APICK Vision

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

Annotations already mark the tool readOnly, and the description adds useful behavior: it returns bounding boxes, optionally returns the annotated image when include_img=1, and states a per-call cost (3 points). 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 short and front-loaded with the core detection function. The bilingual repetition adds some redundancy, but it remains compact and each meaningful behavior (bounding boxes, include_img, cost) is included.

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 read-only detection tool with no output schema, the description gives enough high-level output information (bounding boxes and optional annotated image), lists target vehicle types, and notes the cost. It could detail bounding-box formatting, but that is not critical for tool 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?

All three parameters already have detailed schema descriptions (100% coverage), so the description does not need to re-explain them; it only adds context for include_img. This matches the baseline of 3 for a fully covered 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 uses a specific verb ('Detect') and a concrete resource ('vehicles... in an image'), enumerates vehicle classes, and names the output (bounding boxes). This clearly distinguishes it from sibling detection tools like face_detection and person_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?

It provides a clear context for use: vehicle detection in images, and even notes a parameter option (include_img=1) for returning an annotated result image. However, it does not explicitly name alternatives or exclusion cases, so it stops short of a fully explicit when/when-not comparison.

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 readOnlyHint annotation already indicates a safe read operation, and the description adds valuable context: it returns a ZIP file, extracts at regular intervals, and costs 10 points per call. This goes beyond the annotation and helps the agent understand side effects and cost. No contradiction detected.

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, with a single English sentence and a Korean duplicate alongside the cost note. It front-loads the core action and output format with no unnecessary filler. The bilingual repetition is acceptable and the cost disclosure is important, so every part 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 tool's moderate complexity, the description covers the essential purpose, output format, and cost. The schema fully documents parameters and limits (e.g., max 200MB). It does not explain processing time or failure behavior, but for a read-only extraction tool with strong schema coverage, this is reasonably 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 schema descriptions cover all parameters 100%, so the baseline is 3. The description does not add further semantic detail about the parameters beyond what the schema already provides. It mentions 'regular intervals' but does not clarify count or width behavior beyond their schema 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 a specific action ('Extract preview thumbnail images') on a specific resource ('from a video'), and specifies the output format ('return them as a ZIP file'). This distinguishes it from sibling tools like face_detection or video_to_mp3, which serve different purposes.

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 it (when you need preview images from a video) but does not explicitly mention alternatives or exclusion criteria. It would benefit from stating that it is not for audio extraction or detection tasks. Usage is implied rather than explicitly guided.

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 readOnlyHint=true, so the agent knows this is a safe read operation. The description adds useful non-annotation context: the cost of '[호출당 3포인트]' (3 points per call) and that 'use_feature=1 입력 시 얼굴 특징 정보를 함께 반환합니다' (returning feature info when use_feature=1). This goes beyond what annotations provide, though it does not mention potential limitations like image resolution or error 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 contains bilingual redundancy: 'Detect faces in an image and return their coordinates' followed by its Korean equivalent '이미지 파일에서 얼굴을 인식해 해당 좌표를 반환합니다' which says the same thing. The key cost information is placed at the end rather than front-loaded. Still, it is relatively short, so it is not rambling, but the duplication is unnecessary.

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 simple tool with 3 parameters and no output schema. The description explains the basic return value (coordinates) and optional feature info, which is a minimum viable explanation. However, it does not describe the structure of the coordinates (e.g., bounding box format), how multiple faces are handled, or any error cases. Without an output schema, more detail would benefit the agent, but the tool's simplicity keeps the gap modest.

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 provides 100% parameter description coverage, including image_url format/size, threshold range/default, and use_feature meaning/default. The description only repeats the use_feature parameter behavior without adding new meaning beyond the schema. Baseline of 3 applies because the schema handles the parameter documentation.

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 explicitly states 'Detect faces in an image and return their coordinates,' which specifies the verb (detect), resource (faces in an image), and output (coordinates). This clearly distinguishes it from sibling tools like car_detection, person_detection, and nsfw_detection by the target object (faces).

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, nor does it mention any exclusions or prerequisite conditions. The usage is implied by the action ('detect faces'), but there is no direct comparison to person_detection or other related tools. This matches the 'implied usage' level.

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 readOnlyHint=true annotation already indicates a safe, non-mutating action. The description adds useful context such as the per-call point cost and the comparison use cases, but it does not disclose details like score range, ordering effects, or failure behavior, which would enhance 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 concise and front-loaded, with English and Korean versions plus a use-case sentence and cost note. The bilingual repetition is mild but likely intentional for a Korean-language context, so it remains efficient.

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, annotations cover safety, and the schema fully describes parameters. The description adds use cases and cost, but no output schema exists and the description does not specify the similarity score format or range, which would make it fully 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?

