YouCam for Beauty & Personal Care

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

Annotations already indicate this is a mutating operation (readOnlyHint: false) and non-destructive. The description adds context on input requirements (full-body/upper-body) and the outcome (edited output image), but does not disclose potential limitations or edge cases such as image quality 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?

The description is well-structured, front-loaded with the main purpose, followed by the list of supported modes. It avoids unnecessary detail and earns its length.

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?

It covers the essential workflow and output, but does not mention the asynchronous polling behavior or the `polling` parameter, which is important for a long-running image generation task. The presence of an output schema fills some gaps, but the lack of polling guidance is a notable omission.

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 explains the meaning of the two modes and mentions the intensity level, adding value beyond the schema's enum descriptions. It does not explicitly explain the source file parameters, but the schema descriptions cover those.

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 'add realistic abdominal definition' and clearly identifies the resource (photo editing via an API). It distinguishes this tool from siblings by focusing on abs-specific enhancement, and it lists the two supported modes.

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 clearly states the workflow (upload full-body/upper-body image, select mode, set intensity) and the intended result. However, it does not explicitly mention when to use this tool over sibling body-reshaping tools like AI-Body-Reshape, though the abs-specific focus makes the use case evident.

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, non-destructive behavior. The description adds value beyond annotations by specifying that the tool uses generative AI and by mandating exact URL preservation from response.data.results.url, a practical behavioral requirement. It does not contradict annotations and provides useful operational context.

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 somewhat repetitive, stating 'AI aging generator' and 'generate a series of photos' twice. It also references sample images without showing them. While not excessively long, it could be more concise and front-loaded.

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 moderate complexity and presence of an output schema, the description covers the core functionality and URL handling. However, it omits input format specifics, usage timing, and any caveats, making it only partially complete for an agent to invoke it confidently.

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%, and the description only mentions a 'single selfie image' without detailing the request object, src_file_url, src_file_id, or polling parameter. The description does not compensate for the schema gaps, leaving parameter semantics largely 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?

The description clearly identifies the tool as an AI aging generator that produces a series of photos from a single selfie, distinct from sibling tools like virtual try-on or skin analysis. It specifies the resource (selfie image) and the action (generate aging sequence), making the purpose unmistakable.

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 for aging simulation but provides no explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. It lacks exclusion criteria or context for choosing this over similar tools, so usage is inferred rather than stated.

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 indicate readOnlyHint=false, openWorldHint=true, idempotentHint=false, and destructiveHint=false, but the description adds no behavioral context beyond these. It does not mention what the output looks like, whether it requires a public URL or uploaded file, how long processing takes, or any side effects. Since annotations do not cover these operational traits and the description is silent, the agent has an incomplete picture of the tool's 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 is filled with marketing phrases like 'Realistic Looks,' 'Versatile Styling Options,' and 'Effortless Experience' that add no functional value. It also ends abruptly with incomplete sentences ('Please refer to' and 'Use case: Suggestions for How to Shoot:'), indicating a broken or unfinished structure. This is not concise or well-structured; it wastes space on fluff while omitting essential operational details.

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 requires a template_id, yet the description does not mention how to obtain templates or list available ones (a sibling tool 'AI-Bangs-Filter-Virtual-Try-On-Templates' exists but is not referenced). It also fails to explain the expected input image format, the nature of the output, or any 'use case' despite the heading. The output schema exists but the description does not clarify the return. Overall, the description is severely incomplete and leaves the agent without critical context 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?

The input schema is moderately documented (50% coverage): fields like template_id and src_file_url have basic descriptions and examples. However, the description does not mention any parameters at all, so it fails to compensate for the gap. The description's vague talk about 'trying new bangs' gives no insight into what inputs the agent must supply or how to structure the request 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 states it is for trying on bangs with AI, with phrases like 'Try on Your Perfect Hair Bangs with AI' and 'Experiment with realistic bangs and discover the style that best complements your face.' This gives a clear general purpose (bangs try-on) but does not specify the underlying operation (e.g., applying bangs to a photo) or differentiate it from sibling tools like AI-Hair-Style-Virtual-Try-On. It is vague, leaning toward marketing rather than a precise functional description.

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 explicit guidance on when to use this tool versus alternatives. The description contains an incomplete line 'Want to see more Hair Bang styles? Please refer to' and 'Use case: Suggestions for How to Shoot:' but no actual instructions or context follow. This leaves the agent without any direction on when to select this tool among many similar hair-related try-on 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 annotations already declare this as read-only, idempotent, and non-destructive, and the description adds no additional behavioral information. It does not mention return format, ordering, or any other behavior beyond simply listing, so it offers no value 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 single concise sentence with no filler or redundancy. It is appropriately short for a tool with no parameters.

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?

While the tool is simple, the description is too terse to provide sufficient context for an agent to know which templates are being listed or how they relate to the bangs filter workflow. The name provides context, but the description itself does not explain the tool's role among its many similar siblings.

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, so the description has no parameter information to add. The schema is empty and fully covers the input space, meeting the baseline for zero-parameter tools.

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 clear action ('List') and resource ('predefined templates'), but it does not specify what kind of templates or how this differs from sibling template tools. The tool name provides the context, but the description itself is too generic to independently distinguish it from other template-listing 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 is provided about when to use this tool versus the related try-on tools or other template tools. There is no mention of prerequisites, alternatives, or typical use cases beyond the bare 'list' verb.

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 readOnlyHint=false and openWorldHint=true, but the description does not disclose the async task nature, polling behavior, or that it creates a processed image. It adds vague claims like 'highly sophisticated beard simulation' but no operational detail beyond 'before and after results'.

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 repetitive, with phrases like 'AI Simulation', 'beard styles', and 'beard filters' repeated. It contains a typo ('virtualy') and marketing fluff that does not aid an agent in tool selection or invocation.

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?

While an output schema exists, the description fails to explain the required inputs (a user photo and template_id) or the prerequisite of listing templates. It also omits any mention of the sibling Templates tool, leaving the agent without enough context to correctly invoke the 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 schema descriptions for src_file_url, src_file_id, and template_id are decent, and the tool description lists style options that help interpret template_id. However, it does not explain the request object structure, the anyOf variants, or the polling parameter, so the description only partially compensates for the 50% 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 clearly identifies the tool as an AI simulation for men's beard styles, mentioning specific styles and before/after results. This distinguishes it from sibling tools like AI-Aging-Simulation, though it lacks an explicit verb like 'generate' or 'apply'.

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 alternatives, no prerequisites mentioned, and no mention of the need to fetch templates first via the sibling Templates tool. The text is promotional ('Shoppers can see before and after results') rather than instructive.

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, which cover the safety profile of listing. The description adds no additional behavioral context such as pagination, response ordering, or access requirements. However, for a simple read-only list operation, the annotations and description are consistent, so a baseline score is appropriate.

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?

At two words, the description is maximally concise and front-loaded. Every word contributes meaning: 'List' specifies the operation and 'predefined templates' specifies the resource. No wasted content.

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 is low-complexity with no parameters and has a rich annotation set, but the description is extremely terse. It relies entirely on the tool name to identify what type of templates are listed, and provides no context about the relationship to the AI-Beard-Style-Generator tool. While an output schema exists (so return values needn't be described), a slightly more informative description would aid 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 is empty with 100% schema coverage. Since parameters are not present, the description is not required to explain them. Baseline 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 'List predefined templates' uses a specific verb (List) and identifies a resource (predefined templates). The tool name 'AI-Beard-Style-Generator-Templates' contextualizes this to beard style templates, distinguishing it from sibling template tools in other domains. However, the description alone does not explicitly name the beard-style feature, slightly reducing differentiation clarity.

