Korean News Hub

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

With no annotations provided, the description carries the burden of behavioral disclosure. It clearly states that the tool combines multiple news sources into a single briefing, but it does not mention any output format, freshness constraints, or potential rate/cost implications of generating a 'comprehensive' briefing.

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 sentence fragments that efficiently convey the tool's purpose and content scope. Every word earns its place, with no redundancy or filler.

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 zero-parameter tool with an output schema and clearly listed content categories, the description is largely complete. It could add a touch more about how the briefing is delivered or when 'daily' is anchored, but the core selection and invocation information is 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?

The tool has zero parameters and the schema coverage is 100%, so the schema leaves nothing undocumented. The description adds no parameter details, but none are needed; the baseline of 4 applies.

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 ('Generate'), a resource ('comprehensive daily news briefing'), and precisely enumerates the content categories it draws from. This makes it easy to distinguish from siblings like korean_news or tech_news, which cover only a subset.

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 is for getting a combined briefing across Korean headlines, AI/tech, and Claude/Anthropic news, which suggests when it is appropriate. However, it does not explicitly say when to prefer this over the individual sibling tools, leaving some inference to the agent.

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 behavioral disclosure burden. It clearly signals read-only behavior via 'Get,' and the output schema covers return structure, but it does not mention potential side effects, auth requirements, or whether it returns headlines versus full articles. For a simple fetch tool this is acceptable but not rich.

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 tightly written: a one-line summary followed by compact parameter details. Every sentence adds information, and there is no redundant filler. The front-loaded summary makes the tool's purpose immediately clear.

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 two-parameter fetch tool with an output schema, this is nearly complete: category options and count bounds are covered. The main omission is explicit guidance on how this tool relates to siblings such as news_search and tech_news, which would help an agent choose 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 coverage is 0%, and the description fully compensates by documenting both parameters. It lists all valid category options and specifies count's default and maximum, which the bare schema does not provide. This is essential, actionable 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 uses a specific verb and resource: 'Get Korean news by category.' It immediately differentiates from siblings like tech_news (Korean vs. tech-focused) and news_search (category listing vs. search). It is not a tautology and gives a clear, concrete 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?

The category list and count parameters imply the tool is for fetching Korean news in a given category, but there is no explicit guidance on when to prefer this over siblings like news_search, tech_news, or read_article. Usage context is only implied, not 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?

With no annotations, the description carries the behavioral burden. It does disclose useful constraints like count max 20 and the allowed language values, and the operation is clearly a read-only news search, but it does not describe result behavior such as relevance ordering, freshness, or article metadata beyond what the output schema provides.

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. The one-line purpose is immediately followed by a tight, well-organized Args block with no filler or redundant explanation.

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 three-parameter search tool with an output schema, the description covers everything needed to invoke it correctly, including defaults, maximums, and allowed values. The only missing context is clearer guidance on selecting this tool over the sibling news tools, which is a minor completeness 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?

The schema only supplies names, types, and defaults, so the description's Args block adds essential meaning: query examples, explicit language values ('ko' or 'en'), and count default/max. This fully compensates for the 0% schema description 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 states a specific action and resource: 'Search news by keyword.' It is immediately understandable and distinct from read_article or daily_briefing, but it does not explicitly distinguish itself from sibling news-list tools like korean_news, tech_news, or trending, so it stops short of full sibling 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 phrase 'by keyword' implies this is the tool to use for query-driven news lookups rather than curated lists, but there is no explicit when-to-use/when-not-to-use guidance and no mention of alternative sibling tools. The usage context is present only by implication.

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 behavior disclosure. It explicitly states the read/extract action and implies a non-mutating operation, but it does not disclose request-side considerations such as URL format requirements, blocking, paywalls, rate limits, or failure behavior. This is adequate but not rich.

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: the purpose appears in the first sentence, and the single parameter is documented in a short Args block. There is no filler or redundant detail.

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 one-parameter read tool with an output schema present, the description covers the core mechanics adequately. However, it lacks any guidance about valid URL formats, what type of article content is expected, and how this tool relates to the sibling tools, leaving some selection context incomplete.

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. It does restate the parameter and adds that it is 'The article URL to read', which is minimally helpful, but it does not specify expected URL format (e.g., including http/https) or constraints beyond being a string. The single parameter is simple enough that this is acceptable.

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-object pair, 'Read and extract article content from a URL', which clearly identifies the tool as a direct URL content fetcher. It is clear, but it does not explicitly differentiate itself from sibling tools like news_search or daily_briefing, so it misses the top 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 gives no guidance on when to use this tool versus the sibling news/search tools, nor does it mention any prerequisites or exclusions. An agent must infer the appropriate context from the tool name and the word 'URL', which is weak selection support.

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, the description carries the full burden of behavioral disclosure, but it only restates what the tool does and lists arguments. It does not describe rate limits, ordering, freshness, language of results, or what happens when no articles match the topic. The presence of an output schema helps, but the description itself adds little beyond the schema.

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-loads the core purpose, followed by a clearly formatted Args block. Every sentence earns its place, and there is no filler or repetition.

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 tool with an output schema, the description is largely complete: it defines the topic vocabulary, the count default/maximum, and the global scope. It only lacks explicit routing guidance among siblings and any caveats about result format, which are partially covered by the output 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?

Schema description coverage is 0%, but the description compensates well by enumerating valid topic values (AI, Claude, OpenAI, MCP, OpenClaw, 스타트업, 개발, 클라우드) and stating count constraints (default 10, max 20). This adds meaning beyond the bare schema, though it does not explain subtle differences between overlapping topics.

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 clear verb and resource: 'Get global AI/tech news by topic.' It indicates the tool fetches news and supports topic-based filtering, which distinguishes it from more generic siblings like news_search or read_article, though it never explicitly names those alternatives.

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 no explicit guidance on when to use this tool versus its siblings. It does not mention korean_news for Korean-only news, news_search for search queries, or trending for popularity, so an agent must infer usage solely from the tool name and the word 'global.'

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 mentions 'current trending/headline news' but does not describe recency, source scope, potential duplicates with sibling tools, pagination, or return shape. For an unannotated read tool, this is a minimal disclosure.

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 core purpose, and every line adds meaningful information. The scope definitions are compact and directly useful, with no filler.

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 one parameter and has an output schema, so basic return information is covered elsewhere. However, the existence of strongly overlapping siblings like korean_news and tech_news creates a context gap: the description does not clarify which tool is preferred in which scenario.

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%, and the schema only declares a default value for 'scope' with no description or enum. The description fully compensates by defining the allowed values 'korea' and 'tech' and their meanings, making the parameter actionable for an agent.

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 verb ('Get') and resource ('current trending/headline news'), and it defines two scopes: 'korea' for Korean headlines and 'tech' for global tech trends. It does not explicitly differentiate from sibling tools like korean_news and tech_news, but the purpose itself is understandable.

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 the scope options and what each returns, but it does not explain when to use this tool instead of siblings such as tech_news, korean_news, or news_search. There is no explicit when-not or alternative routing, leaving the agent to guess based on tool names.

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