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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool fetches data, implying a read-only operation, but does not disclose any behavioral traits such as rate limits, authentication needs, error handling, or response format. This is a significant gap for a tool with no annotation coverage.

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, efficient sentence that directly states the tool's purpose without any unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to quickly understand the core functionality.

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 low complexity (2 optional parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral aspects and usage context. Without an output schema, it does not explain return values, which is a minor gap but acceptable given the simplicity.

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, fully documenting both parameters ('date' and 'hd') with their types, defaults, and formats. The description does not add any additional meaning beyond what the schema provides, such as explaining parameter interactions or constraints. Given the high schema coverage, a 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 purpose: 'NASA의 오늘의 천체 사진(Astronomy Picture of the Day)을 가져옵니다' translates to 'Fetches NASA's Astronomy Picture of the Day for today.' It specifies the verb ('가져옵니다' - fetches) and resource (NASA's APOD), but does not explicitly distinguish it from sibling tools like 'get_earth_imagery' or 'search_nasa_images,' which reduces it from a perfect score.

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 context, prerequisites, or exclusions, such as comparing it to sibling tools like 'get_mars_rover_photos' or 'search_nasa_images.' This lack of usage instructions leaves the agent without clear 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions fetching images from an API but does not cover critical aspects such as authentication requirements, rate limits, error handling, or the format of returned data (e.g., image type, size). This leaves significant gaps in understanding how the tool behaves in practice.

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, efficient sentence in Korean that directly states the tool's purpose without any unnecessary words. It is front-loaded and appropriately sized for the tool's complexity, making it easy to parse quickly.

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 (4 parameters, no output schema, and no annotations), the description is insufficient. It lacks details on behavioral traits, output format, and usage guidelines, which are crucial for an AI agent to effectively invoke this tool without annotations or output schema to fill the 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?

Schema description coverage is 100%, so the schema fully documents all parameters (date, dim, lat, lon) with descriptions and constraints. The description adds no additional parameter semantics beyond what the schema provides, which is acceptable given the high coverage, resulting in a baseline score of 3.

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: 'fetch satellite images through NASA's Earth imagery API.' It specifies the verb ('fetch') and resource ('satellite images'), and distinguishes it from siblings by mentioning 'Earth imagery' versus astronomical or Mars-related tools. However, it doesn't explicitly differentiate from 'search_nasa_images,' which might overlap in functionality.

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 like 'search_nasa_images' or other sibling tools. It lacks context on use cases, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name and description alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('가져옵니다' - fetches) but doesn't describe what the tool returns (e.g., list of photos, metadata, pagination), error conditions, rate limits, or authentication needs. For a tool with 4 parameters and no output schema, this leaves critical behavioral aspects undocumented.

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 in Korean that directly states the tool's function. It's front-loaded with the core action and resource, with no wasted words. However, it could be more structured by including key usage hints, but as-is, it's efficiently written.

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 (4 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return format (e.g., what 'photos' entail), pagination behavior implied by the 'page' parameter, or how parameters interact (e.g., sol vs. page for date filtering). Without annotations or output schema, the description should provide more context to compensate, which it fails to do.

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 clear descriptions for all parameters (camera, page, rover, sol), including enums for rover and camera. The description adds no additional parameter semantics beyond what the schema provides, such as explaining relationships between parameters (e.g., sol vs. page). Since the schema is comprehensive, 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 '화성 로버의 사진을 가져옵니다' (Fetches photos of Mars rovers) states the basic action and resource but is vague about scope. It doesn't specify whether it retrieves all rover photos, recent ones, or filtered results, nor does it distinguish this tool from sibling tools like 'search_nasa_images' which might also return Mars rover photos. The purpose is understandable but lacks specificity.

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 doesn't mention sibling tools like 'search_nasa_images' or explain if this is for specific rover missions, filtered queries, or general browsing. Without context, users must infer usage from parameters alone, which is insufficient for effective 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does ('get information') without adding context like whether it's a read-only operation, if there are rate limits, authentication needs, or what the return format might be. For a tool with zero annotation coverage, this is a significant gap in 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, efficient sentence in Korean that directly states the tool's purpose without any unnecessary words or fluff. It's front-loaded and appropriately sized for its content, making it highly concise and well-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?

Given the tool's complexity (fetching NEO data with date parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what information is returned, how results are formatted, or any behavioral traits like error handling. For a data retrieval tool with these gaps, the description should provide more context to be fully helpful.

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, with clear descriptions for 'start_date' and 'end_date' parameters. The description doesn't add any meaning beyond the schema, such as explaining date range constraints or default behaviors. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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: '근지구 천체(Near Earth Objects) 정보를 가져옵니다' translates to 'Get Near Earth Objects information.' This specifies the verb ('get') and resource ('Near Earth Objects information'), making it clear what the tool does. However, it doesn't differentiate from sibling tools like 'get_apod' or 'get_mars_rover_photos,' which also fetch data but for different resources, 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 alternatives. It doesn't mention any context, exclusions, or prerequisites, such as when to choose this over other sibling tools like 'search_nasa_images' for broader searches. Without such information, users must infer usage from the tool name and schema alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action ('검색합니다') without details on permissions, rate limits, pagination (implied by 'page' parameter but not explained), or response format. For a search tool with no annotation coverage, this leaves critical behavioral traits unspecified.

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, efficient sentence in Korean that directly states the tool's function without unnecessary words. It's front-loaded with the core action and resource, making it highly concise and well-structured for quick understanding.

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 (search with parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the search returns, how results are formatted, or any limitations. For a tool with 3 parameters and no structured output information, more context is needed to guide effective use.

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 fully documents parameters like 'q' for search query and 'media_type' with enum values. The description adds no additional meaning beyond what's in the schema, such as search syntax or result examples. Baseline 3 is appropriate when the schema handles parameter documentation effectively.

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: searching ('검색합니다') in NASA's image and video library. It specifies the resource (NASA 이미지 및 비디오 라이브러리) and verb, but doesn't distinguish it from sibling tools like get_apod or get_mars_rover_photos, which also retrieve NASA media. This makes it clear but not fully differentiated.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where search_nasa_images is preferred over get_apod (for astronomy photos) or get_mars_rover_photos (for Mars-specific images). Without such context, users must infer usage from the tool name alone.

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