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 usage guidance is provided in the description. It doesn't explain when to choose this tool over sibling template tools (e.g., AI-Bangs-Filter-Virtual-Try-On-Templates) or how the listing relates to the main AI-Beard-Style-Generator tool. This is a clear gap.

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 little beyond the annotations. It does not disclose that the tool modifies images, any asynchronous behavior, return formats, or side effects. Since annotations already indicate non-read-only and non-idempotent, the description should clarify what actually happens, but it remains purely promotional.

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 only three sentences, but it contains redundancy ('Body Reshape' repeated) and marketing fluff ('stunning results in just a tap!'). It is reasonably concise but not efficiently engineered for an API description.

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 schema with many feature parameters and the presence of a related detection tool, the description is incomplete. It does not explain required inputs, output expectations, or how to invoke the tool correctly. The schema and annotations cannot compensate for the lack of 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?

The description mentions body areas like waist and arms that map to feature properties, but it does not explain parameter meaning, ranges, or the fact that features cannot all be zero. Schema coverage is only 50%, and the description does not compensate; it provides no functional parameter insight.

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 is for body reshaping and slimming in photos, listing specific areas like waist, arms, thighs, and chest. However, it does not distinguish this from the sibling AI-Body-Reshape-Detection or other body-related tools, so it's clear about the action but not unique.

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 guidance on when to use this tool versus alternatives, no mention of prerequisites (e.g., needing a detection step first), and no exclusions. The description only offers a generic use case ('feel confident in photos'), which is too vague for an agent to choose the right 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, providing the safety profile. The description adds no behavioral context beyond 'run a task', such as whether results are returned as a detection score or whether the task is asynchronous. It does not contradict annotations, so a neutral score is appropriate.

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 redundancy. It is front-loaded with the action and resource, making it easy to scan.

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?

While the schema and annotations provide decent structural and safety coverage, the description alone does not sufficiently explain the tool's role among many similar detection tools. The output schema exists, so return values are covered, but the lack of differentiation and vague 'detection' meaning leaves the context incomplete for optimal agent selection.

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% (only 'polling' has a direct description; 'request' lacks a top-level description although nested fields are described). The description does not add any clarification about the 'request' parameter or the file source options, failing 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 'Run an AI Body Reshape detection task' clearly specifies the verb (run) and resource (AI Body Reshape detection task), making the basic purpose understandable. However, it does not distinguish this detection tool from its sibling 'AI-Body-Reshape', which likely performs the actual modification, so sibling differentiation is lacking.

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 the many sibling detection/reshaping tools. It does not mention use cases, alternatives, or prerequisites, leaving the agent without context for 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 adds context beyond annotations by noting the tool is 'non-invasive' and produces 'subtle, realistic improvements' that 'maintain your natural features'. However, it does not disclose behaviors like whether the original file is modified, processing time, or any side effects, which is a gap given the sparse annotations (readOnlyHint=false, idempotentHint=false).

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 marketing-heavy, with repetitive phrasing like 'Enhance your photos naturally' and 'maintain your natural features'. It is not front-loaded with key operational details; the actual usage guidance appears only in the final paragraph, and much of the text could be trimmed without losing 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?

Despite having an output schema, the description fails to specify that the tool requires a source image URL or file ID to start a task. It mentions 'create a task' and 'adjust intensity' but omits the essential input parameter, polling behavior, and how to provide the image, leaving significant gaps for an AI agent to invoke the tool successfully.

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 only mentions 'adjust parameters such as augmentation intensity' without explaining the primary input requirement (a source file URL or ID). The schema already documents the intensity parameter well, but the description does not compensate for the missing top-level description of the 'request' object, which is critical for correct invocation.

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 an AI-powered breast augmentation simulator, explicitly stating it offers 'AI-powered bust contour enhancement' and 'preview before-and-after results'. This specific verb+resource combination distinguishes it from sibling tools like AI-Body-Reshape or AI-Face-Lift.

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 context for when to use this tool, such as 'if you’re seeking a more balanced or proportionate upper-body appearance' and mentions use cases like 'preparing images for social media, personal use, or creative projects'. However, it does not explicitly compare to or exclude sibling tools like AI-Body-Reshape, so it lacks explicit when-not 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 annotations only indicate openWorldHint and non-read-only behavior; the description adds claims about iris detection and reflection preservation but does not disclose operational behavior like async task execution, image input requirements, or the role of the reference file. It also omits limitations or prerequisites.

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 long and repetitive, with multiple marketing sections reiterating 'hyper-realistic' and 'professional-grade.' It lacks a concise functional summary and buries the core purpose amid promotional language.

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 anyOf request structure and polling parameter, but the description never mentions that images must be supplied as URLs or uploaded file IDs, nor does it explain the async/polling workflow. Although an output schema exists, the description still fails to provide enough contextual understanding 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 description coverage is only 50%, and the description does not explain the critical relationship between src_file_url and ref_file_url or the effect parameters like intensity and enlargement. The broad mentions of 'opacity and blending' do not map to specific schema fields, so the description fails to compensate for the schema 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 uses specific verbs like 'transform eye color' and 'virtual try-on' and identifies the resource as contact lenses on the eye. It distinguishes this tool from sibling hair/beauty tools by focusing on iris detection and lens 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?

There is no explicit guidance on when to use this tool instead of siblings such as AI-Hair-Color-Virtual-Try-On or AI-Makeup-Virtual-Try-On. The only implied context is 'allowing shoppers to explore new styles,' which is too vague to inform 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the description does not need to restate safety. It adds minimal behavioral context by mentioning it 'examines' and 'identifies' features, which aligns with the read-only hint. No contradictions exist, but the description does not disclose polling behavior or how the strictness level affects results beyond what the schema already covers.

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 sentence without excessive length, but it includes the filler phrase 'designed to provide personalized recommendations' that does not add concrete operational meaning. It is acceptable but not tightly structured around the most decision-relevant details.

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 large feature enum, two mutually exclusive input modes, a polling parameter, and a configurable strictness level. The description provides only a high-level summary and fails to mention critical invocation details such as how to provide an image (URL or file ID), what the polling parameter does, or how face_angle_strictness_level affects output. The output schema helps, but the description alone is insufficient for complete 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 only 50% because the top-level 'request' parameter lacks a description, and the tool description does not compensate by explaining the request structure or the two input modes (src_file_url vs src_file_id). While the nested schema has rich descriptions for features and face_angle_strictness_level, the overall parameter semantics are not clarified by the description itself.

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 that the tool 'examines face structure' and identifies specific features like face, eye, eyebrow, lip, nose, and cheekbone shapes, which is a specific verb+resource pair. It is distinguishable from sibling tools like AI-Skin-Analysis or virtual try-on tools by focusing on structural attributes and ratios, though it does not explicitly name an alternative.

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 guidance on when to use this tool versus alternatives such as AI-Facial-Color-Tones-Analyzer or AI-Skin-Analysis. The phrase 'designed to provide personalized recommendations' implies a use case but does not specify prerequisites, exclusions, or comparison with related 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 annotations already provide readOnlyHint=false and destructiveHint=false, so the description is not burdened with safety disclosure. It adds useful behavioral context about identity preservation, gradual intensity, and natural results. However, it omits operational details like task-based execution/polling and detection dependency.

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?

