Smasher Studio — AI Fashion Design

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

With no annotations, the description carries the behavioral transparency burden. It adds that the tool is 'Free to use' and indicates a read-only operation via 'Check,' but it does not disclose potential errors, rate limits, authentication needs, or return format details. The output schema covers return structure, but other behavioral traits are left unstated.

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

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

The description is two sentences with no filler. It front-loads the primary action and resources, then a terse cost note. Every word contributes meaning, making it efficient and appropriately sized.

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

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

Given the tool's simplicity (one optional parameter, output schema present), the description covers the essential purpose and cost. It does not provide usage context or explicit read-only confirmation, but for a basic balance check, the information is sufficient to guide an agent. A slight deduction for lacking any alternative guidance.

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

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

Schema description coverage is 100%, and the single parameter 'verbose' is fully documented in the input schema with its own description. The tool description does not add parameter details, but the baseline of 3 applies since the schema already provides complete parameter semantics.

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

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

The description clearly states the tool's purpose with a specific verb ('Check') and resource ('user's current credit balance, subscription plan, and monthly allocation'). This distinguishes it from sibling tools like 'generate_fashion_image' or 'check_video_status', which have different functions.

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

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

The description provides no guidance on when to use this tool versus alternatives. It only notes that it is 'Free to use,' which is a cost attribute, not a usage condition. No exclusions or alternative tools are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Since no annotations exist, the description carries the full burden. It discloses the asynchronous nature, that it returns status and video URL when complete, and that it is free. It could add more on error handling or polling behavior, but for a simple status check it is sufficiently transparent.

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

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

The description is two sentences, front-loaded with the purpose, and includes usage, expected output, and cost without redundancy. Every word adds value.

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

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

For a tool with one parameter and an output schema, the description is complete. It tells the user when to call, what to pass, what to expect, and the cost. No critical information is missing.

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

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

The schema already provides 100% coverage for job_id with a clear description: 'The job_id returned by generate_fashion_video.' The description reinforces this but adds no new semantic meaning, so the baseline score of 3 applies.

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

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

The description clearly states the tool checks the status of an async video generation job, using a specific verb and resource. It distinguishes itself from sibling tools by explicitly referencing generate_fashion_video and the returned job_id.

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

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

It explicitly instructs to call this tool after generate_fashion_video with the returned job_id, providing clear when-to-use guidance. It also notes the tool is free, which is relevant for usage decisions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full behavioral burden. It discloses meaningful details: 'Uses a best-in-class multi-model image chain with automatic fallback' and 'Costs 4 credits per colorway,' which convey technical implementation and cost implications. It does not mention side effects like generation time or failure modes, but for a generation tool, the disclosed info is strong.

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

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

The description is three sentences, each providing distinct value: purpose, output style, and technical/cost details. It is front-loaded with the core action and avoids redundant phrasing. No wasted words.

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

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

The tool has 6 parameters, no output schema, and moderate complexity. The description explains the overall function and cost, but does not explicitly describe the return value (e.g., image URLs, a gallery, or a status object). It also does not mention the max of 8 colorways, though that is in the schema. Given the lack of output schema, the missing return format is a notable gap.

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

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 description adds little beyond the schema: it mentions 'Pantone references' and 'per colorway' cost, but does not clarify parameter syntax or relationships. The schema itself provides thorough descriptions (e.g., 'prompt WITHOUT color'), so the description's contribution is marginal.

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

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

The description clearly states the tool's function: 'Generate color variants of a garment design' with a specific outcome ('Creates product shots in multiple colors with Pantone references'). This distinguishes it from siblings like generate_fashion_image (single image) and generate_fabric_sim (fabric simulation), 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.

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

The description gives a clear context for use (when you want colorway variants of a garment) but does not explicitly mention alternatives or exclusions. It says 'Generate color variants of a garment design' but fails to say 'use this instead of generate_fashion_image when you need multiple colorways' or similar guidance, so the agent lacks direct when-to-use vs. sibling guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the burden. It discloses the internal multi-model image chain with automatic fallback and the cost of 5 credits per fabric variant, but it omits output format, side effects (e.g., whether it modifies anything), or auth requirements.

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

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

Three sentences, each earning its place: purpose, technical behavior, and cost. The phrase 'best-in-class' is marketing filler but does not undermine conciseness.

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

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

The description gives purpose, cost, and a hint about internal fallback, but without an output schema it fails to explain what the tool returns (e.g., image URLs). It also does not mention prerequisites like checking credits, even though a sibling tool exists for that.

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

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

Schema coverage is 100%, with all six parameters fully described in the input schema. The description only restates fabric examples and cost, adding no meaningful parameter semantics beyond what the schema already provides.

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

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

The description opens with a specific verb ('Visualize') and resource ('a garment design in different fabrics and materials'), clearly distinguishing this from sibling tools like generate_colorways (colors) and generate_multi_angle (angles). The purpose is unambiguous and front-loaded.