Schema description coverage is 100%, with both image_url and compare_image_url fully described. The description adds no parameter-specific detail beyond what the schema already provides, 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 uses a specific verb ('Compare') and resource ('base image with another image'), and states it returns a similarity score. It clearly distinguishes from sibling detection tools like face_detection or nsfw_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 by stating it is used for '원본 검증' (source verification) and '중복 이미지 탐지' (duplicate image detection). It does not explicitly name alternatives or exclusions, but this context is sufficient for an agent to select the tool over the listed siblings.

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 readOnlyHint=true, so the read-only nature is known. The description adds cost per call and the behavior of the detail=1 parameter, which is useful context. However, it does not disclose potential side effects beyond the annotation, such as whether the image is stored or the meaning of 'detailed judgment 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 bilingual, repeating the same meaning in English and Korean, which adds some redundancy. However, it remains relatively short, front-loaded with the core purpose, and doesn't waste words beyond the bilingual duplication. It earns a 4 rather than 5 due to the duplication.

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 simonplicity of the tool (2 params, no output schema), the description explains the return value (nsfw_score) and the optional detailed results, plus cost. It could mention score ranges or error cases, but for a detection tool with good schema annotations, it is fairly 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 covers both parameters with descriptions (image_url format/size, detail toggle), so the schema carries the semantic weight. The description repeats the detail=1 behavior but adds no new parameter details, keeping this at the baseline score for full 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 states the tool's function with a specific verb ('Detect') and resource ('image'), specifying it detects NSFW (violent or sexually explicit) content and returns an nsfw_score. This distinguishes it from sibling tools like face_detection or car_detection, which address different detection tasks.

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 hints at usage by mentioning the detail parameter and cost per call, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions. It lacks explicit 'when to use' or 'when not to use' guidance that would help an agent choose this tool over siblings.

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, so the safe read-only nature is covered. The description adds valuable context beyond that: it reveals the return of bounding boxes and the optional include_img parameter that returns an annotated image. It also mentions the cost per call, which is not in the schema or 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 concise and front-loaded with the primary purpose in the first sentence. The second Korean sentence is a direct translation adding no new information, which slightly reduces efficiency. The cost note is useful and compact. Overall, it is appropriately sized with no 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 detection tool with three parameters and no output schema, the description covers the key aspects: what it does, what it returns (bounding boxes), and the optional annotated image behavior. It also mentions the point cost, which is relevant context. It does not detail the exact bounding box format, but that may be inferred or expected in return documentation.

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 already provides 100% coverage for all three parameters with descriptions. The tool description adds no additional parameter semantics beyond the schema; it merely mentions include_img again. Since schema coverage is high, 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 clearly states the tool's function: 'Detect people in an image and return their bounding boxes.' This is a specific verb+resource combination that distinguishes it from sibling tools like car_detection and face_detection. The Korean version reinforces the same purpose without ambiguity.

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 implicitly communicates when to use this tool: whenever people detection is needed, as opposed to car or face detection. However, it does not explicitly name alternatives or state when not to use it. The context is clear enough for an agent, but it lacks explicit exclusion 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, which the description does not contradict. The description adds a cost note (30 points per call), which is a useful behavioral detail. However, no other behavior such as output format details (beyond MP3) or failure modes is disclosed.

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, front-loaded with the key action, and includes a bilingual translation and cost note. Each part serves a purpose, and there is no unnecessary fluff.

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

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

For a single-parameter tool with no output schema, the description covers the core function and output (MP3 file) plus cost. It lacks details on response format or error handling, but remains adequate for a simple conversion 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 input schema provides 100% coverage for the video_url parameter, including allowed formats and size limit. The description adds no additional parameter-specific information, leaving the schema to carry the full burden.

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 ('Extract') and resource ('audio track of a video file') and specifies the output format (MP3). It clearly differentiates from sibling tools like extract_video_thumbnail, which handles thumbnails rather than audio.

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 when an MP3 audio file is needed from a video, but does not explicitly state when to use it vs alternatives or mention any exclusions. Sibling tools are distinct, so selection is straightforward.

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 and openWorldHint. The description adds valuable details: the output format (JPEG), the frequency-based sizing behavior, and a per-call point cost. It does not contradict the annotations, though it omits return transport details like URL vs base64.

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 English sentence is concise and front-loaded. The Korean repetition adds minor redundancy but includes the cost note. Overall the description is well-sized, though it could be tightened by removing the duplicate.

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 one-parameter read-only tool, the description is mostly complete. However, it lacks an output schema and does not specify the return format (URL, binary, etc.) or any input limitations/error behaviors. It is minimally viable but not fully transparent.

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 documents the single 'text' parameter with 100% coverage, so the description does not need to add parameter details. It only explains tool behavior, not parameter constraints, aligning with the baseline 3 for high 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 states the tool generates a JPEG word cloud image from input text, with word sizing based on frequency. It uses a specific verb-resource pairing and distinguishes itself from sibling media analysis tools (e.g., face_detection, video_to_mp3).

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 what the tool does, but does not explicitly state when to use it or mention alternatives. Usage is implied by the tool name and purpose; siblings are clearly different, but no direct 'use this when...' guidance is provided.

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