At about 130 words, the description contains redundant marketing phrases ('natural, balanced' repeated, 'suitable for social media...' final sentence) that do not aid tool invocation. It does front-load the core function but could be streamlined to 2-3 sentences.

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 request schema (four alternative shapes, polling behavior, detection index), the description leaves significant procedural gaps: it never explains that the tool is a task with polling, that 'auto' vs 'custom' determines feature requirements, or that a person index from a detection task is needed. Output schema exists, but the description still under-specifies how to invoke the tool 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 descriptions already cover polling, src_file_url, src_file_id, index, version, and features constraint. The description adds semantic meaning to the feature parameters (0-100, gradual enhancement) and enumerates facial areas. However, it does not address structural parameters like type (auto/custom), global skin smoothing, or request organization, and with 50% schema coverage, the description should compensate more.

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 a facial enhancement tool with specific adjustable facial areas, using a specific verb 'enhancement/refinement'. It distinguishes from a generic filter but does not explicitly name sibling tools like AI-Face-Reshape, so differentiation is partial.

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 facial enhancement and lists use cases, but provides no explicit guidance on when to choose this tool over alternatives such as AI-Face-Reshape or when not to use it (e.g., for full-body changes). This is clear context, but no exclusions or alternative tool mentions.

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 readOnlyHint and destructiveHint false, which already cover safety. However, the description adds no behavioral context beyond 'run a task'—no mention of polling, return behavior, or what happens after detection. It fails to disclose any non-obvious 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 single sentence is concise but under-specified. It does not earn its place because it merely repeats the tool name without adding useful information. True conciseness would still convey purpose and context, which this misses.

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 an output schema, the description is incomplete for a tool with a non-trivial request structure (anyOf URLs or IDs) and polling options. It fails to explain what the detection task actually does, leaving the agent to guess at the tool's role among many similar face-analysis tools.

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 parameters. Schema coverage is 50% (polling has a description, but request lacks a top-level description). With half the schema undocumented and zero param info in the description, the tool fails 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 'Run an AI Face Lift detection task' essentially restates the tool name without explaining what the detection task does or how it differs from sibling tools like AI-Face-Lift. It lacks specific scope or output details, making it tautological.

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 does not mention any prerequisites, exclusions, or distinguish from AI-Face-Lift, which is a likely sibling for similar 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 not read-only and not destructive. The description adds that it generates before/after visualisations and mentions 'hyper-realistic previews', but it does not disclose async behavior, cost, or operational constraints beyond what the schema hints via the polling parameter. 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?

The description is overly verbose and marketing-oriented, with promotional phrases like 'pixel-perfect control', 'medical-grade precision adjustments', and 'clients can clearly visualize'. The bullet-point sections repeat the same information and could be condensed into a single sentence. It is structured but wastes significant space on hype.

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 a good high-level overview of use cases but does not explain how to invoke the tool (e.g., required src_file_url or src_file_id, the request structure, or the meaning of feature values). The schema and output schema fill some gaps, but for a complex tool with many nested feature parameters, the description could more directly cover operational details.

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 only 50%, and the description compensates by mapping feature groups to real-world procedures (e.g., 'Bridge, Lift, and Wing' for nose, 'Chin Length and Chin Shape' for chin, 'volumes and shapes' for lips). This adds meaningful semantic context to many feature parameters that lack individual descriptions in 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 the tool reshapes facial features (eyes, nose, lips, jawline, whole face) and explicitly lists aesthetic procedures (rhinoplasty, chin filler, lip filler, brow lift), distinguishing it from sibling tools like AI-Face-Lift or AI-Body-Reshape. It is specific about the resource and actions.

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 direct usage context: 'Use it to generate before/after visualisations for rhinoplasty, chin fillers, lip augmentations, brow lifts and any other aesthetic-treatment workflow.' It gives clear when-to-use guidance, though it does not explicitly mention when not to use or name 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?

Annotations declare readOnlyHint, openWorldHint, and destructiveHint=false, but the description adds no behavioral context beyond that. It does not mention the polling parameter, file input requirements, or task lifecycle, so the agent cannot anticipate asynchronous behavior or expected outputs.

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 short sentence with no wasted words, but it is under-specified to the point of being uninformative. Conciseness without substantive content is not a strength.

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 an output schema and annotations, the tool description is inadequate for safe invocation. It omits core context such as what the detection result contains, how to provide input files, and how polling works, making it incomplete for an agent operating among many similar tools.

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 covers 50% of parameters, but the description adds no parameter-related meaning. It does not explain the 'request' anyOf structure, the difference between src_file_url and src_file_id, or the polling flag, leaving the agent to rely solely on 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 restates the tool title ('AI Face Reshape detection task') with the generic verb 'Run', so it is essentially a tautology. It does not explain what a 'detection task' actually does or how it differs from sibling tools like AI-Face-Reshape or AI-Body-Reshape-Detection.

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 guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. With dozens of similar sibling tools, the agent receives no decision support at all.

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 and destructiveHint=false, so safety is covered, but the description adds no extra behavioral context. It does not mention what happens if no face is detected, the meaning of face_angle_strictness_level, or whether results are asynchronous/polled. The marketing line about 'tailored shopping experience' is not behavioral.

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 first sentence is concise and informative. The second sentence ('This inclusive technology ensures to a complete tailored shopping experience for all ethnicities') is marketing fluff that wastes space and does not earn its place. The description is short but not efficiently 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?

For a complex tool with asynchronous polling, multiple request types, and angle strictness parameters, the description only covers the high-level purpose. It omits prerequisites like needing a publicly accessible image, the asynchronous nature, and how to interpret results. The output schema exists but the description doesn't set expectations for the task workflow.

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% and the description provides no parameter information. It doesn't mention that the tool accepts a file URL or file ID, nor the polling option or strictness level. The schema itself documents parameters well, but the description does not help bridge the gap for undocumented fields.

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 it detects facial skin tone, eye, eyebrow, lip, and hair colors, which is a specific verb and resource. It distinguishes from siblings like skin-type analyzers and virtual try-ons, though it doesn't name alternatives. The phrase 'complete tailored shopping experience' adds non-functional fluff.

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 vs alternatives like AI-Face-Attributes-and-Ratio-Analyzer or AI-Skin-Analysis. No mention of input requirements (e.g., clear face image) or intended scenarios. Usage is only implied by 'detects.'

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 contains a misleading element: it says the API returns 'one of six standardized skin types', but the accompanying table lists Type V twice and omits Type IV. It also describes the operation as 'in a single request', which conflicts with the polling behavior implied by the 'polling' parameter in the schema. Annotations are not contradicted, but these errors reduce transparency.

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 long and has structured sections, but the inclusion of an erroneous table (duplicate Type V, missing Type IV) undermines the structure. Several sentences repeat marketing-style claims, and the description could be more concise without losing essential 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?

Although an output schema exists, the description omits key operational details: how to invoke the tool with a file URL or ID, what 'index' refers to (from a detection task), and the asynchronous/polling nature. It also fails to connect this tool to its '-Detection' sibling. The description is long but still incomplete for an agent to invoke correctly in 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?

With schema description coverage at 50%, the description should compensate for undocumented parameters, but it does not. It mentions 'camera input' when the schema expects 'src_file_url' or 'src_file_id', and it fails to explain the 'index' parameter or the asynchronous 'polling' option. Thus it adds little value beyond the schema and can even mislead.

