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 disclosing behavioral traits. It only says 'Fetch', implying a read-only operation, but does not mention what 'all' means (e.g., pagination), how domain/country affects results, or any error behavior. This is sparse 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, well-structured sentence that immediately states the action and resources. No unnecessary words 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 3-parameter tool, the description gives the core functionality but omits information about the return format, the exact effect of optional parameters, and whether 'all' implies pagination. Given no output schema and no annotations, the description is adequate but not thorough.

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%, with each parameter described. The description adds no extra 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 uses a specific verb ('Fetch') with a clear resource ('all buying offers and sellers') and identifies the key input ('by ASIN'). It distinguishes this tool from siblings like amazon_product or amazon_reviews, which focus on other product aspects.

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 when to use the tool (when offers/sellers are needed) but provides no explicit guidance on when not to use it or alternatives. With many sibling tools, this lack of differentiation guidance is a gap, though the purpose is clear enough to infer basic use.

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 only mentions the scraping action and basic data types, but does not disclose potential rate limits, anti-bot challenges, error handling, or the structure of the response. This leaves significant behavioral aspects undocumented.

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, focused sentence that front-loads the core action and target. It contains no redundant information and every word contributes to understanding the tool's function.

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 a well-documented schema, the description covers the primary purpose and output content. However, the lack of an output schema and annotations means the description should explain more about return values, error cases, and usage constraints. It is minimally adequate but leaves gaps for complex scenarios.

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 already provides 100% parameter documentation, so the description need not repeat it. The description adds 'by ASIN' reinforcing the required parameter, but does not explain the optional domain, country, or postal_code parameters beyond what the schema already states. This meets the baseline for a well-documented 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 action ('Scrape'), the resource ('a single Amazon product page'), the key input ('by ASIN'), and the data captured ('price, specs, ratings'). This distinguishes it from sibling tools like amazon_search (search) and amazon_reviews (reviews), making the purpose explicit 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 for retrieving details of a specific product via ASIN, but it does not explicitly mention when to choose this over alternatives like amazon_search or amazon_reviews. There are no exclusions or alternative references, so the guidance 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?

No annotations are provided, so the description must carry the behavioral disclosure burden. It only says 'Scrape' without indicating whether it is read-only, any rate limits, anti-bot risks, or output format. It does not disclose behavioral traits beyond the literal act of scraping.

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 directly states the tool's purpose and key input. Every word contributes meaning 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?

With no output schema and no annotations, the description should provide more context about the response structure, pagination behavior, or error conditions. It mentions the high-level outputs (reviews, ratings, reviewer details) but lacks details on how many reviews per page, sort order, or what data fields to expect.

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?

All parameters (asin, page, domain, country) have descriptions in the schema, giving 100% coverage. The description adds no additional parameter semantics; it only reiterates the ASIN requirement already present in 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 uses the specific verb 'Scrape' with a clear resource ('customer reviews, ratings and reviewer details') and a clear identifier ('by ASIN'). This distinguishes it from sibling tools like amazon_search (search) and amazon_product (product details).

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 when you have an ASIN and want reviews for that specific product. It does not explicitly mention when not to use it or provide alternatives, but the 'by ASIN' constraint gives clear context for usage.

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 'scrape' which suggests an unofficial/hard-scrape nature, but it does not disclose specifics like rate limits, proxy usage (despite the premium param), pagination behavior, or success rates. The description provides minimal transparency beyond the action itself.

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, front-loaded with the key action and resource. It is concise and free of filler, making it easy to parse quickly. It earns a perfect score for conciseness and structure.

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 7 parameters, no output schema, and no annotations, the description is far from complete. It lacks any information about return format, result structure, error handling, or behavioral nuances. For a scraping tool with low-level parameters, this is critically insufficient.

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 covers 100% of parameters with descriptions, so the baseline is 3. The description only mentions 'query' implicitly via 'for a query', adding no additional meaning beyond what the schema already provides. It does not explain how parameters like domain, country, or premium 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?

