YouCam for Fashion & Retail

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

The description adds the gender requirement and the 'ask if missing' behavior, which is useful. However, it also claims 'visualize products instantly through camera', which could mislead since the tool actually accepts image URLs or file IDs, not a live camera feed. It also does not disclose the async/polling behavior or what the output image represents, so transparency is limited.

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 opens with unnecessary marketing fluff ('AR makes luxury bag shopping a tangible experience!...') and contains a typo ('through camera.gender must be...'). The key usage instruction is buried at the end, so the description is not front-loaded and contains 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 a complex request schema with multiple oneOf combinations of URL/file IDs, plus a polling parameter. The description does not explain these variants, the meaning of src_file_url vs ref_file_url, or the polling behavior. It only addresses gender handling. Given the complexity, the description is incomplete for an agent to use 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?

The schema already documents each parameter, but the description adds a critical rule not present in the schema: 'gender must be explicitly provided by the user. If missing, ask the user for clarification and set the parameter once received.' This provides actionable meaning beyond the schema's simple 'Gender of the person in the image.'

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 mentions 'handbags' and 'visualize products', which suggests a virtual try-on for bags, but it is phrased as marketing ('AR makes luxury bag shopping a tangible experience') rather than a clear functional statement. It does not explicitly state that the tool takes a user photo and a bag image to produce a composite, leaving the exact operation vague.

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 only usage guidance is the instruction that 'gender must be explicitly provided by the user. If missing, ask the user for clarification and set the parameter once received.' This is a parameter-level workflow hint but there is no guidance on when to use this tool versus sibling try-on tools, nor any exclusions or prerequisites for tool selection.

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 is entirely marketing ('great shopping satisfaction', 'compelling shopping flow') and discloses no behavioral traits such as whether the operation is asynchronous, what it modifies, or how results are returned. Annotations indicate it is not read-only, but the description adds no context beyond that, leaving the agent without 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 a verbose, unstructured marketing paragraph full of superlatives ('hyper-realistic', 'effortless yet high-quality'). It is not front-loaded with the core function; instead, it leads with sales language. Every sentence could be removed without any loss of operational information, making it the opposite of concise.

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 a highly complex input schema with nested objects, multiple file source combinations, and bracelet-specific tuning parameters, but the description provides only vague business value. It gives no sense of what inputs are required, what the output looks like, or how the try-on process works. Even with an output schema present, this one-paragraph pitch is entirely inadequate for the tool's complexity.

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 description never mentions any parameters, and the vague claim 'Only One 2D Image Needed' does not explain the complex `request` object with its four variants (URLs vs File IDs, masks, bracelet-specific parameters). With schema description coverage at 50%, the description fails to compensate for the undocumented half, providing no help in understanding parameter relationships.

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 is a promotional pitch ('The Ultimate AI Bracelet Virtual Try-On', 'Employ AI-powered solutions') that never states a concrete action such as 'generates a bracelet try-on image from a source photo and a reference product image.' The only specific detail, 'Only One 2D Image Needed,' hints at an input requirement but does not clarify what the tool actually does. It also fails to distinguish this from sibling tools like necklace or earrings try-on.

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 over alternatives. There is no mention of prerequisites, such as needing a selfie and a bracelet image, nor any indication of when this tool is appropriate. Given many similar sibling VTO tools, this lack of differentiation is a critical omission.

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 disclose the mutation/open-world profile (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false), so the description needn't restate safety. It does add genuinely useful output-handling behavior—the exact response.data.results.url must be preserved verbatim—but is silent on the async task lifecycle, cost implications, and prerequisite file uploads.

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?

Four sentences of promotional filler precede the single actionable instruction, which is buried at the very end of the description. The critical URL-preservation rule is not front-loaded and the marketing text does not earn its place in an agent-facing tool spec.

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?

An output schema exists, so return-value explanation is not required, but for a task-based image pipeline the description omits the async/polling model, the dependency on the upload_file/File-Upload sibling for file IDs, and any cost or timeout expectations. The one behavioral instruction it does give (preserve the URL) is useful but isolated.

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 only 50%, so the description is expected to compensate, and it does not: neither `polling` nor the `request` anyOf variants/`src_file_id` vs `src_file_url` selection is explained. 'Upload any clothing reference' hints at image input but adds no detail beyond the schema's own descriptions.