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: 'classify skin types accurately using camera input' and return a Fitzpatrick classification. It is specific about the verb and resource, but it does not explicitly differentiate from the sibling tool 'AI-Fitzpatrick-Skin-Type-Analysis-Detection', so it falls short of a full 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 provides usage context by mentioning 'personalized skincare, sunscreen, and product recommendation workflows for eCommerce and digital health platforms'. However, it gives no guidance on when not to use this tool or when to choose an alternative such as the '-Detection' sibling, leaving the guidance 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?

The description adds no behavioral context beyond the annotations. It does not disclose the async task-based workflow, the polling behavior, or the requirement for a publicly accessible file URL/ID. With readOnlyHint and destructiveHint already provided, the description misses the chance to explain what the detection task entails.

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, front-loaded sentence with no redundant words, making it structurally concise. However, its extreme terseness sacrifices substance, though the structure itself is acceptable.

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 moderate complexity (task-based with polling, two request modes) and an output schema, but the description provides no context about the invocation workflow or how polling affects results. It is incomplete for an agent to fully understand the tool's operation.

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%, but the description does not compensate by explaining parameters like 'polling' or the request object's alternatives (src_file_url vs src_file_id). It adds no 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 clearly states the verb 'run' and the resource 'AI Fitzpatrick Scale Analyzer detection task', identifying a specific function. However, it does not explicitly distinguish from sibling tools like 'AI-Fitzpatrick-Skin-Type-Analysis', which may serve a similar purpose.

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 guidance on when to use this tool versus alternatives, nor any mention of prerequisites such as needing a source file or when to enable polling. The description lacks context for selecting the appropriate tool among many similar detection/analysis 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 adds valuable behavioral warnings beyond the annotations: the preset-priority conflict and the requirement that the source image contain the hair area. These caveats help the agent anticipate failure modes. However, it does not mention async processing or polling, though the schema covers that.

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 structured with bullet points and warnings, but the opening sentences are promotional ('Explore a wide range...', 'Try the hair color you've always dreamed of') and do not provide functional information. The length could be reduced without losing 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?

The description covers the essential workflow and key constraints (image must contain hair, preset priority). The input schema provides detailed parameter documentation, and the output schema handles return values, so the description is sufficient for an agent to use the tool effectively without missing critical information.

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?

With 100% schema description coverage, the baseline is 3. The description adds extra meaning by explaining the priority relationship between preset and pattern/palettes, and by referencing sliders for adjusting color intensity, which maps to color_intensity and shine_intensity. This goes 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 description clearly identifies the tool as a hair color changer: 'hair color changer', 'Try the hair color you've always dreamed of', and 'Choose from predefined color presets or fine tune...'. It is specific about the resource (hair color) and distinguishes it from sibling tools like AI-Hair-Style-Virtual-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 explains the workflow (upload image, choose preset or pattern/palettes) and gives warnings, but it does not explicitly state when to use this tool versus alternatives. Usage is implied through context, but no exclusions or alternative comparisons are provided.

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 read-only and non-destructive behavior. The description adds that it evaluates scalp visibility and hair distribution patterns, but does not mention asynchronous polling behavior (evident from the polling parameter) or how results are returned. It does not contradict 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 long, marketing-heavy sentence with redundant claims like 'empowering businesses' and 'expert-level personalization at scale'. It is not concise and contains fluff that could be removed without losing core meaning.

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?

Although the output schema likely covers return values, the description omits critical operational context such as the asynchronous task nature (implied by polling parameter) and the need for a publicly accessible URL or file ID. It provides sufficient purpose but lacks practical usage details.

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 the polling flag and request sub-fields (src_file_url and src_file_id) with examples. The description adds minimal parameter meaning, only vaguely referencing a single uploaded image, so it does not significantly enhance understanding 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 the tool performs a photo-based assessment that classifies hair density into four levels using scalp visibility and hair distribution patterns. This specific verb+resource+outcome clearly distinguishes it from sibling tools like AI-Hair-Length-Detection or AI-Hair-Type-Detection.

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 context for when to use the tool: for businesses wanting fast, professional, photo-based hair density assessment without physical tools. However, it does not explicitly state when not to use it or mention 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?

Annotations are present but sparse (readOnlyHint false, openWorldHint true, etc.). The description adds only marketing claims like 'advanced Generative AI' and 'naturally blends,' but does not disclose operational behavior such as polling, task lifecycle, required uploads, or output handling. No contradiction with annotations, but the burden of disclosure is mostly unmet.

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 redundant promotional language ('No more guessing games,' 'perfect time to experiment') and ends with an incomplete 'Use case' line. It is not tightly written and contains several sentences that do not add functional 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?

This is an async task-based tool requiring template selection, an input image, and likely polling. The description fails to mention these prerequisites, the need to consult the Templates sibling tool, or any workflow steps. Even with an output schema, the description leaves critical usage context 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 only 50% — the top-level 'request' property lacks a description. The tool description does not explain how template_id or input file parameters relate to the advertised features, nor does it mention the need to list templates first. The schema provides some parameter descriptions, but the description fails to add meaningful context.

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 indicates this tool is for virtually trying on hair extensions, mentioning lengths, styles, colors, and bangs. It distinguishes itself from sibling hair tools by focusing specifically on hair extensions and blending with current hair length, though the marketing tone obscures the precise action.

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 on when to use this tool versus alternatives like AI-Hair-Style-Virtual-Try-On or AI-Hair-Color-Virtual-Try-On. The 'Use case: Suggestions for How to Shoot:' section is truncated and provides no actionable direction.

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 consistent with annotations (readOnlyHint, idempotentHint, destructiveHint) and adds the obvious 'list' behavior, but provides no extra behavioral context such as what the templates contain or how they are used. With strong annotations, this is acceptable but minimal.

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 fluff or repetition. It is well-front-loaded and earns every word.

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 is simple with no parameters and an output schema present. The description is adequate for a minimal listing operation, though it does not explain the relationship to the try-on tool, which would be helpful but not necessary for basic 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 tool has zero parameters, so the description does not need to explain parameter details. The schema is fully covered, and the baseline of 4 applies for param-less tools.

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 ('List predefined templates'), but it omits the domain (hair extension virtual try-on) which is only present in the tool name. It is clear but does not explicitly distinguish from sibling template-listing tools without name context.

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 AI-Hair-Extension-Virtual-Try-On or other template listers. The description lacks any contextual cues for 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds that exactly 3 photos (front, left, right) are required and that analysis is fast, but this is mostly redundant with the input schema. It does not disclose potential error conditions, rate limits, or polling behavior beyond what the schema already shows.

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 repetitive ('in seconds' appears twice, 'efficient' appears once) and includes marketing fluff about enabling businesses and avoiding consultations/hardware. The second paragraph adds little actionable information and should be condensed or removed for API consumers.

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 simple schema and output schema, so the description doesn't need to explain return values. However, it lacks guidance on how to feed the 3-photo requirement into the request (beyond schema examples) and doesn't mention the file upload prerequisite. It is adequate but not complete for an agent unfamiliar with the API.

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%; the request parameter lacks a top-level description, but its sub-properties (src_file_urls/src_file_ids) are described. The description clarifies the need for three views front/left/right, which helps map to the array, but it does not explain the polling parameter or how to obtain file URLs/IDs, leaving some gaps.

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 an AI hair frizz analyzer that performs precise frizz analysis using 3 photos. It distinguishes itself from sibling detection tools (e.g., AI-Hair-Density-Detection) by specifying the unique 3-photo front/left/right input and the 180° full-view approach.

