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

No annotations exist, so the description carries the full burden and does disclose meaningful behavior: the request budget (~1+10 per seed), that autocomplete is capped, and that partial per-source failures surface in `errors`. This is genuinely useful operational context, though auth/read-only nature and data source freshness are left implicit.

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?

Dense but front-loaded, leading with what the tool is before the mechanics. Every clause adds information; phrasing is jargon-heavy in places ('AEO', 'title angle') but not padding.

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?

An output schema exists, so return values need not be explained, and the `errors` note is a nice touch. However, for a 4-parameter tool with 0% schema coverage, leaving geo and timeframe unexplained makes the definition incomplete 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 0%, so the description must define all four parameters. It only covers two: seeds (1-5) and limit ('clusters (1-25)'). geo and timeframe (including the non-obvious default 'today 3-m') are left entirely undefined.

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?

States a concrete function (an AEO opportunity finder) and details the output: question-shaped searches clustered by seed/title angle and scored with several signals. The purpose is clear, but no sibling is named, so an agent cannot tell from the text alone how this differs from mine_questions, related_queries, or suggest_keywords.

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?

Gives an input constraint ('For 1-5 seeds') but no when-to-use, when-not-to-use, or explicit alternative. With 13 sibling tools covering overlapping keyword/question space, the absence of routing guidance is a real gap.

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 burden. It discloses the core behavior (comparison, relative share, leader, trend direction) but doesn't mention limitations like data availability, normalization, or how 'leader' is determined. It's 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?

Two sentences, front-loaded with the core action and output, followed by a clear use case. 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?

The tool has an output schema, so return values are covered. However, with no annotations and 0% schema coverage, the description should explain more about the optional parameters and any constraints (e.g., keyword count limits, required format). It's adequate for a simple comparison tool but leaves some 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 0%, so the description must compensate. It explains the 'keywords' parameter implicitly (2-5 keywords) but doesn't explain 'geo', 'gprop', 'category', or 'timeframe'. The description adds some meaning for the main parameter but leaves the optional parameters undocumented.

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 ('Compare') and resource ('keywords head-to-head'), and clearly states what it produces: relative share, leader, and trend direction for each keyword. It also distinguishes itself from siblings by focusing on comparison rather than single-keyword trends or related queries.

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 explicitly states when to use this tool: 'Use to pick the strongest angle among alternatives.' This gives clear context for selection. It doesn't explicitly name alternatives or exclusions, but the sibling list and the comparison-focused wording make the intended use clear.

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 behavioral burden and does so thoroughly: it discloses de-duplication, ranking logic, angle tagging, question aggregation, and per-seed partial failure reporting. This goes well beyond a generic summary and tells the agent what to expect.

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?

Three dense sentences with no filler. The use case is front-loaded, followed by output behavior and failure semantics, and every clause contributes actionable 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?

Output behavior is well covered and an output schema exists, so return values are not a gap. However, the optional filtering parameters (geo, gprop, category, timeframe) are not explained, and there is no guidance on when to prefer a sibling tool. Adequate for default calls but not fully complete.

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 for parameter meaning. It adds useful semantics only for seed_keywords ('1-5') and indirectly for max_per_seed ('per seed'), while geo, gprop, category, and timeframe are left unexplained; gprop in particular is opaque.

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?

States a specific composite behavior: one-shot topic discovery that pulls trend direction and related queries, de-duplicates, and ranks candidates as breakout > rising > evergreen with an `angle` tag. This distinguishes it from siblings like related_queries and related_topics by describing an aggregated, ranked, blog-oriented result.

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?

Explicitly frames the tool as 'one-shot topic discovery for blog ideation' and constrains input to 1-5 seed keywords, giving clear context for when to use it. It does not explicitly name sibling alternatives or state when not to use it, so it stops short of a 5.

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 behavioral burden and does disclose the important constraint matrix between resolution and geo, including the non-US REGION-only limitation. It says nothing about read-only nature, rate limits, pagination, or error behavior, so it is only partially 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?

Two compact sentences with the core purpose front-loaded and no filler. The shorthand notation ('geo='' only') is dense but readable, and nothing is wasted.

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 7-parameter tool with 0% schema description coverage and no annotations, the description covers only the resolution/geo interaction. The return shape is arguably handled by the output schema, but the remaining call-critical parameters are left entirely unexplained.