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 functional core—'Upload any clothing reference to swap outfits with you photo for an instant virtual wardrobe transformation'—states a verb and resource, so an agent can tell it performs garment try-on. However, the first four sentences are marketing copy that largely restate the title ('AI Clothes is a virtual fitting room that lets users try on clothes'), and nothing distinguishes it from the ten near-identical siblings (Shoes, Bag, Hat, etc.) beyond the implicit 'clothes' in the name.

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 only contextual guidance is 'It's perfect for online shopping, style inspiration, or just playing around with fashion ideas,' which describes consumer use cases rather than agent decision criteria. There is no mention of when to prefer this over AI-Shoes-Virtual-Try-On, when to supply file URLs versus file IDs, or how polling should be used.

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=false, destructiveHint=false, idempotentHint=false and openWorldHint=true, so basic safety is covered. The description adds no behavioral context beyond marketing claims: it does not explain the async task model implied by the poll param, that the call consumes credits, or that files must be publicly accessible or pre-uploaded. Only the vague 'advanced algorithms' phrasing appears.

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?

Four sentences of promotional copy ('The Ultimate...', 'Our advanced algorithms...', 'sophisticated lighting effects and physically accurate motions') that convey almost no operational information. Nothing actionable is front-loaded; the only substantive clause about the 2D-image input is buried third.

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?

This is a complex tool with a four-variant request union, an output schema, and several sibling lookalikes. Given that complexity, the description is inadequate: it omits input requirements (source image, reference SKU image, optional masks), the polling/async behavior, and any differentiation from sibling try-on tools. The output schema relieves it of explaining return values, but the input/usage contract 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 description says nothing about the two top-level parameters (request, polling) despite only 50% schema description coverage. It does not explain the URL-vs-File-ID variants of the request union or clarify that 'request' is required, so it fails to compensate for the schema gap and adds no 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 does state a specific verb+resource ('Create ... earrings virtual try-on from a 2D image') and even names an adjacent capability ('virtual ear piercing simulator'), so the purpose is discernible. However, it is buried in marketing superlatives ('The Ultimate', 'Top AI ear piercing simulator') and never distinguishes this tool from the many sibling VTO tools (Bag, Necklace, Ring, Watch), all of which share the same 'create try-on' framing.

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?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives or exclusions. Nothing tells an agent whether to pick this over AI-Necklace-Virtual-Try-On or AI-Bracelet-Virtual-Try-On, or what inputs are required before calling.

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 does not disclose that this is a non-idempotent task-based operation (polling parameter), requires publicly accessible URLs, or any side effects. It only provides promotional content, adding no transparency 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?

The description is a verbose marketing paragraph with exclamation marks and imperatives like 'Transform your look!' It fails to front-load the functional purpose and wastes space on 'fresh fabric updates' and 'see and feel fabrics.' Not concise or structurally helpful.

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?

Despite having a moderately complex input schema with multiple request variants and polling, the description provides no operational context such as how to obtain templates, file requirements, or async behavior. It is entirely inadequate for correct tool invocation.

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 description mentions 'photo mode' and 'fabric styles' but never references parameters like template_id, src_file_url, src_file_id, or polling. The input schema already provides descriptions, but the description adds no additional parameter semantics to assist the agent.

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 marketing language like 'Transform your look with stunning realism' and 'Explore unique fabric styles with photo mode' but never explicitly states the tool performs a virtual try-on of fabric on a provided photo using a template. It is vague and doesn't clearly identify the core operation or distinguish it from sibling 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?

No guidance on when to use this tool versus sibling tools (e.g., AI-Clothes-Virtual-Try-On) or prerequisites like listing templates or uploading files. There is no mention of when to choose this tool over alternatives.

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 annotations (readOnlyHint, idempotentHint, destructiveHint) already establish that this is a safe, non-destructive read operation. The description adds no additional behavioral context, such as what the templates represent or how they are used, but it is consistent with the annotations and does not contradict 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 a single, concise sentence with no filler or redundant information. It is front-loaded with the core verb and object, making it immediately parseable by an AI agent.

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 is adequate for a simple list operation, and the presence of an output schema covers return values. However, it lacks context about how this tool relates to sibling virtual try-on tools, specifically that the templates are likely inputs for those operations. A more explicit connection would enhance completeness.

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 tool has zero parameters, and the schema confirms this with 100% coverage. The description does not need to explain any parameters since none exist, and the baseline score for zero-parameter tools is 4.

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 the specific verb 'List' and resource 'predefined templates,' clearly indicating a read-only enumeration operation. It distinguishes from sibling try-on tools which perform actions rather than list templates, though the description itself does not explicitly mention 'fabric' or the domain context beyond the tool name.