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 context for when to use this tool: when hair frizziness needs to be assessed for tailored product recommendations. It implies the tool is for analysis rather than virtual try-on, but does not explicitly name alternatives or specify when not to use it, so it falls short of 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds value by mentioning the five-category output range and the image dataset, but it does not disclose operational behaviors like processing time, required image quality, or possible variations in results.

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 paragraphs with useful front-loaded information (first sentence), but the second paragraph contains marketing fluff ('meticulously trained on a vast dataset') that does not aid API understanding. It is concise overall but not every sentence earns its place.

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 simple detection nature, output schema existence, and annotations, the description provides adequate but not complete context. It mentions the category range but does not explicitly list all five categories or give operational details. The tool is functional but has room for improvement in usage and parameter 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 50%, with only 'polling' clearly described and 'request' lacking a direct description. The tool description is entirely silent on the input parameters (src_file_url or src_file_id), so the agent must rely on examples within the schema. The description does not 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 clearly states the tool's purpose: analyzing and measuring hair length, specifically categorizing five distinct lengths 'from above-the-ear to mid-back.' This distinguishes it from sibling tools like AI-Hair-Density-Detection or AI-Hair-Frizziness-Detection.

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 haircare brands and salons seeking personalized products, but it does not explicitly state when to choose this tool over alternatives or when not to use it. No exclusions or sibling comparisons are provided, leaving usage context vague.

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 readOnlyHint=false and no other behavioral annotations, the description is expected to explain what side effects occur (e.g., generating a new image) and any asynchronous behavior. It only promises an 'AI-powered hair changer' without technical detail, and it fails to mention the polling option or that a reference source is required. No contradiction, but no added value.

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 but wastes space with promotional filler like 'Discover a world of styles' and 'Find your ideal hairstyle now!'. It does not front-load technical facts, and the structure is a marketing blurb rather than a concise tool specification.

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 (six request variants), the presence of a template option, and many similar sibling tools, the description is severely incomplete. It does not explain the distinction between using a reference image vs a template, nor the 'hair_color' option relevant to templates, nor the polling behavior. The output schema exists, so return values are not required, but the input narrative is almost entirely absent.

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 contains zero parameter information. It does not explain how to provide the source image, reference image, or template ID. With schema description coverage at 50%, the description should at least mention the required inputs, but it does not, leaving the agent to rely solely on the schema's sparse field 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 description uses verbs like 'try' and 'hair changer', indicating the tool changes hairstyles, which is a clear purpose. However, it does not distinguish this from many sibling tools like AI-Hair-Color-Virtual-Try-On or AI-Wavy-Hair-Virtual-Try-On, and the marketing language ('Discover a world of styles') adds vagueness.

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 its many siblings. It does not mention the need for a source image and a reference/template, nor any prerequisites. The only audience context ('catering to both women and men') is not actionable 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=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds no new behavioral details such as pagination, filtering, or template structure; it merely restates the action. There is no contradiction with annotations, so a neutral score is appropriate.

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 short sentence, 'List predefined templates v2.1.', which is fully front-loaded and contains no unnecessary words. It efficiently conveys the core purpose without excess.

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 is a simple list operation with no parameters and an existing output schema, so the description doesn't need to explain return values. However, it lacks any context about why this template list differs from the many sibling template tools or how it should be used alongside AI-Hair-Style-Virtual-Try-On, leaving some contextual gaps.

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 zero parameters, so there are no parameter semantics to clarify. The baseline for zero parameters is 4, and the description introduces no ambiguity, so this score holds.

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 'List predefined templates v2.1' uses a specific verb ('List') and resource ('predefined templates') to clearly state the action and object. However, it does not differentiate this from sibling template-listing tools like AI-Bangs-Filter-Virtual-Try-On-Templates, so it lacks explicit sibling distinction.

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 the many sibling template tools, nor does it mention how it relates to the associated AI-Hair-Style-Virtual-Try-On tool. There are no alternatives, prerequisites, or exclusions provided.

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 and destructiveHint=false, so the safety profile is covered. The description adds that the output is a 'full hair profile' with nine classification types, which is useful but vague. It does not disclose operational details such as async polling or image requirements, but the schema already covers these, so the description adds marginal value beyond 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 padded with marketing phrases like 'Imagine having an AI hair expert in your pocket' and 'spot-on product recommendations,' which are irrelevant for an AI agent. It wastes tokens on hype instead of providing crisp, technical details. While it is only three sentences, the content is not efficiently structured for tool selection.

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 complexity (oneOf request schema, polling parameter, async behavior), the description is incomplete. It explains the high-level purpose but omits usage context, such as requiring exactly three images (front, right, left) or that the tool is read-only and asynchronous. The output schema may cover return details, but the description still fails to provide a complete picture for an agent deciding to invoke it.

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 does not explain the parameters at all. Schema coverage is 50%; the polling parameter is well-described in the schema, and the request object includes descriptions for src_file_urls and src_file_ids. The description's mention of 'ten unique curl shapes' refers to output, not input, so it fails to compensate for the 50% gap or add meaning to the parameters.

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 conveys that the tool analyzes hair texture, thickness, and curl pattern to classify hair into nine types. It distinguishes from sibling hair tools (e.g., density, length, frizziness) by explicitly focusing on curl shape and type. However, the marketing tone obscures the direct imperative verb ('detect' or 'classify') and includes irrelevant brand messaging.

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 explicit guidance on when to use this tool versus alternatives like AI-Hair-Density-Detection or AI-Hair-Frizziness-Detection. It does not mention required inputs (three images) or any exclusions, leaving the agent without clear selection criteria.

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 the tool is not read-only, not idempotent, and not destructive, but the description adds little about what actually happens during processing. It mentions 'add natural volume' but does not disclose side effects, input requirements (e.g., face photo), output format, or any operational caveats. The marketing language ('Say goodbye to bad hair days') provides no behavioral value.

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, repetitive (mentions volume multiple times), and ends with an incomplete 'Use case: Suggestions for How to Shoot:' that appears truncated. Marketing fluff like 'Say goodbye to bad hair days' adds no informational value. A more concise, structured description would be more effective.

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 complexity (nested request object, polling, output schema, and many sibling tools), the description is incomplete. It fails to mention the need to list templates first, how to upload files, or what the result looks like. The existence of an output schema lowers the burden, but the description still omits critical workflow details and leaves a dangling 'Use case' section.

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%, and the description provides no parameter-level information. The schema documents 'polling' and 'request' with nested fields, but the description does not clarify how to use them, what template_id means, or how to obtain src_file_url/src_file_id. The description's vague mention of 'Suggestions for How to Shoot' does not compensate for the lack of parameter guidance.

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: adding natural volume to fine or thinning hair and filling gaps with AI. It distinguishes itself from sibling tools like hair extension or color by focusing specifically on volume. The verb+resource is specific and observable.

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 dating profiles, resumes, and social photos, giving some context. However, it does not explicitly state when to choose this tool over alternatives like AI-Hair-Extension-Virtual-Try-On, nor does it provide exclusions. The abrupt 'Use case: Suggestions for How to Shoot:' with no content weakens 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 readOnly, idempotent, and non-destructive behavior. The description simply says 'List', which is consistent but adds no additional context such as output format or pagination. It does not contradict annotations, but the description contributes little beyond what annotations already convey.

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, direct sentence that communicates the core purpose without any filler. It is appropriately sized for a zero-parameter list operation.

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 low complexity (no parameters, simple list operation) and the presence of an output schema, the description is mostly sufficient. However, it is vague about what 'templates' are and could be confused with the many similar template-listing sibling tools without relying on the tool name. A brief note about being specific to AI Hair Volume would improve 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, so the description does not need to explain parameter details. The baseline for no parameters is 4, and the description adequately avoids adding unnecessary parameter information.

