newsoracle

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

No annotations are provided, so the description carries the full burden. It reveals the combination behavior ('combines trending searches + top headlines') but doesn't disclose other behavioral traits such as real-time updates, country filtering effects, or output format limitations. This leaves significant gaps for a tool with no annotation support.

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 sentence that efficiently communicates purpose and method. The em-dash separates the core value from the combination mechanism, making it front-loaded and concise with no wasted words.

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 tool with one optional parameter and no output schema, the description gives the core function but omits details like number of results, sort order, or any disclaimers. It is minimally adequate but would benefit from a bit more context about what the returned stories look like or how they are updated.

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% (the only parameter 'country' is described), so the baseline is 3. The description adds no additional detail about the parameter; the schema already explains 'Country code (default: us)'. No extra meaning is provided beyond what the schema contains.

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 'Latest breaking/developing stories — combines trending searches + top headlines,' which clearly identifies the tool's function and distinguishes it from siblings like top_news and trending_topics by indicating it combines them. It lacks an explicit verb but the noun phrase conveys the purpose effectively.

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 explicit when-to-use guidance or alternatives are mentioned. The description implies usage for breaking/developing stories but doesn't state when not to use related tools like top_news or trending_topics. The implied use case is sufficient to merit a mid-range score.

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, and the description does not disclose any behavioral traits such as the output format, limitations, or what happens with the query. The tool's behavior is completely opaque beyond the vague hint of comparing 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 brief (five words) but takes the form of a question rather than a clear instruction. It is underspecified and fails to communicate the tool's function effectively, making it an ineffective use of concise wording.

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 a simple one-parameter schema and no output schema, the description still fails to explain what the tool returns or how it operates. It is more complete than a tautology but far from adequate for an agent to use it reliably.

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 covers the only parameter 'query' with a description, so the parameter semantics are already documented. The description does not add any additional meaning beyond the schema, 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 hints at the tool's purpose by saying 'How different news sources cover the same story,' but it lacks an explicit action verb like 'compare' or 'show.' It is a vague phrase rather than a clear verb+resource statement, and it does not differentiate from sibling tools such as related_queries or topic_deep_dive.

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?

There is no guidance on when to use this tool versus alternatives. The description does not mention any use cases, prerequisites, or exclusions, leaving the agent to guess when this comparison tool is appropriate.

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. It only mentions 'Server status, API connectivity,' which hints at a read-only health check but provides no details about what exactly is checked, whether it makes network requests, or what the output represents. This is insufficient for an agent to understand the tool's 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 is short, but it is not a complete sentence and reads more like a label than a functional description. While brevity is good, the lack of a verb makes it less structured and less useful. It does not waste words, but it also does not use them effectively.

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 no parameters and no output schema, the description is the only source of guidance. 'Server status, API connectivity' is too sparse to fully inform an agent about the tool's purpose, expected results, or when to invoke it. Even though the tool is simple, the description leaves significant ambiguity about what the agent will receive and how to interpret 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?

The tool has zero parameters, and the schema confirms an empty object. The baseline for 0 params is 4, and there is no need for the description to elaborate on parameter meanings. The description does not add anything param-related, but none is necessary.

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 'Server status, API connectivity.' is a noun phrase rather than a clear verb-based statement. It indicates the tool relates to server and API health but does not explicitly say what action is performed (e.g., 'checks' or 'returns'). It is somewhat distinguishable from the news-focused sibling tools, but the purpose remains vague.

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?

There is no guidance on when to use this tool versus alternatives. The description does not mention any context, prerequisites, or exclusions. An agent would have no idea whether to call health_check instead of, say, top_news or trending_topics when assessing system status.

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 tells us the output includes rising and top queries, which is useful, but it does not specify return format, whether a keyword is required (though schema says 0 required), or any limitations. For a simple read-only query tool, this is adequate but not fully transparent.

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 extremely concise: two short clauses, no filler, and it front-loads the core purpose. Every word earns its place, and it immediately tells the agent what the tool produces.

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 simplicity (2 parameters, no output schema, no nested objects), the description is largely complete. It states the purpose and output types. Minor gaps like output format or dependencies on keyword are not critical because they are implied by the nature of the tool and the 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?

The input schema already has 100% parameter descriptions, so the baseline is 3. The description adds no extra meaning beyond what the schema says: it mentions 'around a keyword' which repeats the keyword parameter description, and it doesn't clarify the country parameter further.

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 what the tool does: it returns related queries (what people also search for) around a keyword, and specifically mentions both rising and top queries. This distinguishes it from sibling tools like trending_topics or search_news, which focus on different data.

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 context by indicating the tool is for keyword-based related query discovery. It does not explicitly name alternatives or provide exclusions, but the use case is directly implied: use this when you need 'people also search for' data for a specific keyword.

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 full responsibility for disclosing behavior. It only states the core function, omitting any details about return format, ordering, rate limits, or side effects. For a search tool, this minimal transparency is insufficient.