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?

No guidance is provided on when to use this tool versus alternatives, such as other try-on tools or discovery methods. There is no mention of typical workflows, prerequisites, or exclusions, leaving the agent to infer usage solely from the tool name and the fact that it lists templates.

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 indicate non-read-only behavior, and the description adds no details about side effects, costs, or long-running tasks. The appended instruction that gender must be explicitly provided and that the agent should ask for clarification is a useful behavioral/enforcement note, but the rest is promotional and adds nothing about how the tool behaves.

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 verbose and filled with marketing claims ('Elevate engagement, boost confidence') that earn no functional value. The critical gender instruction is awkwardly appended to a long promotional paragraph, making it difficult to parse and not front-loaded with the key technical purpose.

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 multiple request schema variants, an output schema, and polling options, the description is incomplete. It does not clarify how to choose between URL and ID inputs, what the reference vs source images mean, or what the workflow should be, leaving the agent to infer everything from the schema.

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 50%, so the description needs to compensate, but it does not. The only parameter-related content is the gender requirement, which merely echoes the schema's 'required' flag. There is no explanation of src_file_url vs ref_file_url, file IDs, style options, or polling behavior.

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 as a virtual try-on for headwear, mentioning hats and headbands, which distinguishes it from sibling tools for other accessories. However, the meaning is buried in marketing language ('unmatched precision and realism') rather than a crisp functional definition like 'Generate a try-on image from a source and reference 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?

No guidance is provided on when to use this tool versus the many sibling virtual try-on tools, nor any context about customer scenarios. The only prescriptive sentence is about requiring the gender parameter, which is a parameter-level detail rather than usage 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?

Annotations already declare readOnlyHint=false, so the agent knows this is not a pure read operation. The description adds no behavioral details such as async processing, polling requirements, result format, or limitations; it only makes quality claims like 'ultra-realistic' and 'physically accurate motions', which do not inform invocation behavior.

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 opens with marketing slogans ('Luxurious Look and Feel') and repeats hyperbolic adjectives ('ultra-realistic', 'lifelike', 'sophisticated') that add no operational value. The core functional sentence is buried at the end, making it poorly structured and unnecessarily verbose.

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?

This is a complex tool with multiple request schema variants (URL vs File ID combinations), yet the description gives no overview of these options, prerequisites like publicly accessible URLs, or how to use upload APIs. The output schema exists, but the description still leaves the agent without a map of the tool's full input space.

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 only 50% (only the 'polling' parameter is described at the top level). The description does not mention or explain any parameters, offering no guidance on the complex 'request' object or its multiple URL/File ID variants, so it fails to compensate for the coverage gap.

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 'Create realistic and dynamic necklace virtual try-on from a 2D image', which clearly identifies the action (create) and resource (necklace virtual try-on). It also implicitly differentiates from sibling accessory VTO tools by focusing on 'necklace', though the phrasing is promotional and does not crisply define the exact input/output relationship.

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?

No explicit when-to-use or alternative guidance is provided. The purpose of necklace try-on is implied by the tool name and repeated 'necklace' references, and the phrase 'from a 2D image' hints at the input context, but the description never states when this tool should be chosen over sibling accessory VTO tools or how it relates to file upload tasks.

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 indicate this is a non-read-only, open-world operation. The description adds that it produces AR-based results and suggests 2D images, but does not disclose asynchronous polling behavior, potential side effects on uploaded data, or time expectations. With annotations present, this is acceptable but not rich.

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 and front-loads the core purpose. The second sentence is mildly promotional ('effortless yet high-quality') but does not waste much space. It is concise overall.

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 highly complex nested request schema (four union variants, many ring parameters, mask fields), the description is far too thin. It does not explain how to construct a valid request, the polling option, or the nature of the task (e.g., synchronous vs. async). The output schema exists but does not compensate for missing request semantics.

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 only 50%, and the description does not compensate. It only mentions uploading images, providing no insight into the complex 'request' object, optional masks, or the polling parameter. Since half the parameters are undocumented in the schema, the description fails to fill the gap.

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: creating AR ring or engagement ring try-ons from uploaded images. This specific verb+resource combination (create + ring try-on) distinguishes it from sibling tools for other jewelry types like necklaces or earrings.

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 phrase 'Ring or Engagement Ring Try Ons' implies this tool is for rings, but there is no explicit mention of when to use it vs. alternatives or exclusions. The statement 'You Only Need to Upload Images' offers a basic input requirement but no comparative 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?