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 'List predefined templates' clearly states the action (list) and resource (predefined templates). The tool name disambiguates it from sibling template tools, but the description itself does not explicitly distinguish which templates it lists, relying on the name for context.

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 the related 'AI-Hair-Volume-Virtual-Try-On' or other template-listing tools. There is no mention of prerequisites or whether this should be called before a try-on operation.

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 the tool is not read-only and not idempotent, but the description adds little beyond that. It claims 'applied instantly,' which may mislead about the asynchronous polling behavior implied by the 'polling' parameter, and it does not explain the workflow or potential 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 concise at two sentences and front-loads the core purpose. However, the second sentence about beauty experts is promotional and does not add functional value, slightly reducing efficiency.

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 two source options and polling, plus many similar sibling tools, the description is too minimal to guide an agent. It does not explain the need for a template or the async behavior, relying entirely on 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?

The description does not mention any parameters, leaving the schema to carry all meaning. With only 50% schema description coverage, the description should compensate by explaining the request structure (template_id, src_file_url, src_file_id), but it offers nothing.

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 applies professionally designed facial looks to user photos, which is a specific verb-resource combination. However, it does not explicitly differentiate from similar siblings like AI-Makeup-Virtual-Try-On, so it falls short of full clarity.

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, nor does it mention prerequisites like selecting a template or uploading a file. The only contextual hint is the phrase 'complete workflow,' but there is no explicit usage direction.

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 states the core behavior (listing templates), which is not explicitly in the annotations. But it adds no further behavioral context such as pagination, response size, or whether the templates are static per user. Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered; however, the description does not go beyond a basic statement.

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 short sentence that is front-loaded and easy to scan. It is concise, though perhaps too terse, lacking any additional context that could aid understanding. It is not a tautology, but it is minimal.

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 simplicity of the tool (no parameters, output schema exists, strong annotations), the description is mostly complete. However, it does not explain how the templates relate to AI-Look-Virtual-Try-On or whether they are required for that operation. This missing linkage might cause an agent to underutilize the 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 tool has zero parameters, and the schema has 100% description coverage. With no parameters to clarify, the baseline of 4 applies. The description does not need to elaborate on parameter usage, as there are none.

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' and a clear resource 'predefined templates', making the tool's function explicit. However, it does not differentiate this template listing from the many sibling template-listing tools (e.g., AI-Hair-Style-Virtual-Try-On-Templates), which all likely have the same generic wording.

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 guidance on when to use this tool versus alternatives. The description does not mention any prerequisites, related tools, or typical invocation patterns (e.g., 'use to get template IDs for AI-Look-Virtual-Try-On'). It is purely a one-line description without any contextual usage advice.

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 only an input quality requirement (clear face visibility) and the basic upload/reference workflow. It does not disclose processing behavior, side effects, wait times, or how files are handled, which matters given annotations indicate the tool is not read-only and may have open-world effects. No contradiction with annotations, but little extra context.

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 overly promotional and verbose, with repetitive phrasing ('upload... upload...'), exclamation points, and a dangling 'Samples:' that suggests incomplete content. It lacks a crisp, front-loaded summary and would be better as a single direct sentence.

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 and the presence of an output schema, the description covers the core concept and input roles. It does not mention the polling parameter or file ID vs URL options, but these are documented in the schema. Overall adequate but not comprehensive for an agent deciding how to invoke it.

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 maps 'src' to the target photo ('upload a photo of yourself') and 'ref' to the makeup reference ('upload a photo of your favorite makeup look'). This semantic distinction is absent from the schema's generic 'Url of the file to run task' descriptions, adding significant meaning beyond the structured fields.

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 conveys that the tool transfers a makeup look from a reference photo to a target photo (the user's own photo). It describes the workflow of uploading two photos, which distinguishes it from single-photo virtual try-on siblings, even though it doesn't name them 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?

The description provides procedural instructions with prerequisites ('upload a photo of yourself where your face and its features are clearly visible'), implying the tool is for replicating a specific makeup look. However, it does not explicitly state when to use this tool over alternatives like AI-Makeup-Virtual-Try-On, nor does it mention when not to use it.

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 declare readOnlyHint=false and openWorldHint=true, so the description carries the burden of explaining execution behavior. It fails to disclose that this is an asynchronous task (with polling), how the input image is supplied (URL vs file ID), or what the response contains. The technology-focused text doesn't address these API behaviors.

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 long and filled with marketing claims about patented technology and hyper-realism. Bullet points and 'Core Concepts' take up space without providing actionable invocation details. A concise, front-loaded description of actual tool behavior would be far more useful.

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 asynchronous tool with a large nested schema and many sibling makeup/analysis tools. The description lacks essential details: how to construct a valid effects array, the polling mechanism, the two source-input options, and the nature of the output. The rich schema doesn't compensate for the description's failure to explain the overall invocation flow.

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 is highly detailed, but the description adds no semantic information about top-level parameters like 'request' or 'polling'. It doesn't explain that the request accepts either src_file_url or src_file_id, nor does it clarify the effects array structure. With only 50% schema coverage, the description should compensate but doesn't.

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 applies realistic makeup effects to user-provided selfie images, using a specific action ('apply') and resource ('makeup effects onto selfie images'). However, it's wrapped in marketing language and doesn't distinguish it from similar sibling tools like AI-Makeup-Transfer or AI-Look-Virtual-Try-On, so it doesn't merit 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 provides no guidance on when to use this tool versus its siblings, no prerequisites, and no mention of the request structure or asynchronous behavior. It reads as a feature list rather than a usage guide, leaving the agent without context 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?

Annotations indicate readOnlyHint=false and openWorldHint=true, but the description does not explain the tool's side effects, asynchronous processing, polling behavior, or input/output requirements. It describes interactive previews but not how results are returned or whether it creates files, so an agent lacks behavioral transparency.

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 five-sentence description is promotional and repetitive, using phrases like 'enhance the online experience,' 'effortlessly explore,' and 'streamlines product discovery' that add no operational value. It lacks front-loaded technical summary and every sentence could be cut without losing agent-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?

For a complex tool with a large nested schema, polling flag, and file-reference mechanics, the description provides no invocation guidance, no output explanation, and no mention of the request structure. An agent would have to rely solely on the schema, which is insufficient given the multiple discriminated unions and obscure parameter relationships (sub_type, texture, ref_file_url vs index).

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 has 50% description coverage and the description adds no parameter semantics; it only generically mentions 'unlimited color and texture options' without explaining the effects array, finger selection, effect_type, or ref_file mechanics. It does not compensate for the undocumented parameters in the large request object.

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 nail virtual try-on capability, mentioning nail styles, artificial/acrylic/press-on/gel, and before/after comparisons. It distinguishes from hair/makeup siblings by focusing on nails. However, it uses promotional language ('offers an innovative way to enhance') rather than an explicit verb like 'applies' or 'generates,' making the action less direct.

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 customers exploring nail art should use this tool ('customers interested in nail art'), but it provides no guidance on when to choose this over sibling tools like AI-Makeup-Virtual-Try-On or AI-Look-Virtual-Try-On. No prerequisites, exclusions, or alternative recommendations are given, leaving the agent without selection criteria.