The description clearly states the tool scrapes Amazon search-result listings for a query. The verb 'scrape' and resource 'Amazon search-result listings' are specific, and the phrase 'search-result listings' helps distinguish it from sibling tools like amazon_product, amazon_reviews, and amazon_offers.

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 alternatives (e.g., google_search, amazon_product). It does not mention any context, prerequisites, or exclusions. The only implied usage is 'for a query', but there is no explicit comparative guidance.

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 for behavioral disclosure. It only states that output is structured JSON, omitting important scraping behaviors such as rate limits, authentication needs, error handling, or whether raw HTML can be returned (though 'html' param exists). This is 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 a single, front-loaded sentence with no filler. Every part ('Scrape', 'Baidu', 'Chinese-market', 'search results', 'structured JSON') contributes meaningful information, making it appropriately concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 5 parameters, no output schema, and no annotations, the description is too sparse. It lacks context about expected outputs, potential errors, usage limits, or prerequisites. The schema covers parameters well, but for a scraping tool, more behavioral context (e.g., rate limiting, blocked requests, language specifics) is needed to be 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 100%, so the baseline is 3. The description adds no extra parameter semantics beyond what the schema already provides, but it does not need to since all params are well documented. The 'Chinese-market' context in the description slightly reinforces the query semantics but adds no new detail.

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 ('Scrape') and resource ('Baidu (Chinese-market) search results') and states the output as 'structured JSON'. This clearly distinguishes it from sibling search tools like google_search and bing_search by specifying the Chinese-market scope.

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 when needing Chinese-market search results via the phrase 'Chinese-market', but it does not explicitly state when to use this tool over alternatives or provide exclusions. No alternative tools are mentioned, so guidance is merely implicit rather than 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?

No annotations are provided, so the description carries the full burden. It says 'scrape' which implies a read operation, but it does not disclose behaviors like rate limits, pagination handling, or the structure of the returned JSON. The description adds no beyond the basic action.

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 concise sentence, front-loaded with the core purpose. No filler 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?

Despite having 8 parameters and no output schema, the description only states the core function. Missing details about result format details, when to use Bing over other engines, or any operational constraints. The description is incomplete for a tool of this complexity.

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 every parameter is already documented with a description. The tool description itself adds no param semantics, but this is acceptable as the schema fully covers the parameters.

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

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

The description clearly states the action ('scrape'), resource ('Bing search results'), and output ('structured JSON'). It distinguishes from sibling tools like google_search and baidu_search by explicitly naming Bing.

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 provided on when to use this tool vs alternatives. With many search siblings available, the description does not mention any specific use cases, advantages, 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions sending a prompt to ChatGPT and returning structured JSON, but does not disclose potential side effects, rate limits, external dependencies, authentication requirements, or error behavior. The phrase 'at scale' is vague and lacks concrete detail.

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, focused sentence of 14 words. It front-loads the primary action and output, with no redundant information. Every word serves a purpose, including 'at scale' to imply batch/high-volume usage.

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?

Despite the tool being simple (2 params, no output schema, no annotations), the description lacks important contextual information such as when to use it, what to expect regarding response structure beyond 'JSON', and any operational constraints. An agent would have insufficient information to confidently select this tool over similar search/scraping tools.

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%, with both parameters ('prompt' and 'html') having clear descriptions. The tool description adds minimal value beyond the schema, though it does reinforce that the default output is structured JSON (consistent with html=false). The baseline of 3 for full schema coverage 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 the action ('Send a prompt'), the resource ('ChatGPT'), and the outcome ('response as structured JSON, at scale'). It uniquely identifies this tool among the sibling tools, none of which mention ChatGPT. The verb+resource+output structure is specific and immediately understood.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. It simply states what the tool does without contextual decision-making support. There is no reference to sibling tools or specific use cases.

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 carry the burden of behavioral disclosure. It only says 'scrape' without detailing return format, pagination, rate limits, or what 'listings' includes. This is minimal and leaves significant gaps.

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, concise sentence that immediately conveys the tool's purpose and required input. No unnecessary information 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?

