Smasher Studio — AI Fashion Design

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is fully covered. The description adds that it is 'free to use,' which is a useful extra behavioral detail beyond annotations, and the verbose parameter behavior is partially implied. However, no deeper behavioral context is provided beyond the annotations.

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?

Two short sentences, front-loaded with the tool's main purpose and then a quick 'free to use' note. Zero 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?

There is an output schema, so return values are covered by structured data. For a read-only balance check with one optional boolean parameter, the description plus annotations are nearly complete. Slightly more context about what 'verbose' affects could be added, but it's not essential.

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%, so the schema fully documents the single 'verbose' parameter. The description adds the notion of monthly allocation but doesn't add meaning about the parameter itself beyond what the schema provides. Baseline 3 is appropriate.

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?

States a specific verb and resource: checks the user's credit balance, subscription plan, and monthly allocation. This clearly distinguishes it from siblings like check_video_status and the 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 doesn't explicitly name alternatives or exclusion conditions, but its scope is clear: it's the credit/plan check tool, distinguishable from the generation and status siblings. Adding an explicit 'when not to use' would be an improvement, so not a 5.

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?

The description adds useful behavioral context beyond the annotations: it mentions the async nature, that it returns the video URL when complete, and that it is free/no-credit. The annotations already declare readOnlyHint and idempotentHint, so the description supplements rather than repeats them.

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 short and front-loaded. Every sentence serves a purpose: what the tool does, when to call it, what it returns, and its cost profile. No filler or redundant restating of the title.

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?

With a single well-documented parameter, an output schema, and annotations covering safety, the description covers everything needed to invoke the tool correctly. The mention of status and video URL aligns with the output schema, and the job_id source is clearly indicated.

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% and the schema already describes job_id. The description adds meaning by specifying that the job_id is the one returned by generate_fashion_video, which helps the agent understand the parameter's origin and lifecycle.

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 specific action: checking the status of an async video generation job. It names the related tool generate_fashion_video and makes clear what the resource is, distinguishing it from sibling tools like check_credits or generate_fashion_image.

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 says to call this tool after generate_fashion_video using the returned job_id, providing clear sequencing. It doesn't discuss alternatives or when not to use it, but for a status-polling tool the usage context is well covered.

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?

Annotations only indicate the tool is not read-only, not idempotent, and not destructive. The description adds meaningful behavioral detail: it uses a multi-model image chain, has automatic fallback, and costs 4 credits per colorway. No contradiction with annotations is present.

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 compact and front-loaded with the core purpose, followed by cost and behavior details. The phrase 'best-in-class' is mild marketing fluff, but overall every sentence contributes useful information.

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 that there is no output schema, the description covers the essential selection and invocation context: purpose, colorway specialization, cost, and fallback behavior. It does not describe the exact return format or async behavior, but combined with the fully documented schema this is a reasonably complete definition.

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 schema already documents all six parameters clearly. The description adds only marginal colorway-related context, such as Pantone references and per-colorway credit cost, but does not meaningfully clarify parameters beyond the schema. Baseline 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 states a specific verb and resource: 'Generate color variants of a garment design.' It further differentiates itself with 'Pantone references' and 'multiple colors,' making it easy to distinguish from siblings like generate_fashion_image or generate_multi_angle.

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 the tool is for colorway generation, but it does not explicitly state when to use it versus alternatives or when not to use it. It provides useful context like credit cost and product shots, but no routing guidance among the sibling image-generation tools.

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?

The description discloses two useful behavioral traits: it uses a multi-model image chain with automatic fallback and costs 5 credits per fabric variant. However, it does not explain whether the call is synchronous, what the response contains, whether variants are returned together, or what fallback implies for the result. Annotations carry some context but do not fill these gaps.

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 short and front-loaded with the core purpose. The phrase 'best-in-class' is mild marketing fluff and could be removed, but the overall structure is efficient and all sentences contribute relevant information.

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?

With six parameters, no output schema, and no explanation of return values or execution behavior, the description leaves important gaps. An agent knows what the tool does and roughly what it costs, but not what a successful call returns, whether results are immediate, or how automatic fallback affects output.

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 input schema already fully documents all six parameters. The description adds the credit-cost-per-variant detail and clarifies that fabric names are expected in the fabrics array, but it does not substantially improve on the schema's parameter explanations.

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 identifies the tool's purpose: visualizing a garment design in different fabrics and materials, with concrete examples like cotton, silk, denim, and leather. This distinguishes it from color-focused or single-image generation tools, though it does not explicitly name a sibling tool for comparison.

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 the tool should be used when fabric/material variants are needed, but it gives no explicit when-to-use guidance, conditions, prerequisites, or comparisons with alternatives like generate_colorways or generate_fashion_image. An agent must infer selection from the purpose alone.

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?

Annotations are minimal (readOnlyHint false, openWorldHint true, etc.), but the description adds valuable behavioral context: cost per quality tier, Pro plan restriction for ultra, and mention of a multi-model image chain with automatic fallback. This goes beyond schema, disclosing pricing and internal behavior that an agent would need for resource planning. It does not mention auth or rate limits, but those are not critical for a generation tool.

