agentic-commerce-catalog

agent_image

AI image generation from a text prompt. Generate album and single cover art, podcast and video thumbnails, social and campaign graphics, concept art, storyboard frames, product shots, and article illustrations. Returns one hosted still image as PNG or JPEG in square, portrait, or widescreen framing (1:1, 4:5, 9:16, 16:9, 21:9). Async by default: poll the returned pollUrl for imageUrl, or pass ?async=false to block. — $0.15 USDC via x402 on Base (chain 8453).

Input Schema

TableJSON Schema

Name	Required	Description
`prompt`	Yes	Image description — subject, style, composition.
`asyncMode`	No	Return immediately with a jobId while the render runs. Can only switch async on: false is the same as omitting it, so a paid call stays asynchronous; ?async=false is the only way to force a blocking call.
`aspectRatio`	No	Aspect ratio, e.g. '1:1', '16:9', '9:16'.

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations provided, the description carries the full disclosure burden and does so well. It reveals async-by-default behavior, the polling mechanism (pollUrl → imageUrl), the blocking alternative (?async=false), output format (one hosted PNG/JPEG), supported aspect ratios, and pricing/chain details ($0.15 USDC on Base). This gives the agent a reliable mental model of how the call behaves.

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 three dense sentences with no filler. It front-loads the core purpose first, then use cases, output behavior, async semantics, and cost. Every sentence contributes operational or selection-relevant information.

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?

The tool has no output schema and no annotations, so the description must supply essential invocation and return details. It covers purpose, output format, aspect ratios, async polling, blocking option, and cost. The only notable gap is how the x402 payment flow is initiated or handled, but the description provides enough for an agent to make the call and interpret the response.

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 coverage is 100%, so the baseline is 3. The description adds value beyond the schema by listing the full set of supported aspect ratios and reinforcing the async/blocking behavioral distinction. It does not deeply elaborate on prompt nuances, but the schema already describes prompt as 'subject, style, composition.'

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 a specific verb and resource: 'AI image generation from a text prompt.' It then enumerates concrete use cases (album art, thumbnails, concept art, product shots, etc.) that make the tool's scope unmistakable. The contrast with siblings agent_video and create_music is implicit but clear: this tool produces still images, not video or music.

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 description gives clear context for when to use the tool through its list of image-generation use cases. It does not explicitly exclude alternatives or name sibling tools for video/music, but the domain separation is evident enough that an agent can route correctly. No explicit 'when not to use' guidance is provided.

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

A4.4/5.0

Disambiguation5/5

Each tool targets a distinctly different media type: image, video, and music. There is no overlap or ambiguity between generating a still image, a video clip, or an original song.

Naming Consistency3/5

Two tools follow the agent_ prefix pattern (agent_image, agent_video) while the third uses create_music, mixing conventions. The names are still readable, but the pattern is not fully consistent.

Tool Count4/5

Three tools is a lean but reasonable set for a media generation server. Each tool covers a meaningful content type and earns its place, though the server could arguably include additional related creation tools.

Completeness4/5

The surface covers the core generation needs for image, video, and music content. Minor gaps exist, such as no editing or asset management tools, but the core lifecycle of generating and receiving media is complete.

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