While the tool is simple with only two parameters, the description lacks details about the returned data structure or behavior since there is no output schema. It is minimally adequate but leaves the agent guessing about response format and content.

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 provides descriptions for both parameters with 100% coverage, so the description adds no extra parameter context. Baseline of 3 is appropriate as the schema handles the 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 scrapes eBay search listings from a provided eBay search URL, using a specific verb and resource. This distinguishes it from sibling tools like amazon_search or google_search.

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 for eBay search scraping but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusion criteria. It is clear enough but lacks explicit comparison to sibling tools.

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 implies a read-only retrieval action but does not mention response format, authentication requirements, rate limits, or any side effects. The phrase 'conversational answer' gives a hint but not enough detail about the actual output structure.

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 with no wasted words. It efficiently states the core action and resource. While it is terse, that is not a structural flaw; the under-specification is penalized in other dimensions.

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?

Despite complete schema coverage, the tool lacks an output schema and annotations. The description gives no indication of what the retrieved 'conversational answer' looks like (e.g., text, JSON structure, citations). This is a critical gap for an agent to correctly process the tool's result. Usage guidance is also absent, making the overall 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?

The input schema has 100% coverage with each parameter (query, html, safe, country, language, location) having a meaningful description. The tool description itself adds no extra parameter context, but the schema fully compensates, 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 uses a specific verb 'Retrieve' with a clear resource ('Google's AI Mode conversational answer') and a query object. It is clear and unambiguous, though it does not explicitly contrast with the sibling tool google_ai_overview, which may have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives such as google_search or google_ai_overview. There is no mention of scenarios, exclusions, or preferred use cases, so the agent gets no decision 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 behavioral disclosure burden. It discloses the URL expiry (~2 minutes), which is critical context. However, it does not describe the response format, error behavior, or any rate limits, leaving gaps for a fetch 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 two sentences, front-loaded with the main action, and contains no filler. It efficiently conveys the purpose and the key usage constraint.

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 single-parameter tool, the description covers the what, how, and a critical temporal constraint. However, the absence of an output schema means the description could have mentioned the expected return format, but this is a minor gap given the tool's simplicity.

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 already provides a complete description of the 'url' parameter (source and expiry). The tool description essentially repeats this information without adding new semantics, so the baseline score of 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?

The description clearly states the tool's action ('Fetch the Google AI Overview block') and its specific resource ('for a search'). It distinguishes itself from siblings by referencing the AI-overview URL from google_search responses, which clarifies its role in the search workflow.

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: use this after a google_search to fetch the AI Overview block using the returned URL. It does not explicitly exclude alternatives or mention when not to use, so it falls 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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It only says 'fetch' without mentioning potential rate limits, authentication, data freshness, or response formats. It does not even explicitly confirm it is read-only, although 'fetch' implies it. This is a minimal disclosure that does not prepare the agent for possible behavioral nuances.

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, concise sentence with no unnecessary words. It front-loads the main action ('Fetch live stock quotes and market data') and source ('from Google Finance'), making it easy to parse and immediately informative.

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 three parameters, one required, no output schema, and no nested objects, the complexity is low. The description gives a clear purpose, and the schema documents all parameters. It lacks detail on response structure and potential limitations, but given its simplicity, the description is sufficiently complete for an agent to invoke the tool 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 schema has 100% coverage of parameter descriptions, including ticker format and language code, so the baseline is 3. The description adds no additional meaning beyond the schema, such as examples or parameter constraints. It does not compensate or elaborate on any parameter specifics.

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 fetches live stock quotes and market data from Google Finance, using a specific verb and resource. This distinguishes it from sibling tools like google_news or google_search, which serve different purposes. The action and scope are immediately clear.

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 provided on when to use this tool versus alternatives, nor are any exclusions or alternative tools mentioned. Usage is only implied by the tool name and purpose, not explicitly stated. An agent is left without clear direction on when to choose google_finance over other search tools.