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 with no fluff. It front-loads the core purpose, lists output types, and adds critical cost info. Every sentence earns its place, and the structure is efficient for agent consumption.

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 purpose, output types, and cost, but does not explain the return format (e.g., image URL) or error handling. Given the tool has 5 parameters, all documented in schema, and no output schema (so the description would need to explain return values), it is slightly incomplete but still adequate for calling the tool. The missing return format is a minor gap, but the cost and fallback details add 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 coverage is 100%, with all five parameters fully described including enums, defaults, and cost implications. The description adds little beyond what the schema already provides, only restating cost and fallback behavior. Since the schema carries the heavy lifting, a baseline of 3 is appropriate; the description does not introduce new param 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: generating AI fashion product photography, listing specific output types (product shots, on-model photos, flat lays, editorial). It distinguishes from siblings like generate_fashion_video and generate_colorways by focusing on image generation, though it does not explicitly name alternatives. This is clear and specific enough for an agent to identify the tool's role.

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 for fashion product photography but does not provide explicit when-to-use vs. when-not-to-use guidance. It does not mention alternatives or exclusions, such as 'use generate_fashion_video for video content.' Given the sibling tools are clearly different in domain, the lack of explicit routing is a minor gap, but context implies this is the image tool.

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?

Beyond the annotations (readOnlyHint false, idempotentHint false), the description reveals important behavior: the operation is asynchronous, returns a job_id immediately, requires polling, produces native audio, uses a fallback engine chain, and has credit costs tied to quality and duration. This gives an agent meaningful operational expectations.

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 focused sentences with no filler. It front-loads the core action and async workflow, then adds cost and reliability context without digressing.

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 an async tool with no output schema, the description sufficiently covers what the agent needs: the job submission model, immediate job_id return, polling via check_video_status, source image prerequisite, audio behavior, and credit costs. Combined with the rich schema, nothing essential 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?

Schema description coverage is 100%, so the schema already explains prompt, style, quality, duration, and source_image_url. The description adds a concise cost summary and mentions 'existing image,' but it does not materially extend parameter meaning 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 uses a specific verb and resource: 'Submit an async video generation job from an existing image.' It clearly distinguishes this from static image generation by emphasizing async submission and polling, and it names check_video_status as the follow-up, which helps an agent understand the tool's role.

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 clear usage context: video generation starts from an existing image, returns a job_id immediately, and requires polling via check_video_status. The schema adds that source_image_url comes from a previous generate_fashion_image result, implying the intended workflow, though it does not explicitly name exclusions or alternatives like generate_multi_angle.

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?

Annotations already cover the basic read/write/destructive profile. The description adds non-obvious behavioral context: a multi-model image chain with automatic fallback and a cost of 4 credits per angle. This goes beyond what annotations or schema supply, with no contradictions.

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?

Two front-loaded sentences carry the essential information: what the tool does, the consistency guarantee, the technical approach, and the credit cost. There is no filler or redundant restatement of the schema.

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 a fully described schema and helpful annotations, the description covers purpose, behavioral details, and cost. It stops short of describing the output format, though 'multi-angle views' makes the return type reasonably inferable. Overall it is complete enough for an agent to select and invoke the tool effectively.

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 every parameter already has a meaningful description including defaults, enums, and format examples. The tool description adds no additional parameter-level detail, which is acceptable under the baseline for complete schema coverage.

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 verb and resource: 'Generate coordinated multi-angle views of a garment' with concrete angle examples. It also differentiates from the sibling single-image tool generate_fashion_image by emphasizing coordinated, consistent multi-angle output.

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 establishes the use case: generating multiple coordinated garment angles with consistent styling. It does not explicitly name alternatives or exclusions such as 'use generate_fashion_image for a single view,' but the context is clear enough for an agent to route appropriately.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavior beyond the annotations by stating that authentication is required and clarifying the scope ('the user's design collections') and included counts. No contradiction with annotations.

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?

Two short sentences with no redundancy. The primary action and resource are front-loaded, and the authentication requirement is a necessary addition. Every word contributes to understanding the tool.

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 simple list operation with one optional parameter, an output schema, and strong safety annotations, the description covers all essentials: what is listed, whose collections, what metrics are included, and the authentication prerequisite. Nothing critical 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?

Schema description coverage is 100%: the single optional parameter `limit` is fully documented in the schema with its default behavior. The description does not add additional parameter semantics, so the baseline of 3 is appropriate; the schema already carries the needed meaning.

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 'List' with a clear resource ('the user's design collections') and specifies exactly what is returned ('session counts and asset counts'). This clearly distinguishes it from sibling tools like generate_* and check_credits, so an agent can correctly differentiate it without additional investigation.

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 conveys the core use case and notes that authentication is required, which is a useful prerequisite. However, it does not explicitly state when to use this tool versus alternatives or when not to use it. Usage is implied by the clear contrast with generation and status-check siblings, but no explicit routing is provided.

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