YouTube Trends API

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so safety is covered. The description adds genuinely non-structured behavior: cost of 1 request per source, and that a plan-limit message is returned when rate limited or the monthly quota is exhausted — useful operational context an agent cannot get elsewhere.

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?

Front-loaded with what it returns, then the distinguishing use case, then cost and error behavior. Every sentence carries distinct information; no restatement of the tool name 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?

No output schema exists, so the description must describe return values — and it does, enumerating item types, engagement metrics and top comments. Combined with cost and quota-message disclosure, an agent has everything needed to call this correctly and interpret limits.

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 still adds meaning by tying results to sources ('per source') and clarifying that comments and engagement are per-item, which explains how the source, limit and comments parameters interact.

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-plus-resource: retrieves what people are posting, reading and betting about a keyword right now, returning posts, videos, news, discussions and prediction markets. It also explicitly distinguishes itself from the measurement siblings ('get_time_series and get_growth measure how much'), so an agent can pick correctly without opening another schema.

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 explicit use case ('use it to explain why something is trending or what the conversation says') and names the alternative tools with the condition that selects them (measurement vs. explanation). Nothing about when to reach for it is left to inference.

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?

Annotations already cover read-only/idempotent/non-destructive, and the description adds behavior beyond them: values are on a 0-100 scale with absolute volume when available, and a plan-limit message is returned on rate limit or quota exhaustion. It does not describe pagination or failure modes beyond the quota message, so not a 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?

Front-loads the purpose and the sibling routing in the first two sentences, then handles clarifications. Slightly padded by restating the preset-window format already enumerated in the schema, but nothing is wasted overall.

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?

No output schema exists, so the description correctly supplies return semantics (0-100 scale, optional absolute volume) and quota-limit behavior. Given six parameters with rich schema coverage, this is nearly sufficient; a bit more on what a per-source/per-window result collection looks like would close the 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?

Schema description coverage is 100%, so the schema carries the parameter burden with detailed per-source keyword formats, country/language/resolution restrictions, and the full preset list. The description only restates that windows are preset strings like '12M' and that sources can be multiple, adding no semantics beyond the schema — baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific operation ('Point-to-point growth for a keyword on one or more sources') and immediately separates itself from get_time_series (full series) and get_top_trends (live app boards). An agent can distinguish this from every sibling without opening a schema.

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?

Explicit routing: 'Designed for growth questions; for charting or custom math, get_time_series provides the full series.' It also preempts a likely confusion by clarifying that App Downloads/App Rankings are keyword sources here, not the live boards on get_top_trends.

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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive behavior. The description adds valuable behavioral context beyond those: it discloses that a plan-limit message is returned when rate limited or when the monthly quota is exhausted, and it describes the return values (0-100 plus volume). It does not cover authentication or any series-length/pagination behavior, leaving a minor 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 four sentences with zero waste: purpose first, then usage alternatives, then rate-limit behavior. It is front-loaded and appropriately sized for the tool's complexity.

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 rich 100%-covered input schema and the absence of an output schema, the description is complete enough for correct invocation. It covers return format, rate-limit/quota behavior, and sibling alternatives, while the schema handles source-specific keyword formats and optional filters.

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

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

Schema description coverage is 100%, so the schema already provides detailed parameter semantics for keyword, source, country, language, and resolution. The description only reinforces the one-keyword/one-source constraint and does not add syntax or conditional rules beyond what the schema already states.

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 and resource: 'Full historical series for one keyword and one source', and it clarifies the return shape (0-100 values, plus volume). It explicitly distinguishes itself from get_top_trends and get_growth, so an agent can identify its role without opening the schema.

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 names the alternative for live 'what's trending now' boards (get_top_trends) and the alternative for most growth questions (get_growth), making the routing conditions explicit. This is exactly the when-to-use-this-vs-alternatives guidance needed.

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?

Goes well beyond the readOnly/idempotent annotations by disclosing that data is fetched live every call with no storage, and that a plan-limit message is returned on rate limiting or monthly quota exhaustion — operationally important for an agent's retry logic. It stops short of describing the actual post payload or ordering, so it is very good rather than complete.

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?

Four short sentences, each carrying distinct information: what it returns, freshness semantics, when to pick it, the sibling alternative, and the failure mode. The core scope is stated first with no preamble.

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 no output schema, the description still conveys the shape at a high level ('top posts' per hashtag/source) and covers the quota failure mode. Slight gap: it does not describe ordering or how results are ranked, nor whether an empty result is possible for an unknown hashtag.

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% and the schema documents all three parameters including the valid source values and the limit default/min/max, so the description correctly defers. It adds nothing extra about parameter behavior, which is the expected baseline here.

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 ('Live top posts') bounded to exactly one hashtag and one source, and explicitly contrasts with the sibling that ranks hashtags instead. An agent can distinguish it from get_top_trends without opening either schema.

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?

'For when the user already has a hashtag and wants the posts winning on it' gives the selecting condition, and 'get_top_trends ranks the hashtags themselves' names the alternative and its own condition. Both sides of the routing decision are explicit.

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?

Annotations already declare read-only, idempotent, non-destructive and open-world, so the safety profile is covered. The description still adds non-obvious behavior: a live-fetch fallback when a board isn't in the database, a default US board, and a plan-limit message returned on rate limit or quota exhaustion. It does not describe row shape or pagination behavior, so it stops short of a 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 most decision-relevant facts (one feed type, no keyword, category semantics) are front-loaded, and each subsequent sentence covers a distinct constraint. A couple of sentences reinforce points already in the schema ('Live boards are only available here', 'Returns a plan-limit message'), which costs some efficiency.

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, multi-mode tool with no output schema, the description covers mode selection, fallback fetching, geographic defaults, and error messaging. It is slightly thin on what a returned board row looks like, though it points to the sort-dependent mover fields indirectly.

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% and the parameter descriptions are already detailed (valid type list, country applicability, category guidance, window semantics). The description restates the category-type coupling and window values but adds no syntax, format, or edge-case detail beyond what the schema provides, so baseline 3 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?

Opens with a specific verb+resource ('Live top-trending board for exactly one feed type') and immediately carves out scope ('No keyword'), which separates it from the keyword-lookup siblings. It also names the sibling it is not (get_time_series) and lists the concrete feed families it serves.

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 routing rules: use sort='rank_change' for climbers vs a prior snapshot, and use get_growth or get_time_series for app history with an Android bundle ID. It also states a hard exclusion ('Live boards are only available here, not via get_time_series'), which is exactly the when/when-not shape an agent needs.

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?

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds genuinely useful behavioral context not in the annotations: 'Free: does not use a request,' which matters for quota-awareness. It stops short of describing return format/pagination but no output schema exists.

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, zero waste, with the return contents front-loaded ahead of the invocation guidance. Nothing extraneous.

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 zero params and no output schema, the description fully covers what the tool does and what it returns (plan, used, left, reset time) plus the cost note. An agent has everything needed to call it 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?

The tool takes zero parameters, so the baseline is 4. The schema is empty and there is nothing for the description to compensate for; no confusion is possible.

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?

Names a specific verb ('Shows') and enumerates the exact resource fields returned: plan, requests used, remaining, and reset time. An agent can clearly distinguish this diagnostic tool from the analytics siblings (get_growth, get_time_series, get_top_posts, get_top_trends).

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 trigger conditions: 'when the user asks about their limit' and 'after a limit error.' Clear invocation context. It doesn't name when-not-to-use or an alternative, but the siblings are unrelated analytics tools so an exclusion is less necessary.

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