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 for behavioral disclosure, but it only mentions the data returned (prices, ratings, availability). It does not address pagination tokens, output format, rate limits, or any side effects, leaving significant behavioral gaps.

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 directly captures the core function without waste. It is concise and well-structured, with every word contributing to the purpose.

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 complexity (19 parameters) and lack of an output schema, the description is notably incomplete. It does not explain how to use next_page_token for pagination, what property_token does, or what the response structure looks like, making it difficult for an agent to fully understand the tool's behavior.

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 provides 100% parameter coverage with detailed descriptions for each of the 19 parameters, so the description does not need to add additional semantics. The description adds no parameter-specific explanation beyond the schema, landing at the baseline of 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?

The description uses a specific verb 'Get' and clearly identifies the resource: hotel and vacation-rental prices, ratings, and availability from Google Hotels. This distinguishes it from sibling tools like google_maps or google_shopping, making the purpose unmistakable.

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 provided on when to use this tool versus alternatives such as google_maps or google_shopping, nor are any prerequisites or exclusions mentioned. The description simply states what the tool does, leaving the agent without direction 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?

With no annotations provided, the description carries full responsibility for behavioral disclosure. It only mentions 'sources and thumbnails' but does not explain any constraints, rate limits, pagination behavior, or response structure. This is insufficient for a tool with 8 parameters and no output 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 a single, well-structured sentence that is immediately informative. Every word contributes value, with no redundant phrases or unnecessary context, making it highly concise and 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?

Despite covering the core purpose, the description omits crucial context: usage guidelines, behavioral nuances, and details about the return format. Since there is no output schema, the description should at least indicate the structure or content of the response beyond 'sources and thumbnails', but it does not.

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 covers all 8 parameters with descriptions (100% coverage), so the description does not need to repeat parameter details. However, it also does not add any extra meaning beyond the schema, such as example values or parameter interactions, warranting the baseline score of 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?

The description uses a specific verb 'Get' and identifies the exact resource 'Google Images search results', clearly distinguishing it from general search tools like google_search. It also highlights the key output components ('sources and thumbnails'), giving a precise understanding of what the tool does.

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 like google_lens or google_search. No context is provided about use cases, exclusions, or preferred scenarios, leaving the agent to infer usage solely from the tool name.

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 is the sole source of behavioral disclosure. It discloses that the operation is a retrieval ('Retrieve'), but gives no details about response behavior, error cases, or rate limits, and does not explain what 'immersive product popup' entails beyond the name.

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, front-loaded with the main action and resource, and contains no filler words. It is appropriately concise for its purpose.

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 no output schema and no annotations, leaving the description to carry contextual weight, but it offers minimal information about the response format or how to obtain the page token. While the schema fully documents parameters, the description does not sufficiently compensate for the lack of output schema and behavioral 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?

The input schema covers 100% of the parameters with detailed descriptions, so the description does not need to add parameter information. The description mentions only 'page token' in passing and adds no additional semantic value 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 uses a specific verb 'retrieve' and specifies the resource 'detailed product info from Google's immersive product popup', which clearly distinguishes it from broader siblings like google_shopping and google_search. It also states the mechanism ('via a page token'), making the tool's function 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 through 'via a page token', indicating a prerequisite from a prior Google Shopping/Search response, but it does not provide explicit guidance on when to prefer this tool over alternatives like google_shopping or google_lens. There are no exclusions or alternative tool references.

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, but it only states 'Scrape job listings' without mentioning pagination, rate limits, output format, or any other behavioral traits. It adds no value beyond the basic action.

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 with no fluff, but it is under-specified for a tool with 7 parameters. It is concise but not appropriately informative, earning a middle score.

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 lack of annotations, no output schema, and the tool's complexity (7 params, pagination via next_page_token), the description is far too minimal. It doesn't explain response structure or pagination, leaving agents under-informed.

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 already provides full descriptions for all 7 parameters (100% coverage), so the baseline is 3. The description adds no additional parameter context, but the schema covers the load, making this 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 'Scrape' and a clear resource 'job listings aggregated across Google Jobs', clearly distinguishing it from generic search tools. However, it doesn't explicitly name sibling tools or alternatives, preventing a perfect 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?