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 and destructiveHint=false, so the safety profile is established. The description adds that the tool works from a single selfie and returns scores/masks, but it does not disclose additional behavioral details such as async/polling behavior, image size limits, or error handling. It does not contradict 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 excessively verbose and opens with marketing filler ('AI skincare analysis technology harnesses the power...') before reaching the operational details. The critical instructions about HD/SD fallback and URL handling are placed at the very end, making the structure poorly front-loaded 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?

The description covers the essential workflow (analyze selfie, return scores/masks) and includes the important HD/SD fallback and URL-return requirements. However, it does not mention the polling parameter, the choice between src_file_url and src_file_id, or any output schema specifics, relying on the schema to carry that information. For a tool of this complexity, the description is only partially 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 is fully documented with descriptions for all parameters, including detailed per-feature definitions and format options, so the baseline is 3. The description adds minimal parameter-level value beyond the schema, only reiterating the HD/SD distinction and the requirement to use a publicly accessible URL.

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 performing AI skin analysis on a front-facing selfie, providing 'skin concern scores and detection masks.' It states the core resource (facial skin image) and action (analyze), but it is buried in promotional language and does not differentiate from sibling tools like AI-Fitzpatrick-Skin-Type-Analysis or AI-Skin-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?

The description provides clear usage guidance: 'You should run HD first, if there's problem with the image size, run the SD next.' It also specifies a mandatory response behavior with the exact URL. However, it does not explicitly mention when to choose this tool over alternative skin analysis tools, so it stops short of 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 only minimal behavioral context, such as 'non-invasive previews' and 'generated in seconds'. It does not disclose operational behaviors like asynchronous processing, polling, file input requirements, or limitations. With annotations already indicating mutable/non-destructive, the description adds little beyond what is already declared, and the lack of operational detail is a significant gap.

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 long, marketing-heavy wall of text with no bullet points or structured sections. While it contains useful information, it is verbose and repetitive (e.g., repeated references to professionals, brands, and evidence-based efficacy). A concise operational summary would be far more effective, but the current structure obscures the core 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 two input modes (URL/file ID), an async polling parameter, and a defined output schema, the description is operationally inadequate. It does not mention how to provide the source image, the need for polling, or the output format. The description is entirely focused on marketing value propositions and lacks the technical invocation details an agent needs to use 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?

The description lists the ten skin concern parameters (acne, pores, etc.), which helps identify available options, but it does not explain the 0-1 scale or the critical requirement to provide a source image (src_file_url or src_file_id). The schema descriptions for individual parameters are clear, but the top-level 'request' parameter remains unexplained. Overall, the description adds moderate value but fails to compensate for the missing high-level input structure.

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 it is an AI-powered skin simulation tool that visualizes before-and-after facial skin conditions, which is a specific verb and resource. It lists the supported skin concerns (acne, wrinkles, etc.) which adds clarity, but it does not explicitly distinguish it from sibling tools like AI-Skin-Analysis, relying instead on the implicit simulation vs. analysis distinction.

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 broad use cases (skincare brands, clinics, e-commerce) but does not explicitly state when to use this tool versus alternatives or exclude other tools. No mention of 'use this for X, not Y' or any differentiation from sibling tools, leaving the agent to infer usage context from the marketing tone.

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 this is not read-only and not destructive, and the description aligns by noting it 'transforms' images. It adds a prerequisite ('Upload a face') and mentions the AI-generative nature, but it does not disclose async/polling behavior, potential costs, or limitations (e.g., input requirements). The description adds some context but not a full behavioral profile.

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 heavily padded with promotional language ('turn that frown upside down', 'spreads happiness everywhere') and repeated phrases. While it includes a structured numbered list for smile styles, the overall text is verbose and contains many sentences that add no operational value. It is not concise or front-loaded with essential facts.

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 core functionality and style choices, and the output schema likely handles return values. However, it omits crucial operational details such as async task behavior, how to handle polling, cost implications, and technical constraints (e.g., face visibility, file size limits). For a tool with multiple parameters and a task-based workflow, this is a noticeable 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 only 50%, but the description compensates by elaborating on the expression_type parameter with detailed explanations of both enum values and their ideal use cases. It does not add meaning to the polling or src_file parameters, but the schema already documents those sufficiently. The added enum context exceeds the schema's bare list.

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 transforms sad/neutral facial expressions into smiles using generative AI. It explicitly mentions two smile styles and distinguishes itself from sibling tools by focusing exclusively on smile generation, with a specific verb ('transform') and resource (face expressions).

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 context for when to use the tool (photo editing, fun content, positivity) and recommends ideal scenarios for each smile style (portraits, social media, professional photos). However, it does not explicitly say when not to use it or name alternative tools for other facial modifications, leaving selection guidance incomplete.

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 focuses solely on the aesthetic outcome and does not disclose operational behavior like task-based asynchronous processing, input source requirements (public URL or file ID), or output format. Annotations already convey readOnlyHint=false, but the description adds no extra side-effect or safety context.

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 excessively verbose with redundant sections (e.g., 'Key Features' repeating earlier points). It is not front-loaded with essential usage information, and every sentence does not earn its place.

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 request object with two input variants (src_file_url vs src_file_id) and a polling parameter, the description is operationally incomplete. It does not explain how to invoke the API, how to handle task status or results, or how to use polling, even though an output schema exists.

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 only 50%, and the description uses marketing language like 'adjustable whitening intensity' without explaining the 0-100 scale or the skin smoothing parameters. It does not compensate for undocumented fields such as polling, index, or effect details, leaving the agent without enough parameter understanding.

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: it 'brightens teeth naturally and accurately' in photos, which is a specific verb+resource. This distinguishes it from siblings like AI-Smile. However, the purpose is buried in lengthy marketing text and lacks a concise one-line summary.

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 such as AI-Smile or AI-Teeth-Whitening-Detection. It does not mention prerequisites, exclusions, or correct use cases, leaving the agent without context 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 adds no behavioral context beyond the annotations. It does not mention asynchronous behavior, polling, or return semantics, despite the schema's polling parameter. It does not contradict the annotations, but it also provides no extra transparency.

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 redundant information. It is front-loaded with the verb and resource, but it borders on under-specification.

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 moderate complexity (two parameters, an object property, output schema, and many sibling tools), the one-sentence description is insufficient. It does not convey what the detection task outputs or how it relates to similar tools, leaving much to inference.

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 has 50% coverage; only the 'polling' parameter is fully described, while the 'request' parameter lacks its own description. The tool description does not compensate for this gap, adding no meaning beyond the schema's existing property-level 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 description clearly states the action ('Run') and the resource ('AI Teeth Whitening detection task'). However, it does not explicitly differentiate from the sibling 'AI-Teeth-Whitening' tool or explain what detection entails, so it is clear but not fully distinguishing.

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 tools. There is no mention of prerequisites, alternatives, or context in which detection is appropriate.

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, non-idempotent, non-destructive behavior, but the description adds no operational context such as how the filter is applied, whether an uploaded image is required, or what the output contains. Claims like 'precision and realism' and 'risk-free' are promotional rather than transparent behavioral disclosures.

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 repetitive marketing sentences and ends with an incomplete heading. It is not front-loaded, and most sentences do not earn their place; a truncated 'Suggestions for How to Shoot' further weakens structure.

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 the description leaves major gaps around template selection, source image requirements, and how the virtual try-on process works. It is not enough to guide correct invocation beyond what the schema already states.

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 description never mentions template_id, src_file_url, src_file_id, or polling. The schema provides some parameter descriptions, but with only 50% coverage, the prose needed to compensate; 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 indicates this is a virtual hair try-on tool for curly/wavy styles ('curly hair filter lets you experiment with a fresh, fabulous hairstyle', 'loose waves', 'wild afro'). It is not a tautology, but it lacks a precise operational verb like 'applies' and does not explicitly differentiate itself from sibling hair-style 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?