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% across 7 parameters, so the description must compensate. It explains the semantics and valid values of resolution and its coupling to geo, but says nothing about keywords, timeframe, gprop, category, or limit, leaving most parameters undocumented anywhere.

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 opening phrase 'Where a keyword is searched most' states the resource and the breakdown dimension (geography) clearly, which separates it from the time-oriented sibling interest_over_time. It is not a clean verb+resource statement and no sibling is named explicitly, so it lands just 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?

The resolution/geo compatibility rules ('COUNTRY (geo='' only)', 'CITY and DMA ... geo='US' or worldwide only') effectively tell the caller when a given configuration is legal, which is real usage guidance. However, there is no guidance on when to choose this tool over interest_over_time or related_queries.

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 behavioral burden, and it does a solid job: it discloses the 0-100 scale, downsampling to ~60 points, the summary fields, and valid gprop values. It does not mention error behavior or rate limits, but it provides meaningful operational detail beyond a bare verb phrase.

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 output and keyword constraint stated first. The gprop list is efficient, and the parenthetical rationale for youtube adds useful context without excessive verbosity.

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?

Since an output schema exists, the return value shape is handled externally. However, the description lacks guidance on geo/category semantics and does not explain valid timeframe formats beyond the default, leaving some ambiguity for an agent making a correct call.

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 for parameter meaning. It explains the gprop choices and limits keywords to 1-5, but it does not clarify geo, category, or timeframe formats beyond schema defaults, leaving part of the parameter surface undocumented.

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 returns search-interest time series with per-keyword summary metrics, which conveys the core function of a time-series tool. It is distinct enough from siblings like interest_by_region and related_queries by emphasizing the temporal dimension, though it does not explicitly name 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?

There is no explicit guidance on when to use this tool versus alternatives such as compare_keywords, interest_by_region, or trending_now. The time-series nature is implied, but no exclusions, prerequisites, or routing advice are provided, leaving the agent to infer the appropriate 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?

There are no annotations, so the description carries full responsibility for disclosing behavior. It does strongly indicate a read/browse/search action and gives an example of the output form. However, it says nothing about how the limit parameter behaves, whether the empty search returns all categories, or if the list is sorted—a moderate gap for a read-only tool with no pre-existing annotation context.

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 entire description fits into two sentences and carries immediate value. The first word is the primary verb, a concrete example is embedded naturally, and the final sentence tells exactly what to do with the result. No repetition or filler—every clause earns its place.

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 that an output schema exists, the description does not need to explain return fields. It sufficiently covers what the tool's purpose, how to invoke it, and how to apply results in the broader tool set. The only real gaps are the unresolved semantic of the limit parameter and some search matching details, which lower the score from a perfect context.

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 take on the burden of explaining parameters. It gives an example of what 'search' is for ('coffee' -> category path) and implies 'browse' covers the limit case, but it never mentions the limit parameter by name or explains its behavior. With only 2 parameters, both undocumented at schema level, a clear explanation of one is not enough; this is a meaningful gap.

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 starts with a specific verb and resource: 'Browse/search Google Trends category ids.' It gives a concrete example ('coffee' -> Food & Drink > Coffee & Tea) and explicitly explains its output is used by passing the id to other tools. This clearly distinguishes the function from its trend-analysis siblings, which operate on time series and related terms.

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 states when to use the tool: when you need category IDs to narrow other tools' results. It does not explicitly mention when not to use it or name alternate siblings like suggest_keywords, but the use case is clear enough. It provides a strong implication of usage but leaves exclusions and alternatives unstated.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It mentions throttling (up to ~16 requests per call), 24-hour caching, de-duplication, and tagging with a title angle. These are concrete behaviors that help the agent understand side effects and constraints. It does not mention read-only nature, but that is implied by the operation. The coverage is strong for a read-only mining tool.

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, dense sentence that packs purpose, examples, behavioral details, and limit into a compact form. It is front-loaded with the core purpose. While it could be split for readability, every clause adds useful information, and it avoids redundancy. It is appropriately concise for the amount of 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?

An output schema exists, so return values are covered. However, the description lacks parameter semantics and explicit usage guidance. It does not explain how 'seed' and 'geo' affect results, nor does it mention any ordering or filtering behavior. The behavioral details (throttling, caching) are good, but the missing parameter explanations and sibling differentiation make the tool incomplete for an agent to call it correctly without additional context.