No guidance is given on when to use this tool vs. alternatives like google_search or web_scrape. The description lacks any context, exclusions, or alternative references, leaving the agent without selection criteria.

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 only states the function and says nothing about output format, rate limits, authentication, or result structure. This is a significant gap 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, front-loaded sentence with zero wasted words. It immediately communicates the core action and the specific result types, making it highly scannable.

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?

Although the schema is thorough, the tool has 7 parameters, no output schema, and no annotations. The description does not explain what the response looks like, how to choose between result types, or any behavioral nuances. This makes it insufficient for fully understanding the tool's 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?

The input schema documents all 7 parameters with descriptions, achieving 100% coverage, so the baseline is 3. The description adds little beyond the schema—only mentioning 'visual matches and products' which maps to existing schema fields, but does not enrich understanding of the parameters.

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

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

The description uses a specific verb ('Reverse-image search') and resource ('via Google Lens'), and explicitly states the scope ('for visual matches and products'). This clearly distinguishes it from siblings like google_search or google_images.

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 clearly implies the tool is for reverse-image search and mentions two output categories (visual matches, products), giving clear context. However, it does not explicitly mention when not to use it or name alternative tools, so it stops one step short of a full 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 burden of behavioral disclosure. It only says 'scrape' and gives no information about rate limits, pagination behavior, authentication requirements, or data output format, which is a significant gap for an agent.

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, clear sentence that immediately conveys the tool's purpose. No filler or unnecessary repetition exists, making it appropriately concise and front-loaded.

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?

Despite full schema coverage, the tool has 9 parameters and no annotations or output schema. The description lacks context about how scraping works, typical usage patterns, pagination, or what the results look like, making it incomplete for an agent to safely and effectively invoke.

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 provides descriptions for all 9 parameters with 100% coverage, so the baseline is 3. The description adds no extra parameter semantics beyond what the schema already documents, such as the meaning of 'll' or 'data'.

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 action ('scrape') and resource ('local business listings and place details from Google Maps'). It specifies the domain, but does not explicitly contrast with sibling tools like google_search or web_scrape, so it lacks strong 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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention use cases, prerequisites, or exclusions, leaving the agent to infer suitability from the name alone.

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 must disclose behavioral traits. It only mentions output components, not pagination, rate limits, limitations, or whether it returns full content. The term 'scrape' implies unofficial access but no specifics are provided.

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?

One sentence, immediately front-loaded with the action and result. No filler words, fully efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The schema richly documents 7 parameters, and the description conveys core purpose and return values. However, it does not introduce the query vs. token modes or geo/language options, which are discoverable via schema but would benefit from a brief mention. Still, adequate given schema support.

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 all 7 parameters are documented in the schema. The description itself adds no parameter-level meaning beyond mentioning output types, which is not parameter semantics. Baseline 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 uses a specific verb 'scrape' with resource 'Google News' and lists return types (headlines, sources, timestamps), clearly differentiating from siblings like google_search and google_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?

No guidance on when to use this tool versus alternatives. The description only states the action without exclusions or comparisons to sibling tools like google_search or google_ai_overview.

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 behavioral disclosure, but it only says 'search,' revealing nothing about return format, pagination, filtering behavior, or limitations.

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 efficiently communicates the core purpose without any wasted words or redundancy.

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 13 parameters, no annotations, and no output schema, this description is severely under-specified. It lacks any detail on return values, edge cases, or how to construct effective queries.

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 covers all 13 parameters with individual descriptions, so the baseline is 3. The description adds no additional parameter meaning, but the schema is sufficient.

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 'Search patent records and metadata via Google Patents,' using a specific verb and resource. It distinguishes itself from siblings like google_search and google_scholar by focusing on patents.

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 provided on when to use this tool versus alternatives. It mentions no exclusions, prerequisites, or contexts, leaving the agent without any decision-making information.