The description adds context about AR simulation and high-fidelity visualization, but it does not disclose operational traits such as asynchronous execution, polling behavior, or the nature of the output (e.g., a generated image). Annotations already mark the tool as not read-only and not destructive, and the description does not contradict them. It also includes a behavioral requirement for gender, which is useful context beyond the schema.

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 padded with marketing language like 'Enhance your fashion experience' and 'high-fidelity AR simulation,' repeating the concept of virtual try-on multiple times. The useful instruction about gender is appended awkwardly after a space, forming 'anywhere.gender' typo. It could be trimmed to one or two sentences focusing on purpose and the gender requirement.

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 complex input schema with four anyOf variants, a polling parameter, and an output schema, the description is far too high-level. It omits guidance on choosing between URL and ID inputs, the asynchronous nature of the task, and the expected result. The gender requirement is the only operational detail provided, which is insufficient for an agent to confidently invoke this tool.

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 description explicitly stresses that gender must be provided and instructs the agent to ask if missing, adding semantic weight to that parameter beyond the schema's required flag. However, it does not clarify the meaning of src_file_url vs ref_file_url or the file ID variants, and with only 50% schema description coverage, it does not fully compensate. The mention of 'styles and colors' loosely aligns with the style parameter but adds little detail.

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 as a virtual scarf try-on service, using active language like 'drape scarves over their outfits' and 'see how patterns flow in real life.' This distinguishes it from sibling tools for other accessories/clothing. The resource (scarf) and action (virtual try-on) are both explicit, earning a 5.

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 shoppers interested in virtually trying on scarves, but it does not explicitly contrast with sibling try-on tools or state when not to use it. It does provide a clear operational guideline that 'gender must be explicitly provided' and instructs the agent to ask for clarification if missing, which is helpful. However, this is not about tool selection, so the overall guidance is only implied rather than explicit.

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 declare readOnlyHint=false, idempotentHint=false and openWorldHint=true, so the agent already knows this is a non-idempotent mutating operation against external resources. The description adds nothing behavioral – no mention of it being a long-running async task (implied only by the polling param), no cost, no rate limits – and 'Instantly see' mildly conflicts with the polling semantics in the schema.

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?

Roughly five sentences of pure marketing fluff precede the one sentence with any operational content, and that content is appended with no separator. The structure is not front-loaded – the only actionable instruction is at the very end, glued to the prose.

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?

An output schema exists so return values are covered, but for a non-idempotent async generation tool the description omits the task/polling model, the meaning of the two source inputs, and cost behavior. Instead it spends its length on promotional copy and a reference to a parameter that does not exist.

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 only 50%, so the description needs to compensate, and it does not. It never explains the crucial src (selfie) vs ref (product) distinction, and it introduces a phantom 'gender' parameter absent from the schema, which is worse than silence.

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 conveys that this is an AR/virtual try-on for shoes, which distinguishes it from the bag/hat/watch siblings. However, it never states the actual operation in concrete terms (e.g., 'generates an image of shoes on a source photo') – it is buried in marketing prose about 'stepping into the future of shopping' and 'high-fidelity AR simulation'.

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?

No guidance on when to choose this over the other try-on siblings, nor on the async/polling workflow. The only operational instruction is about a 'gender' parameter that does not exist anywhere in the input schema, which would actively mislead an agent into asking the user for a nonexistent field.

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 indicate non-read-only, non-idempotent, and non-destructive behavior, but the description adds little beyond that. It does not disclose the async/polling behavior hinted by the 'polling' parameter, nor does it mention the need for masks or multiple images. The phrase 'instantly try on' could actually mislead about task latency.

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 contains marketing fluff ('top-notch watches', 'sets us apart', 'easier than ever') that provides no operational value. The core message could be conveyed in one sentence. It is not efficiently structured for an AI agent.

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 complexity of the schema (four request variants, nested objects, optional masks, adjustable watch parameters), the description is far too thin. It does not explain the different input modes (URL vs File ID), the role of masks, or how the output is produced, despite an output schema existing.

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 description adds essentially no parameter information. It mentions 'one 2D image' but the schema involves source images, reference images, optional masks, and watch-specific tuning parameters. With 50% schema coverage, the description does nothing to compensate for the gap.

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 as a virtual try-on for AR watches, using phrases like 'Try-On AR Watches' and 'AR-Watches App'. This differentiates it from sibling tools for other products, though the marketing tone adds some noise. It is a specific verb+resource statement.

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?

