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 the return type (List[Dict[str, Any]]) and raises a ValueError for invalid types, adding some context. However, it doesn't cover important aspects like rate limits, authentication needs, or whether it's read-only/destructive, leaving gaps for a tool with parameters.

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

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

The description is well-structured with sections for Args, Returns, and Raises, making it easy to scan. It's appropriately sized with no redundant sentences. However, the initial purpose statement could be more front-loaded for immediate clarity, though it's still 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?

Given the tool has 2 parameters, no annotations, and no output schema, the description is moderately complete. It covers parameters and return values adequately but lacks behavioral details like error handling beyond ValueError, performance constraints, or sibling tool differentiation. It's sufficient for basic use but has clear gaps for full contextual understanding.

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

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

The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'type' is the article type with valid values (top, new, ask, show) and 'limit' is the number of articles to return with a maximum of 30. This fully compensates for the schema's lack of descriptions, providing clear semantics 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 the tool's purpose: 'GeekNews에서 아티클을 가져오는 도구' (Get articles from GeekNews). It specifies the verb '가져오는' (get/fetch) and resource '아티클' (articles), making the action explicit. However, it doesn't distinguish from the sibling tool 'get_weekly_news', which likely serves a different purpose but isn't contrasted here.

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 mentions parameters and returns but doesn't explain context, prerequisites, or comparisons to 'get_weekly_news'. Usage is implied through parameter details but lacks explicit when/when-not instructions.

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 states the tool fetches weekly news but doesn't describe what '가져오는' entails (e.g., read-only operation, data format, potential errors, or rate limits). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and safety 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 appropriately sized and front-loaded, with the core purpose stated first in a single sentence. The Args and Returns sections are structured clearly, though the inclusion of type hints like 'Dict[str, Any]' adds minor verbosity without schema support. Overall, it's efficient with minimal waste.

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 (1 parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the parameter semantics well but lacks behavioral details and usage guidelines relative to the sibling tool. Without annotations or output schema, more context on what the tool returns or how it behaves 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 description adds meaningful semantics beyond the input schema, which has 0% coverage. It explains that 'weekly_id' is a weekly news ID and specifies that an empty string fetches the most recent weekly news, clarifying the parameter's purpose and default behavior. This compensates well for the lack of schema 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 clearly states the tool's purpose: 'GeekNews에서 주간 뉴스를 가져오는 도구' (Get weekly news from GeekNews). It specifies the verb '가져오는' (get/fetch) and the resource '주간 뉴스' (weekly news). However, it doesn't explicitly differentiate from the sibling tool 'get_articles', which might also retrieve news content, leaving some ambiguity about when to use each.

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 'get_articles'. It mentions that an empty weekly_id fetches the most recent weekly news, which is a usage hint, but lacks explicit context, prerequisites, or exclusions for tool selection.

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