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 transparency. It only states the basic search functionality and does not disclose behaviors such as pagination, return format, rate limits, or the purpose of specific parameters like 'cites' for citation tracking.

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, clear, front-loaded sentence with absolutely no filler. It efficiently communicates the core purpose without unnecessary 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?

Given the absence of an output schema and annotations, the description is too minimal to fully inform an agent about response structure, pagination, or how to combine optional parameters. It only covers the basic action, leaving significant gaps for a tool with 8 parameters.

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 a 100% coverage of all 8 parameters with descriptions, so the baseline is 3. The description adds no additional parameter semantics beyond what the schema already documents, which is acceptable but not enhancing.

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's function: 'Search academic papers, authors and citation counts via Google Scholar.' It uses a specific verb ('Search'), names the resource (academic papers, authors, citation counts), and distinguishes it from sibling tools like google_search or google_patents.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or sibling comparisons. The academic context is implied but not explicitly stated as a differentiator from other search tools.

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. It discloses that results are returned as structured JSON and mentions result types, which is useful, but it does not cover critical behavioral aspects such as API key requirements, rate limits, pagination behavior, or latencies. The 'real-time' and 'scrape' wording hints at live fetching but lacks depth.

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, concise sentence that front-loads the core function and provides a representative list of output components. 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?

Given the tool's complexity (12 params, no output schema, no annotations), the description is adequate but not complete. It communicates the return format and general content, and the schema fills parameter gaps, but it misses usage context (e.g., when to use vs. siblings) and behavioral details. It does not fully compensate for the absent output schema and annotations.

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 has 100% description coverage, so the schema already documents all 12 parameters with clear descriptions. The tool description adds no additional parameter-level meaning or examples, so it meets the baseline but does not elevate it.

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

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

The description clearly states the tool's function with a specific verb ('Scrape') and resource ('real-time Google Search results'), and explicitly lists the output types (organic results, ads, knowledge graph, people-also-ask). This distinguishes it from sibling tools like google_news or google_images, which target specific verticals.

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 this is the general-purpose Google SERP tool, covering a broad range of search result types, which provides clear context for when to use it. However, it does not explicitly contrast with alternatives or state when not to use it, so it stops short of full exclusions or alternative guidance.

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 full responsibility for disclosing behavior. It only says 'scrape' without explaining output format, pagination, rate limits, or other operational traits. This is a minimal disclosure that leaves significant behavioral aspects undisclosed.

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 with no redundant words. It is front-loaded with the action and subject, making it highly concise and easy to parse. Every word contributes meaning.

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 8 parameters, no annotations, and no output schema, the one-line description is insufficient. It does not explain return value structure, pagination behavior, or how parameters like domain, country, and language affect results, leaving significant gaps for an agent to use the tool 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 input schema has 100% parameter description coverage, so the baseline is 3. The tool description adds no additional parameter semantics beyond what the schema already provides, but it also does not need to, given the schema's completeness.

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 'Scrape product listings and prices from Google Shopping' uses a specific verb ('scrape') and resource ('product listings and prices from Google Shopping'), clearly distinguishing it from sibling tools like google_search or amazon_search. It is precise 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?

No usage guidance is provided. The description does not indicate when to use this tool instead of alternatives, nor does it mention any exclusions or prerequisites. It simply states what it does, leaving the agent without contextual direction.

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 but provides only a basic retrieval statement. It does not disclose response format, pagination behavior, how parameters like html or start affect output, or any safety/permission considerations. This is a significant gap for a tool with 10 parameters.

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

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

The description is a single concise sentence with a clear verb-resource-object structure. It is front-loaded and contains no filler, making it easy to parse and understand the core action.

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 complexity (10 parameters, no output schema, no annotations), the description is incomplete. It does not explain the response structure beyond mentioning a few fields, nor does it address how the tool behaves with various parameters or what makes it distinct from other video search tools. The rich schema partially compensates but the description itself leaves many questions unanswered.