There is no guidance on when to use this tool versus alternatives such as AI-Hair-Style-Virtual-Try-On or the dedicated Templates tool. The phrase 'perfect for anyone wanting to experiment... risk-free' is generic marketing, and the 'Suggestions for How to Shoot' section is empty or truncated.

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 read-only, non-destructive behavior is established. The description adds no extra context beyond 'list' (which is consistent with the annotations) and does not describe output structure or any side effects, but no contradiction exists.

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. It gets straight to the point, though it could be marginally more informative (e.g., 'wavy hair templates') without sacrificing brevity.

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 (0 parameters, output schema present, annotations provided), the description is minimally adequate. However, it lacks any contextual link to the related virtual try-on tool and does not explain the purpose of these templates or how they fit into the broader workflow.

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 input schema is empty with 100% coverage. The description does not need to explain parameters, and the baseline for 0-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 verb 'List' and object 'predefined templates', which clearly indicates the action, but it is generic and does not specify 'wavy hair' or 'virtual try-on'. The name and title provide that context, yet the description itself does not differentiate from other template-listing 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 is given about when to use this tool vs. alternatives. It does not mention that these templates are meant for the AI-Wavy-Hair-Virtual-Try-On tool or how to use the returned template identifiers with it.

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 full burden. It discloses that the tool is an interactive widget, uploads directly from the browser, bypasses sandbox egress restrictions, and returns file IDs. These are meaningful behavioral traits. However, it does not mention potential limitations like file size limits or authentication requirements, though the interactive nature implies user involvement. This is more transparent than average but not exhaustive.

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 split into three meaningful sections using ' /n ' separators: purpose, usage, and fallback behavior. It front-loads the main function and keeps each sentence relevant. While slightly longer than the bare minimum, every sentence contributes value, and the structure is logical and scannable.

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 interactive nature and the availability of an output schema (not shown but referenced), the description covers the essential context: what it does, when to use it, that it returns file IDs, and that it bypasses sandbox restrictions. It does not explicitly differentiate from the sibling 'upload_file' tool, but the description is still complete enough for the main use cases, including the fallback behavior for Get-Upload-API-Info.

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 only parameter (task_type), which already explains its purpose ('Pre-select a task type. If omitted, the user picks one from the dropdown.'). The tool description does not add parameter-specific information but does provide context by listing example AI tools that the file IDs will be used with, which helps understand the enum. Per the baseline rule, with high schema coverage, a score of 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?

The description clearly states the verb and resource: 'Interactive drag-and-drop widget for uploading images (JPEG, PNG) or videos (MP4).' It also explains the output ('Returns file IDs for YouCam AI tools'), which distinguishes it from siblings like Get-Upload-API-Info or upload_file by specifying the interactive browser-based nature and the intended downstream usage.

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 explicitly states when to use the tool: 'Use when the user wants to upload or select an image/video (e.g. upload, drag & drop, choose file, pick image/video), or before any AI-* tool requiring src_file_id or ref_file_id when no file ID or URL is available.' It also names the alternative and fallback condition: 'Also use as the mandatory fallback whenever Get-Upload-API-Info cannot complete the upload for any reason.' This provides clear when-to-use and when-not-to-use 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, 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context by explaining the polling parameter: it can either 'keep polling until the task finishes' or 'return immediately without waiting.' It also clarifies that status and results are retrievable for previously initiated tasks, going beyond the structured fields. 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?

The description opens with a crisp purpose statement, followed by a structured 'WHEN TO USE' bullet list. It is somewhat longer than strictly necessary but each bullet adds a distinct real-world scenario, making the length justified. The front-loaded purpose ensures quick comprehension, and the formatting improves scannability.

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 status-checking tool with an output schema, the description is reasonably complete. It covers the core behavior, polling semantics, and typical usage scenarios. It does not mention error handling or timeouts beyond the polling parameter, but given the output schema exists and the tool is straightforward, these omissions are not critical.

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 describes all four parameters with full 100% coverage, including defaults and enum values. The description adds no extra parameter-level detail beyond what the schema already provides. It references task_id in usage examples but does not elaborate on parameter semantics, so the baseline score of 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?

The description starts with a clear, specific verb-resource pair: 'Retrieves the current status and results of a previously initiated AI processing task.' It unequivocally states the tool's function and uniquely identifies it among siblings—no other tool appears to handle task status retrieval. The rest of the description reinforces the purpose with concrete user scenarios.

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?

A dedicated 'WHEN TO USE THIS TOOL' section explicitly lists four distinct triggers, such as a user providing a task_id from a previous session or resuming a conversation with a pending task. This provides clear guidance on when to invoke the tool. While no alternatives are named, there are no sibling tools with overlapping functionality, so the exclusion guidance is implicitly satisfied.

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 goes beyond the readOnlyHint and idempotentHint annotations by disclosing the specific fallback behavior and retry prohibition: 'do not retry. Immediately use the File Upload Widget instead.' It also details supported file types (JPEG, PNG, MP4) and that it returns both the endpoint and example request/response, setting clear behavioral 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 concise and well-structured: first sentence states the purpose, second provides usage guidance, and the remainder covers fallback behavior and exclusions. Every sentence adds distinct value 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?

Given the presence of an output schema (as indicated) and strong annotations, the description is complete. It explains what the tool returns (endpoint and example request/response), when to use it, which file types are supported, and how to handle upload failures. No important 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?

The schema fully describes the only parameter (task_type) with an enum and description, achieving 100% coverage. The description itself adds no parameter-specific details beyond contextualizing task_type as an 'AI task,' which is already in the schema. Therefore, per rubric, the baseline of 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?

The description clearly states the tool's function: 'Returns the upload API endpoint and example request/response for uploading images (JPEG, PNG) or videos (MP4) to YouCam AI.' It uses a specific verb and resource, and distinguishes the tool from siblings like File-Upload and upload_file by focusing on API info rather than performing the upload itself.

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?

Explicitly states when to use the tool: 'Use when the user wants to upload an image/video via the API.' It also names an alternative and a fallback: 'If upload cannot be completed... Immediately use the File Upload Widget instead.' Additionally, it provides a clear exclusion: '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 provided, the description bears full responsibility for behavioral disclosure. It mentions base64 encoding and the return of file_id(s), but it omits critical behavioral traits such as file size limits, expiration, authentication requirements, or error handling. This is insufficient 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?

The description is a single sentence that is direct and free of unnecessary detail. It front-loads the action and efficiently conveys the key resource and output, earning a perfect score.

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?

Although the tool has only two parameters and an output schema, the description is too minimal. It lacks parameter semantics, usage guidance, and any constraints, making it incomplete for correct invocation, especially given the absence of annotations and schema descriptions.

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 must compensate. While it hints that 'files' contains base64-encoded media, it does not explain the structure of the file objects or the meaning and allowed values of 'task_type'. Significant ambiguity remains for both parameters.

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 a specific action (Upload), a resource (base64-encoded media files to the YouCam API), and an output (file_id(s)). However, it does not differentiate from the sibling tool 'File-Upload', which may be similar or serve as an alternative, so it falls short of 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?

There is no guidance on when to use this tool versus other file-related tools like 'File-Upload' or 'Get-Upload-API-Info'. It does not mention prerequisites, use cases, or exclusions, leaving the agent without context for appropriate selection.

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