all my online devices" list_devices({ status: "online", limit: 50 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of devices to return (default: 50, max: 100)
offsetNoPagination offset
statusNoFilter by device status
device_typeNoFilter by device type

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It details the return structure (devices, total, counts) and implies a read-only operation, but it does not mention potential side effects, permissions, or rate limits. Given the read-only nature is self-evident and the return format is disclosed, this is solid but not exhaustive.

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 efficiently structured with distinct sections: purpose, when-to-use, returns, and an example. Every section adds value, and the main purpose is front-loaded in the first sentence. No wasted words.

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 and annotations, the description provides a clear return structure, example invocation, and usage scenarios. This compensates well for the missing structured context, making the tool's behavior and expected output fully understandable for an agent.

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 schema already describes all four parameters with 100% coverage, so the description adds limited new semantics. It provides an example showing 'status' and 'limit' usage, which slightly enriches understanding, but the schema remains the primary source of parameter meaning. 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 opens with 'List all devices registered to the partner account,' which is a specific verb+resource statement. It clearly distinguishes itself from siblings like get_device (single device) and delete_device by focusing on listing all devices with optional filters.

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 explicitly lists three use cases (overview, finding by status, auditing). It provides clear context for when to invoke the tool, though it does not explicitly mention alternatives or when not to use it, so it stops short of a full 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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