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 detailed descriptions for all 10 parameters (100% coverage), so the baseline is 3. The description itself adds no parameter-specific information, but the schema already covers this dimension well, and the description's mention of 'sources, thumbnails, and dates' loosely aligns with expected output.

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 'Retrieve' and a clear resource 'Google short videos', and lists key returned fields (sources, thumbnails, dates). This distinguishes it as a video retrieval tool among search siblings, though it does not explicitly name alternatives. It could have been clearer about what 'short videos' means (e.g., YouTube Shorts or general Google video results), but it is sufficiently clear.

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 provided about when to use this tool versus other video or search tools, nor any exclusions or alternative references. The description simply states what it does without contextualizing use cases.

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 full responsibility for behavioral transparency. It only states the basic function and provides no details about output format, data limitations, rate limits, or how the data is normalized. The lack of an output schema makes this gap more significant, as the agent has no insight into what the tool returns.

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, clearly front-loaded sentence that conveys the core purpose in under 20 words. Every word is essential, with no fluff or redundancy. It is a model of 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?

Despite high schema coverage, the tool has 9 parameters, no output schema, and no annotations. The description provides only a high-level purpose and fails to explain the nature of the response (e.g., timeseries or map data, value ranges, normalization). Given the tool's complexity, the description is too minimal to fully inform an agent about expected behavior and return value.

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 even though the description itself does not elaborate on parameters. The tool description adds no extra meaning beyond the schema, but the schema already documents each parameter (e.g., query constraints, language, data_type enums). The description does not need to compensate for missing parameter information.

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

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

The description clearly states the tool's function: 'Track keyword search interest over time and by region via Google Trends.' It uses a specific verb ('track') and resource ('keyword search interest'), and the phrase 'over time and by region' distinguishes it from sibling search tools by focusing on trend analysis rather than general search results.

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 trend analysis, not general search, but does not explicitly mention when to use it over alternatives or provide exclusions. The context is clear enough given the sibling tools (e.g., google_search), but it lacks explicit guidance on use cases or boundary conditions.

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 only mentions 'public' profiles, but does not disclose rate limits, error behavior, premium mode implications, or output characteristics. This lack of detail leaves the agent without insight into expected 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 a single, well-structured sentence that immediately conveys the essential action and target. No redundant information or unnecessary elaboration, making it maximally concise.

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 low complexity (3 params, no output schema), the description provides sufficient context for a basic scrape operation. The schema fills in parameter details, but the lack of any annotation or output description leaves minor gaps in expected return behavior, though not critical.

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 descriptions cover all parameters (100% coverage), including the example for linkId and the purpose of premium. The description itself adds no additional meaning beyond referencing the slug, so the 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 clearly states the tool's action ('Scrape'), target ('public LinkedIn person or company profile'), and identification method ('by its public identifier'). It effectively distinguishes from sibling tools by specifying LinkedIn, unlike general web scraping or other social profile tools.

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 use for LinkedIn profile/company scraping via the given slug, but it does not explicitly compare with alternatives like x_profile or web_scrape. No exclusions or 'when not to use' guidance is provided, making the usage context only implicit.

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 states 'Returns the image' without disclosing load behavior, wait conditions, permission needs, rate limits, or failure modes. The return format (binary, URL, base64) is also unspecified.

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 no wasted words. It front-loads the core action and immediately states the output.

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 7 parameters, no output schema, and no annotations, the description fails to explain return format details, page load behavior, or potential errors. It is inadequate for a tool of this complexity.

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 has 100% coverage for all parameters, so the baseline is 3. The description adds no extra meaning beyond what's already in the schema; it only mentions image formats, which are already enumerated in the format parameter.

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 captures a screenshot of a web page and lists supported formats (PNG/JPG/WEBP). This specific verb+resource combination distinguishes it from sibling tools like web_scrape, which extracts text content.

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 for capturing web page images but provides no explicit guidance on when to choose this over alternatives like web_scrape for text extraction. There are no when-to-use or when-not-to-use statements.