No explicit guidance is provided for when to choose this tool over the sibling try-on tools. The only clue is the product category in the name, but the description itself does not mention alternatives, prerequisites, or exclusions.

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 the full burden and does well: it discloses that uploads happen directly from the browser, bypassing sandbox egress restrictions, and that it returns file IDs consumed by downstream AI tools. It omits size/format limits and any auth expectations, so it is strong but not exhaustive for a mutation-style upload.

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?

Core purpose and usage are front-loaded in the first sentences, but the parenthetical enumeration of AI tool names ('AI-Fabric, AI-Cloth, AI-Makeup-Vto...') is filler, and the '/n' separators are a formatting defect. Still, every major sentence carries routing or behavioral 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?

Because an output schema exists, return values need not be explained, and the description already covers purpose, triggers, fallback behavior, and browser-egress behavior. An agent has everything needed to decide when to call it and what to expect.

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 single parameter (task_type, a large enum of pre-selectable tasks) is already fully documented in the schema. The description adds no parameter-level detail beyond the baseline, so 3 is correct.

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 ('Interactive drag-and-drop widget for uploading images (JPEG, PNG) or videos (MP4)') and names its output ('file IDs'). It also positions itself against the sibling Get-Upload-API-Info by describing itself as the mandatory fallback, so the agent can distinguish it without opening either schema.

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?

Gives explicit triggers (user wants to upload/select, or any AI-* tool needing src_file_id/ref_file_id when no file ID or URL exists) and a clear fallback condition (Get-Upload-API-Info failing due to network restrictions/host_not_allowed). It does not, however, explain how it differs from the sibling tool 'upload_file', leaving one routing ambiguity.

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, idempotentHint, and destructiveHint, so the description doesn't need to restate safety. It adds context by stating the values are consistent with an external URL, which clarifies the data source and reliability. This goes slightly beyond the structured 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?

The description is a single, concise sentence that front-loads the core functionality and includes a helpful reference URL. Every word is purposeful, with no redundancy or 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?

For a simple read-only lookup tool with a rich output schema and clear annotations, the description is nearly adequate. It identifies the data source and purpose, but it omits any mention of pagination behavior, which is implied by the input parameters. Still, the output schema and annotations cover many details, making the description sufficient for most use cases.

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 0% description coverage, and the description does not explain the 'request' object, page_size, or starting_token. The parameter names hint at pagination, but the description adds no semantic value, forcing the agent to guess from examples. Given the low coverage, the description should compensate, but it does not.

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: 'Check the unit consumption for each API.' It uses a specific verb ('check') and resource ('unit consumption'), and it links to an official pricing page to further clarify what the tool accesses. This clearly distinguishes it from sibling tools, which focus on AI transformations and file 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 when to use the tool (when you need to know unit consumption/costs), but it does not explicitly state usage contexts or exclude alternatives. It provides a reference URL for consistency but lacks guidance on when to prefer this tool over others, though such guidance is not critical since no sibling tool overlaps in purpose.

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 safety (readOnly, idempotent, non-destructive, closed-world), and the description adds useful behavior: it explains the polling timeout risk implied by 'a task exceeded the initial polling timeout' and the polling parameter's blocking-vs-immediate semantics in the schema. It does not explicitly disclose the long-polling default behavior or clarify what happens when polling=true and the task never completes, which would fully close the loop.

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?

Front-loaded one-sentence purpose followed by a bulleted when-to-use list; every bullet maps to a distinct decision branch and none is redundant. Zero filler.

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 annotations covering safety, a 100%-covered four-param schema, an enum for task_type, and an output schema present, the description need not explain return values. The coverage of triggers and the polling/timeout behavior makes the definition complete enough for correct invocation.

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 baseline is 3; the description adds a bit by tying task_id/task_type to 'previously initiated' tasks and the polling/timeout narrative that gives is_preprocess and polling context. It doesn't explain polling's default or is_preprocess meaning beyond the schema, so a modest boost over baseline.

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 (Retrieves) and resource (status and results of a previously initiated AI processing task), and the title plus the task_id/task_type framing make clear it is a status-check tool distinct from the initiation sibling tools. Sibling names are all initiation tools, so this is clearly differentiated.

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 'WHEN TO USE THIS TOOL' block enumerates four concrete triggering scenarios (task_id from prior session, user asking 'is my hairstyle ready?', resuming a conversation, task timed out on polling) that map directly to agent decision branches. This is explicit when-to-use guidance and the task_id/task_type requirement implicitly excludes the initiation path.

