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Trillboards DOOH Advertising

get_device

Get detailed information about a specific device.

WHEN TO USE:

  • Checking status of a single device

  • Getting device configuration details

  • Debugging device issues

RETURNS:

  • device_id: Your internal device ID

  • trillboards_device_id: Internal Trillboards ID

  • fingerprint: Device fingerprint

  • name: Device name

  • status: online/offline

  • last_seen: Last heartbeat timestamp

  • location: Location details

  • specs: Device specifications

  • stats: Impression and earnings stats

EXAMPLE: User: "Get details for vending machine 001" get_device({ device_id: "vending-001-nyc" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
device_idYesYour internal device identifier

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries full responsibility. It does not disclose behavioral traits such as read-only nature, authorization requirements, error behavior for unknown device IDs, or rate limits. The return fields are listed, but operational behavior is not addressed.

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 (WHEN TO USE, RETURNS, EXAMPLE), each serving a purpose. It is appropriately sized for a simple getter tool, with no redundant information and key details front-loaded.

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?

Despite having no output schema, the description enumerates all return fields and provides a concrete example, making it sufficient for a single-parameter read tool. Minor gaps include lack of error handling info and no mention of the obvious sibling list_devices, but overall it is fairly complete.

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% and the schema description ('Your internal device identifier') aligns with the tool's return description. The DESCRIPTION adds a concrete example ('vending-001-nyc') but does not meaningfully extend the semantic meaning beyond what the schema already 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 'Get detailed information about a specific device' with a specific verb and resource, and the WHEN TO USE section adds concrete use cases (checking status, configuration, debugging). This distinguishes it from sibling tools like list_devices and get_device_ads.

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?

Explicit WHEN TO USE list provides clear context for when to invoke this tool. However, it does not mention when not to use it or name alternative tools (e.g., list_devices for multiple devices), 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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