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Runs once, in the background: writes a hook-first 15-second script from what is working in the product's niche, makes an AI presenter (holding the product when its photo is given in image or image_link) and a vertical phone-style video of them presenting it, lip-synced, with a caption. The presenter is AI-made and never claims to be a customer. product: the product and offer. presenter: woman or man (optional). About $1.50 a video. The same request again returns its progress or the finished video.

Input Schema

NameRequiredDescriptionDefault
imageNo
productYes
presenterNo
image_linkNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

Beyond the annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=false) the description discloses genuinely valuable behavior: asynchronous background execution, an approximate price of $1.50 per video, the repeat-call/polling semantics, and a disclosure policy that the presenter is AI-made and never claims to be a customer. The one tension is that 'the same request again returns its progress' reads as dedupe/idempotent behavior while idempotentHint is false; it is not a hard contradiction, but the mismatch costs it a point.

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?

It is a single dense paragraph with the most important fact (background, one-shot run) front-loaded, and every sentence carries information about output, cost, or parameters. It is slightly overloaded, mixing artifact description, pricing, polling semantics, and parameter glossary in one block, which would read better split into short labeled parts.

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?

For a paid, asynchronous, generative tool with no output schema and 0% schema coverage, the description covers a lot: cost, async behavior, polling, disclosure, and parameter meaning. It is still incomplete on what the return value actually looks like (video URL? asset handle?), how long generation typically takes, and what happens if generation fails or the image is unusable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden, and it largely does: product is 'the product and offer', presenter is 'woman or man (optional)', and image/image_link are explained as the product photo the presenter holds. The gap is that image is an untyped free-form object in the schema and the description never says what shape it takes or how it differs from image_link.

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?

The description names a concrete artifact with concrete attributes: a hook-first 15-second script, an AI presenter lip-synced in a vertical phone-style video, with a caption. That is far more specific than the name alone. It stops short of a 5 because it never distinguishes itself from close siblings such as product_ad_pack, trend_to_video, or make_media, so the agent must infer which generator to pick.

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?

Useful operational guidance is present: it 'runs once, in the background' and 'the same request again returns its progress or the finished video', which tells the agent how to poll. However, there is no explicit when-to-use or when-not-to-use guidance against the many sibling generators, and no prerequisites such as whether a product image is required for the presenter to hold it.

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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