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, front-loaded sentence that is appropriately concise. It conveys the essential purpose without unnecessary verbosity, making it easy for an agent 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 presence of many sibling tools and no output schema or annotations, the description is incomplete. It lacks usage guidance, return value expectations, or any filtering/sorting semantics, leaving the agent under-informed 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?

Schema coverage is 100%, so the baseline is 3. The description adds minimal semantic value by mapping 'keyword' and 'time filter' to the query and when parameters, but it does not enrich beyond the schema's own 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 the tool searches news articles by keyword with a time filter, using a specific verb and resource. It distinguishes from siblings like 'top_news' or 'breaking_now' by emphasizing generic search capability, though it doesn't explicitly name 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?

There is no guidance on when to use this tool versus alternatives such as 'top_news' or 'trend_over_time'. The description implies ad-hoc search but provides no contextual signals or exclusions.

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. It only lists output aspects (articles, source diversity, interest trend) but does not disclose how the tool behaves, what data sources it uses, whether the analysis is real-time, or the shape of the returned result. This is insufficient for an analysis tool with no annotation support.

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 very brief, but the brevity comes at the cost of clarity. It is structured as a label followed by a colon and list, which is efficient but lacks a clear verb or sentence structure. Every word is necessary, but the content is under-specified.

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?

There is no output schema and no annotations, so the description must explain the full behavior and expected return. It mentions three aspects but does not describe how they are combined, what response format to expect, or any prerequisites. For a complex analysis tool, this is 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?

The schema provides complete descriptions for both parameters (query and country), achieving 100% coverage. The description does not add any meaning beyond the schema's parameter descriptions, so 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 says 'Deep analysis' which is a vague verb, but it does list the covered aspects: articles, source diversity, interest trend over time. This gives some sense of the resource and scope, though it doesn't clearly differentiate from siblings like trend_over_time or search_news.

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?

There is no guidance on when to use this tool versus alternatives. It does not mention similar tools like trend_over_time or breaking_now, nor does it state exclusions or preferred use cases. The description leaves the agent without a basis for selecting it 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?

No annotations are provided, so the description must fully disclose behavior. It only states the purpose and lists topics (also in schema), without explaining what the response contains, pagination, sorting, or any other behavioral traits. This is a significant gap.

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 extremely concise, using just two short sentences. It includes essential information without unnecessary words, earning a perfect score for conciseness.

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, but without an output schema, the description should explain the response format. It only says 'Top headlines', which is vague and does not mention what fields or structure the output has. Adequate but with clear 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 describes both parameters fully (topic and country with default), and the description reiterates the topic list without adding extra meaning. With 100% schema coverage, a 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 that the tool provides top headlines filtered by country and topic, which is a specific resource and dimensions. However, it does not differentiate from siblings like search_news or trending_topics, so it does not fully meet the 5-level criterion.

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 guidance is given about when to use this tool versus alternatives. There is no mention of appropriate scenarios, limitations, or exclusions, leaving the agent without context for 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. It implies a read-only query but does not disclose time-window semantics, regional variations, rate limits, or response structure. The single sentence adds little beyond the basic purpose.

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 one short sentence, front-loaded with the core purpose, and contains no filler or redundant information. It is maximally concise for the information it conveys.

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 optional parameter and no output schema, the description is serviceable but incomplete: it does not describe what the response contains (e.g., a list of topics, rankings, metadata), and with no annotations there is no safety or behavioral context. It is adequate but leaves 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?

The schema covers the single 'country' parameter fully with examples and a default, so the parameter meaning is already clear. The description adds only the phrase 'in a country,' which does not provide meaningful additional semantics 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 clearly states the resource: trending topics on Google for a country. It lacks an imperative verb like 'Get' or 'List' but the intent is obvious. It does not explicitly distinguish from siblings like breaking_now or top_news, but the Google Trends scope differentiates it enough.

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 guidance is given on when to use this tool versus siblings such as breaking_now, top_news, or trend_over_time. The description only defines what it does, with no mention of use cases, prerequisites, or when not to use it.

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 must disclose behavioral traits. It identifies the data source (Google Trends) and the keyword limit, but does not explain the output format, time series characteristics, normalization, or any other behaviors. This is a significant gap for a tool that returns data.

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 short sentences, front-loads the core action, and wastes no words. It is concise and well-structured, making it 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?

The tool has low-to-medium complexity with three optional parameters and no output schema. The description is sufficient to invoke the tool, but it does not clarify the return structure or any non-obvious behavior. Given the absence of annotations and output schema, a bit more detail on expected output or limitations 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?

Schema description coverage is 100%, so the baseline is 3. The description adds '1-5 keywords' which reinforces the schema but does not provide additional meaning beyond what the schema already states for each parameter. No extra parametric nuance is given.

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 action (Google Trends interest over time) and the resource (1-5 keywords), and adds 'Compare search interest' which conveys the comparison purpose. However, it does not explicitly differentiate from sibling tools like trending_topics or compare_coverage, so it falls short of a 5.

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?

Usage context is implied: 'for 1-5 keywords' suggests when to use it, but there is no explicit guidance on when to choose this over sibling tools, nor any mention of exclusions or alternatives. This meets the 'implied usage' level but not clear context without exclusions.

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