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Trillboards DOOH Advertising

register_partner

Register a new partner organization with Trillboards.

WHEN TO USE:

  • First-time setup for a new partner integration

  • Creating a new partner account to manage devices

RETURNS:

  • partner_id: Unique partner identifier

  • api_key: API key for authenticated requests (store securely!)

  • status: Account status

EXAMPLE: User: "Register my vending machine company" register_partner({ company_name: "Acme Vending Co", email: "tech@acmevending.com", industry: "vending", expected_devices: 50 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesContact email for the partner account
websiteNoCompany website URL (optional)
industryNoIndustry type (e.g., "vending", "retail", "hospitality")
company_nameYesCompany or organization name
contact_nameNoPrimary contact person name (optional)
contact_phoneNoContact phone number (optional)
expected_devicesNoEstimated number of devices to connect

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the transparency burden. It discloses return values (partner_id, api_key, status) and adds a security note ('store securely!') for the api_key. However, it omits other behavioral aspects like permissions, idempotency, or side effects, so it is only partially 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 well-structured and front-loaded: purpose, when-to-use, returns, and an example. Every section serves a distinct purpose, with no fluff or repetition. The example is concise and illustrative.

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?

For a 7-parameter tool with no annotations and no output schema, the description provides essential context: usage conditions, return fields, and an example. It does not cover edge cases like duplicate registrations or permission requirements, but the provided information is sufficient for basic usage and the schema fills the parameter-level gaps.

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 description coverage is 100%, so the baseline is 3. The example in the description demonstrates typical usage of some parameters (company_name, email, industry, expected_devices) but does not add new semantic meaning beyond the schema's existing parameter 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 'Register a new partner organization with Trillboards' with a specific verb and resource. It distinguishes from sibling tools like register_device by focusing on partner organizations and mentions 'manage devices' as a use case, making the purpose unambiguous.

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?

Provides explicit 'WHEN TO USE' conditions: first-time setup for new partner integration and creating a new partner account. It gives clear context for when to invoke the tool, though it does not explicitly name alternatives or exclusions, which would push it to 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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