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Trillboards DOOH Advertising

import_feedback_creative

Idempotent

Import an AI model’s image through native bytes, moderation and brand review. Provide exactly one of asset_url, source_creative_id or image_base64. Inline bytes require content_type. PNG, JPEG and WebP are staged through native ownership, actual MIME, byte-hash, brand review and moderation. The existing data_api/api_calls tariff applies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior4/5

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

Annotations cover the safety profile (readOnlyHint=false, idempotentHint=true, destructiveHint=false), so the description is not obligated to restate them. It adds material context beyond that: staging through native ownership, MIME and byte-hash handling, mandatory brand review and moderation, and the fact that the existing data_api/api_calls tariff applies — billing disclosure is exactly the kind of thing annotations cannot convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four tight sentences, front-loaded with the action and the exactly-one-of rule before the pipeline detail and cost note. Jargon ('staged through native ownership') is dense but each sentence carries information; nothing is padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, no declared parameters, and only minimal annotations, the description must carry most of the burden. It covers inputs, processing stages and billing, but omits what the caller gets back, whether moderation/brand review blocks or just flags, and what happens on duplicate byte-hashes despite the idempotent hint. Adequate, but there are visible gaps for an ingest tool.

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 declares zero properties, so the description is the only source for the three mutually exclusive input modes and the content_type requirement — that is real added meaning. However, it names parameters the schema does not expose, creating an inconsistency an agent calling this tool cannot resolve (it has nowhere to pass asset_url or image_base64). Useful content, undermined by the schema mismatch.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Import an AI model's image') and adds scope that separates it from siblings like generate_brand_creative, register_creative_variants or sync_creatives: this is a bytes-level ingest pipeline with moderation and brand review. It never explicitly names a sibling, so an agent must infer the boundary, but the purpose itself is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a real usage constraint ('Provide exactly one of asset_url, source_creative_id or image_base64', plus 'inline bytes require content_type'), which is genuine routing guidance for the input mode. It does not say when to prefer this tool over review_feedback_creative, sync_creatives or register_creative_variants, so alternative selection is left to inference.

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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