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, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful content context (usage entries tied to tasks/orders) but says nothing about pagination, result volume, or ordering — notable given page_size/starting_token parameters.

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?

A single, front-loaded sentence with no filler. It is efficient, though its brevity contributes to the missing pagination and usage detail.

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?

An output schema exists, so return values needn't be described, and the annotations cover safety. What is missing for a paginated history tool is any statement about paging behavior or how the result set is scoped/ordered, leaving the agent to guess how to iterate.

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 0%, so the description carries the burden of explaining parameters, yet it never mentions the pagination controls (page_size, starting_token) in the request object. The only parameterization available to the agent is undocumented in both places.

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 ('View the unit usage history') and clarifies the record type ('units consumed or granted per task/order'). It does not, however, differentiate itself from the close sibling Get-Unit-Info, so an agent must infer which to pick.

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?

There is no when-to-use guidance and no mention of alternatives such as Get-Unit-Info or Get-Feature-Cost, despite those siblings clearly overlapping in domain. Usage is only implied by the phrase 'for this account'.

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, idempotentHint, and destructiveHint=false, so the safety profile is fully covered elsewhere. The description adds only that results are scoped to 'this account' and that entries carry type, amount and expiry — modest added context, with no mention of pagination, caching, or freshness.

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?

A single front-loaded sentence that states the resource, the scope, and the returned fields with no wasted words. Nothing is buried or repeated.

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 an output schema present, the description need not document return values, and the input schema is empty, so the remaining burden is light. It is nearly complete for such a simple read tool; only the relationship to Get-Unit-History is left implicit.

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 tool takes no parameters (schema coverage 100%, empty properties object), so there is nothing for the description to disambiguate. Baseline 4 applies for a zero-parameter tool.

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 names a specific verb and resource ('Get the current unit balance for this account') and enumerates the fields returned. It implicitly distinguishes itself from the sibling Get-Unit-History by the word 'current', though it never names that sibling explicitly.

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?

Usage is only implied: 'current' suggests this is a point-in-time snapshot as opposed to Get-Unit-History, but there is no explicit statement of when to use this tool versus that one. No prerequisites or exclusions are given.

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 establish readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds real behavioral value above that bar: a no-retry policy on host_not_allowed and the required fallback path. It stops short of describing response shape or the endpoint's access constraints.

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?

Front-loaded with what is returned, then the usage and failure directives; every sentence earns its place. Minor formatting blemish: the literal '/n' escape mid-description instead of a newline.

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 an output schema present and annotations covering the safety profile, the description need not explain return values. It covers purpose, usage, and failure fallback adequately; only a note on the endpoint's environment/auth context 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% for the single task_type parameter, so the schema already carries the semantics. The description does not add any meaning about which task types are valid or how task_type affects the returned endpoint, so 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: returns the upload API endpoint plus example request/response, with supported formats (JPEG, PNG, MP4). This clearly separates it from siblings like File-Upload and upload_file, which perform the upload rather than returning API metadata.

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?

Explicit when-to-use ('when the user wants to upload an image/video via the API') and when-not, with a named fallback: if upload fails for any reason (network restrictions, host_not_allowed), do not retry and switch to the File Upload Widget. It also preempts a wrong action ('Do not ask the user for a public URL').

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 the full behavioral burden. It usefully discloses the return value and that the upload endpoint changes based on whether task_type is supplied (POST /s2s/v2.0/file), but it omits auth requirements, size/format limits, and failure behavior for an upload operation.

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 tightly written sentences with the core action front-loaded and the conditional task_type detail following. No filler, though the endpoint path detail could be trimmed as implementation leakage.

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?

An output schema exists, so return values need not be re-explained. However, for a no-annotation upload tool, the description leaves the 'files' object shape and operational limits (size, encoding constraints, auth) undocumented, which is a meaningful 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 coverage is 0%, so the description must compensate. It adds real meaning by stating files must be base64-encoded and that task_type selects the upload path, but it does not describe the internal structure of the file objects in the 'files' array, leaving that half of the contract unexplained.

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+resource ('Upload base64-encoded media files to the YouCam API') and the return value ('file_id(s)'). It is clear what the tool does, but it does not distinguish itself from the sibling 'File-Upload' or the many task-specific try-on tools, so sibling differentiation is missing.

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 explains the conditional behavior of task_type (generic vs task-specific upload path) but never says when to use this tool versus alternatives like 'File-Upload' or the per-task try-on tools. No explicit when/when-not guidance or prerequisites are given.

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