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 for parameter explanations. It only mentions 'limit: 1-100' in the description, providing a range for that parameter. However, it does not explain what 'seed' refers to (beyond being a starting topic) nor the meaning or possible values of 'geo'. With three parameters and 0% schema coverage, this is a significant gap that leaves the agent guessing about required input semantics.

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 mines long-tail questions from Google Autocomplete for a given seed, with explicit examples of expansions ('how to', 'why', 'what is', 'vs'). It distinguishes itself from siblings like related_queries or suggest_keywords by focusing on question-form queries and mentions the downstream use (FAQ, AEO content). The verb 'mine' plus resource 'questions' is specific and unambiguous.

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 usage by mentioning 'Feeds FAQ sections and answer-engine (AEO) content', which tells the agent when this tool is relevant. However, it does not explicitly contrast with sibling tools (e.g., related_queries, suggest_keywords) or state when not to use it. The context is present but not explicit, so the agent must infer the distinction.

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; it usefully discloses a 15-minute cache and the limit range (1-50), which the schema does not. However it says nothing about the geo parameter's behavior, error cases, or whether results are scoped or sampled, leaving meaningful behavioral gaps for a network-backed tool.

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?

Compact, front-loads the output-focused purpose, and appends operational facts (cache, limit) without padding. Slight awkwardness in the enumerated list, but no wasted sentences.

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?

An output schema exists, so return-shape explanation is not strictly required, yet the description still names the outputs, adds caching and limit bounds, and gives a use-case hint. Only the geo parameter's meaning and scope remain unaddressed.

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% across 3 parameters, so the schema contributes nothing. The description rescues 'limit' by defining it as headlines returned with a 1-50 range (bounds absent from the schema), but 'topic' and 'geo' receive no semantic elaboration beyond their names.

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?

States a specific verb+resource ('who is covering a topic in Google News') and enumerates the three outputs returned: headlines, a publisher frequency table, and a recency histogram. The Google News source is named, which separates it from wiki_attention and interest_over_time among 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 parenthetical '(mention / pitch targets)' hints at a PR/media-outreach use case, but the description never states when to prefer this over siblings like trending_now or wiki_attention, nor any exclusions or prerequisites. Usage is only implied.

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 adds meaningful detail: rising queries include % growth and is_breakout (>5000%), every item carries a title 'angle', and 'questions' lists question-shaped queries. This exceeds a bare statement of function, though it omits rate limits or error 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?

Two sentences with no wasted words. The main purpose is front-loaded, followed by the most decision-relevant output details and a clear use case. Every sentence earns its place.

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 description sufficiently explains what the tool returns and why an agent would use it, and an output schema exists so return-value details are covered elsewhere. However, with six parameters and zero schema description coverage, the lack of parameter guidance makes the definition incomplete for confident invocation, especially for ambiguous params like gprop and category.

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. Only 'keyword' is implied by the phrase 'related to a keyword'; geo, gprop, limit, category, and timeframe receive no explanation. Defaults exist in the schema, but their semantics are unexplained, leaving an agent to guess.

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 returns top and rising search queries related to a keyword, using specific behavior ('rising' includes % growth and is_breakout) and naming the question-shaped subset. It is distinguishable from sibling tools like related_topics and suggest_keywords because it centers on 'search queries' and blog-angle output.

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 'Best single source of fresh blog angles' gives a concrete use case and context for when an agent would choose this tool. It does not explicitly name alternatives or exclusion conditions, but the usage context is clear enough 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?

With no annotations provided, the description carries the behavioral burden. It discloses a non-obvious trait—that Google often returns no data—and explains the availability flag semantics. This goes beyond a basic summary, though it does not detail all response behavior or edge cases.

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 concise sentences with the core purpose front-loaded and the important fallback caveat placed second. Every word earns its place.

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 description is useful for basic invocation and fallback decisions, and the output schema covers return values. However, with six parameters and zero schema descriptions, the lack of parameter guidance leaves meaningful gaps for correct invocation with non-default options.

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 for the six parameters, but it only mentions 'keyword.' The meanings of geo, gprop, limit, category, and timeframe are left entirely to inference from their names and defaults.

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 returns 'Top and rising Knowledge-Graph topics related to a keyword,' identifying both the specific verb and resource. It also differentiates from the sibling tool related_queries by mentioning the fallback path, so an agent can distinguish them.

