APICK Vision

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

Annotations already declare readOnlyHint=true. The description adds behavioral context beyond annotations by stating that thumbnails are extracted at regular intervals, the result is a ZIP file, and each call costs 10 points. This 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 core behavior is front-loaded and the cost note is compact. The bilingual repetition of the same sentence is slightly redundant for an AI agent, but it remains acceptably concise for a mixed-language tool listing.

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

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

For a tool with one required parameter, fully documented optional parameters, a clearly stated ZIP output, and read-only annotations, the description is largely complete. It lacks only explicit usage-alternative guidance, but that does not prevent an agent from invoking 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 description coverage is 100%, so count, width, and video_url are already fully documented with ranges and defaults. The description adds no parameter-level detail beyond what the schema provides, so 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 operation (extract thumbnail images), the resource (a video), and the return type (a ZIP file). It is unambiguous, but it does not explicitly distinguish this tool from sibling media tools such as video_to_mp3 or download_youtube_video, so it stops short of full differentiation.

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 intended use is implied: use this tool when you need preview thumbnails extracted at regular intervals from a video. However, there are no explicit when-to-use or when-not-to-use instructions, and no alternative tools are named, leaving the agent to infer the appropriate selection context.

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=true, and the description adds useful behavioral context: it returns coordinates, optionally includes facial feature information when use_feature=1, and notes a 3-point charge per call. No contradiction with annotations exists, though error behavior and edge cases are not 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 short and front-loaded with the core action, but the Korean sentence largely duplicates the English one. The cost note is useful and placed at the end, yet the redundancy prevents a higher 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?

With no output schema, the description should explain the return value shape, and it only says 'coordinates' without describing the format or coordinate system. It also does not mention behavior when no face is found, which leaves meaningful gaps for an agent that must consume the result.

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

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

Schema coverage is 100%, so the schema fully documents all three parameters. The description only repeats the use_feature=1 behavior and adds no meaning beyond the schema for threshold or image_url, 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 ('Detect'), names the resource (faces in an image), and states the output ('return their coordinates'). This clearly identifies what the tool does and distinguishes it from sibling tools like face_blur, which edit images rather than return detection coordinates.

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 when-to-use or when-not-to-use guidance is provided, and no alternative tools are mentioned. The agent must infer applicability from the schema and sibling list instead of being told how to choose between face_detection and related tools such as face_blur or nsfw_detection.

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 mark the tool as read-only, so the safety profile is covered. The description adds useful context about per-call cost and typical application areas, but it does not disclose the similarity score scale, failure behavior, or limitations beyond the schema-provided constraints.

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

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

The description is compact and front-loaded with the core action. The English and Korean portions repeat the same information, which is mildly redundant, but the use-case and cost notes earn their place and the overall length is appropriate.

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 two-parameter read-only tool with a richly documented schema, the description provides enough to invoke it correctly: what it does, what it returns, when to use it, and its cost. The lack of an output schema leaves the exact score range ambiguous, but this does not block 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 100%, with both parameters fully documented for URL format, MIME types, and size limits. The description only restates the conceptual roles of the two images ('base image' and 'comparison image') without adding meaning beyond the schema.

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

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

The description uses a specific verb and resource: 'Compare a base image with another image and return a similarity score.' It also names concrete use cases—original verification and duplicate detection—which clearly distinguishes it from image editing, generation, and detection siblings.

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

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

The description gives clear usage context by stating the tool is for '원본 검증, 중복 이미지 탐지 등' (original verification, duplicate image detection, etc.). It does not explicitly name alternatives or when-not-to-use conditions, but the use cases provide enough guidance 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 mark readOnlyHint=true, and the description adds that it returns an nsfw_score and can include detailed results when detail=1. It also discloses the per-call cost, but it does not explain the score scale, threshold, or what the detailed result contains.

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 purpose is front-loaded, but the first two sentences repeat the same statement in English and Korean, wasting tokens. The detail=1 and cost notes are useful but do not fully compensate for the redundancy.

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

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

With no output schema, the description gives minimal return information (nsfw_score, optional detailed result) but omits the interpretation or range of the score and the shape of the detailed output. Still, for a simple single-URL detector, the core invocation requirements are present.

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

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

Schema description coverage is 100%, so the schema already documents image_url and detail sufficiently. The description's mention of detail=1 effectively restates the schema's '세부 판정 결과 포함 여부' rather than adding new semantic meaning.

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

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

The description states a specific operation ('Detect whether an image contains NSFW content') and a specific output ('return an nsfw_score'), with an explicit definition of NSFW as violent or sexually explicit. This clearly distinguishes it from image-related siblings like face_detection or image_similarity.

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 intended use is implied by the operation itself, but there is no explicit when-to-use or when-not-to-use guidance, and no alternatives are named. An agent must infer when this tool is appropriate among many image-processing 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 provide readOnlyHint=true, and the description adds useful context beyond that: it is a non-destructive conversion, it returns an MP3 file, and it costs 30 points per call. No contradiction with the annotations was found.

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 the core action is front-loaded. The Korean sentence repeats the English content, which is slightly redundant for an AI agent, but the overall structure remains compact and the cost note is clearly separated.

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

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

With one well-documented parameter and no output schema, the description sufficiently explains the input, output, and cost. An agent can determine what to pass and what to expect, so nothing critical is missing 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 100%, so the video_url parameter is already fully documented with allowed MIME types and the 200MB limit. The main description adds no additional parameter-level meaning, 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 ('Extract') and clearly identifies the resource ('audio track of a video file') and output format ('MP3'). This naturally distinguishes it from sibling tools like download_youtube_video (returns a video file) and extract_video_thumbnail (returns an image).

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

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

The intended use is clear: when the user needs the audio of a video as an MP3, this is the tool. However, it does not explicitly say when not to use it or compare it with alternatives such as download_youtube_video or stt.

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 description does not need to restate safety. The description adds useful behavioral details beyond annotations: output is a JPEG, word sizes reflect frequency/importance, and each call costs 10 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, front-loaded with the key English statement, and the cost note is a concise addition. The Korean sentence largely restates the English content, creating minor redundancy, but it is not bloated and remains easy to parse.

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, read-only tool with full schema coverage, the description supplies the essential purpose, output format, and pricing. It does not specify how the JPEG is returned (URL vs. binary), but no output schema exists and the tool is simple enough that an agent can still invoke it correctly.

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

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

The only parameter, 'text', is fully described in the input schema ('워드클라우드를 생성할 텍스트'), so schema coverage is 100%. The description adds no additional parameter-level detail such as length limits or language constraints, 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 clearly states the specific action ('Generate a word cloud image (JPEG)') and the method ('sizing each word by frequency'). It is unambiguous about the resource and distinguishable from all sibling tools, none of which advertise word-cloud generation.

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 intended use is implied: call this when the user needs a word cloud from input text. However, the description does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or conditions. The cost note is useful but does not provide selection guidance.

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