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

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

The use case is clear: when you want to see a garment in various fabric finishes, this tool is for you. However, it does not explicitly mention alternatives or when not to use it, though the sibling list makes the differentiation obvious.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the behavioral transparency burden. It adds meaningful context: 'multi-model image chain with automatic fallback' and 'costs 5 credits per image', which go beyond basic generation semantics. However, it omits details like output format or processing time.

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

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

The description is two sentences, front-loaded with the core purpose, and every phrase adds value. The second sentence includes important operational details (cost, fallback) without unnecessary fluff.

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

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

The schema is rich and the purpose is clear, but there is no output schema and no annotations. The description does not explain the return format, expected latency, or prerequisites beyond credit cost. It is adequate but leaves gaps in operational context.

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

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 description does not add parameter-specific meaning beyond the schema's already detailed descriptions for style, prompt, quality, background, and aspect_ratio.

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

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

The description uses a specific verb ('Generate') followed by a clear resource ('AI fashion product photography') and enumerates concrete output types (product shots, on-model, flat lays, editorial). This distinguishes it from sibling tools like generate_fashion_video or generate_colorways.

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

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

The description implies use for fashion product photography but provides no explicit when-to-use or when-not-to-use guidance compared to siblings such as generate_multi_angle, generate_colorways, or generate_fabric_sim. No alternatives or exclusions are named.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden and does well by disclosing asynchronous behavior, immediate job_id return, native audio, automatic engine fallback for reliability, and cost dependence on quality/duration. It lacks specifics on failure modes or the fallback chain's exact behavior, but covers the core operational traits.

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

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

The description is four concise sentences, each adding essential information: purpose, workflow/async polling, reliability features, and cost structure. Every sentence earns its place with no filler or redundancy.

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

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

The description combined with a fully self-describing schema (5 params, enums, defaults) provides comprehensive guidance for invoking the tool: input requirements, async flow, follow-up polling, and pricing. It could mention the prerequisite of a previous generate_fashion_image result, but that is already in the schema's source_image_url description, so the lack is minor.

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

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 description's mention of costs for 720p/1080p partially duplicates the schema's own parameter descriptions, adding only a summarized pricing overview rather than new semantic meaning beyond the schema.

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

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

The description clearly states 'Submit an async video generation job from an existing image', providing a specific verb and resource that distinguishes it from sibling tools like generate_fashion_image and check_video_status. It precisely captures the tool's primary function.

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

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

The description provides clear usage context by explaining the async pattern: it returns a job_id immediately and instructs to call check_video_status for completion. However, it does not explicitly contrast with alternatives like generate_multi_angle or state when not to use, though the input requirement 'from an existing image' implies its applicability.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the burden of behavioral disclosure. It adds useful context about using a multi-model image chain with automatic fallback and the cost of 4 credits per angle, which are not visible in the schema. However, it does not describe potential delays, failure modes, or side effects.

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

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

The description is three sentences, each carrying distinct value: what it does, how it works (model chain), and cost. No fluff or repetition of schema fields, making it concise and well-structured.

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

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

The description covers the tool's core purpose, mechanism, and cost, and the schema fully documents all parameters. It does not explain the return format (no output schema exists), but for a generation tool that likely returns standard image results, the given context is sufficient for an agent to invoke it correctly.

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

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 description's examples of angles ('front, back, side, etc.') add little beyond the schema's enum, and it does not explain parameter interactions or formatting beyond what the schema already states. It adds no significant parameter semantics.

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

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

The description clearly states the tool's action ('Generate coordinated multi-angle views of a garment') and specifies the resource (garment) and angle types (front, back, side, etc.). It also mentions consistent style, which distinguishes it from generic image generation tools.

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

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

The description clearly indicates when to use the tool (when multi-angle views are needed) and emphasizes coordination, but it does not explicitly name alternative tools or provide when-not-to-use guidance. The context is clear, yet exclusions or comparisons with siblings are absent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden. It discloses that authentication is required, which is useful. However, it does not mention whether the operation is read-only, what happens without auth, or any pagination/rate-limit behavior. For a list operation, 'list' implies read-only, but more explicit context would be better.

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

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

The description is two sentences, with the core purpose in the first sentence and a critical prerequisite in the second. Every word earns its place, and it is front-loaded with the primary action.

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

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

The tool has low complexity (one optional parameter), an output schema exists to document return values, and the description covers authentication and the included metrics. It lacks some details like pagination, but for a simple list operation, this is nearly complete.

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

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

The input schema has 100% description coverage for the single 'limit' parameter, so the baseline of 3 applies. The description does not add any extra meaning beyond what the schema already provides, but it also does not need to given the schema's clarity.

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

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

The description states a specific action ('List') and a specific resource ('the user's design collections'), and further specifies the returned information ('session counts and asset counts'). This clearly distinguishes it from the sibling tools, which are all generate/check operations.

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

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

The description implies usage by stating what it does, but it does not explicitly say when to use this tool versus alternatives. There is no mention of when not to use it, and while siblings are clearly different, no direct guidance or comparison is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.