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 explicit guidance: Google often returns nothing here, and if available=false the agent should fall back to related_queries. This directly tells the agent when to use the tool and when to switch to an alternative.

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 present, so the description carries the burden. It discloses that this is a read-style suggestion API and what it returns. It does not mention errors, limits, or external dependencies, but for this simple tool the core behavior is clear.

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?

Two dense sentences with no wasted words: output shape first, then concrete usage scenarios.

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 one-parameter read tool with an output schema, the description covers purpose, response fields, and usage context. It stops short of documenting edge cases or output size limits, but the core calling context is complete.

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 gives no parameter descriptions, so the description compensates by clarifying that the keyword is used to retrieve entity suggestions and can disambiguate ambiguous terms. It could add input format details, but the one-parameter surface is simple.

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?

States a concrete operation—returning Google entity suggestions for a keyword—and identifies the response fields (title, type, mid), distinguishing it from trend/time-series 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?

Gives explicit use cases: disambiguate ambiguous terms or find the canonical topic ID. It doesn't name sibling tools or when not to use the tool, but the guidance is still actionable.

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 disclosure burden. It reveals the ~24h freshness window, geographic scoping, approximate traffic, and linked headlines. It does not cover data caveats or response behavior in depth, but provides a reasonable baseline.

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?

Two tight sentences front-load the core behavior, scope, and output contents, followed by a practical use case. Every sentence adds value and there is 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?

For a read-oriented tool with an output schema and optional parameters, the description supplies enough context: time range, geo scope, output elements, and intended use. Minor gaps remain around exact geo format and max_articles semantics, but these are not blocking.

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 clarifies that geo refers to a country or US state and hints at max_articles through 'linked news headlines', but it never explicitly ties max_articles to the number of headlines returned.

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 identifies the tool as returning real-time trending searches for a country or US state, with approximate traffic and linked news headlines. This distinguishes it from sibling tools like interest_over_time or related_queries, though it does not explicitly name an alternative.

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?

'Good for newsjacking' gives a clear, actionable use case and implies the tool is for time-sensitive, content-relevant trend discovery. It does not mention exclusions or alternatives, but the context is sufficient for basic routing.

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 burden and does well: it discloses what statuses are reported (live/error/idle with last error), that cache usage and settings are included, and that no network requests are made. Remaining gaps about cost or rate behavior are minor given the output schema covers the return shape.

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?

Two tight sentences, front-loaded with what is reported and closed with the useful no-network-request property. No wasted text.

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, read-only status tool with an existing output schema, the description adequately covers what the tool inspects and its side-effect-free nature; return-value details are appropriately delegated to 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 tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a parameterless tool 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?

States a specific resource and scope: the health of every data source, plus cache usage and effective settings. It is clearly distinguishable from the sibling tools, which are all data-retrieval operations, though it doesn't name a specific sibling it contrasts with.

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 is only implied – an agent can infer this is a diagnostic/health-check tool, and the 'Free: makes no network requests' note hints at low-cost use, but there is no explicit when-to-use or when-not-to-use guidance relative to the query 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?

With no annotations, the description carries the full burden and does well: it discloses a dedicated rate lane ('Own rate lane'), a 24-hour cache, and the branching failure mode (has_article=false with a related_article suggestion). It does not mention auth requirements or pagination, so it falls short of a perfect 5.

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 question is front-loaded in the first sentence, followed by output shape and constraints. It is dense and slightly cramped, but every sentence carries information; 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?

For a 3-param tool with an output schema, the description is complete: it covers inputs, return shape, fallback behavior, caching, and rate-lane characteristics. An agent has everything needed to call it and interpret results despite zero schema description coverage.

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%, so the description must compensate, and it does: it documents the `days` range (7-90), `lang` as the Wikipedia edition with examples ('en', 'pt', 'de'), and `topic` implicitly via the exact-title lookup. It omits the default value for `days`/`lang` but adds real meaning beyond the bare 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?

States a specific verb and resource: it answers whether Wikipedia has an exact-title article for a topic and whether attention is growing. The output contract (exact article fields, daily pageviews, has_article=false fallback) is spelled out, so an agent can distinguish it from siblings like interest_over_time or news_coverage.

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?

It hints at usage ('verify gaps before citing') and describes what happens when no exact article exists, which implies a validation use case. However, it never explicitly contrasts itself with any sibling (e.g., interest_over_time for Google Trends vs. this for Wikipedia), leaving the when-to-choose decision to inference.

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