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 must disclose behavioral traits. It mentions scraping but omits critical details like potential rate limiting, blocking risks, or that 'unified organic results' may aggregate data from multiple engines without clear provenance. The minimal disclosure is insufficient for a tool that performs web scraping.

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 concise sentence that immediately conveys the core function. Every word adds value, and it is front-loaded with 'Single endpoint', making the purpose clear without unnecessary elaboration.

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 lack of output schema and annotations, the description provides only a basic understanding. It does not mention that the tool aggregates across multiple engines, which is essential context for an agent comparing it to single-engine tools. The description is adequate but leaves gaps around behavior and scope.

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 covers 100% of parameters with descriptions, so the baseline is 3. The description adds no parameter-specific semantics, but the schema already provides adequate meaning for query, country, and language.

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 scrapes major search engines and returns unified organic results, using a specific verb ('scrapes') and resource ('major search engines'). This distinguishes it from sibling tools like google_search or bing_search, which target a single engine.

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 provided on when to use this tool versus alternatives like google_search, web_scrape, or other sibling search tools. The description lacks any mention of use cases, exclusions, or comparisons.

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 must disclose behavioral traits. It only states 'Scrape... results,' conveying the basic action but omitting important details such as authentication requirements, rate limits, or the response format. No side effects or limitations are mentioned.

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, concise sentence with no redundant words. It is efficiently structured, placing the action and resource at the beginning.

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 adequate for a single-parameter tool, but with no output schema and no annotations, it leaves the return format unspecified. An agent would need to know whether 'search results' means product titles, prices, links, etc., making the description minimally viable 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?

The schema already documents the single 'url' parameter with a clear example, and coverage is 100%. The description adds the phrase 'Walmart search URL' which mildly reinforces the parameter's purpose, but provides no additional semantic value beyond the schema. Baseline 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 uses 'Scrape' as a specific verb and 'Walmart search results' as the resource, clearly stating the tool's function. It differentiates from sibling tools like google_search or amazon_search by naming Walmart, though it does not enumerate the exact fields returned.

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 usage guidance is provided. The description does not mention when to use this tool versus alternatives like amazon_search or ebay_search, nor does it state any exclusions or preferred use cases. This leaves 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?

With no annotations, the description carries the full burden of behavioral disclosure. It transparently mentions the use of Scrapingdog's rotating proxies, optional JavaScript rendering, premium proxies, geo-targeting, and AI-based extraction, as well as the return type (HTML or extracted data). However, it does not cover error handling, rate limits, or limitations like login-protected pages, but the provided detail is substantial.

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 sentences, front-loaded with the core purpose, and every phrase adds value. It efficiently conveys the tool's scope, mechanisms, and output without 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?

Given the complexity of 9 parameters and no output schema, the description provides a high-level overview but lacks guidance on parameter interplay (e.g., wait only with dynamic), cost implications beyond dynamic's mention in schema, and return format nuances. The schema partially compensates, but the description alone is not fully complete for a tool of this complexity.

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 parameters are fully documented in the schema. The tool description adds no additional parameter semantics beyond what the schema already provides, making this a baseline score.

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

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

The description clearly states the tool's function: fetching any URL via a general-purpose web scraper with rotating proxies and returning HTML or extracted data. The verb 'fetches' and resource 'any URL' are specific, and it distinguishes itself from specialized siblings like google_search and screenshot by being general-purpose.

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 scraping arbitrary URLs but does not explicitly state when to use it instead of specialized tools like google_search or screenshot. There are no exclusions or alternative suggestions, leaving usage context to be inferred.

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 'public' as an access limitation but does not disclose rate limits, authentication needs, whether it scrapes live data, or behavior for non-existent/private profiles. This is a significant gap for a web scraping 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, scannable sentence that front-loads the primary action and resource. The parenthetical with examples adds useful detail without verbosity. Every word earns its place, making it highly concise and well-structured.

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 tool with no output schema and no annotations, the description covers the essential purpose and scope. It could be enhanced by mentioning what counts are included or any error behaviors, but the given information is adequate for basic usage. The lack of output schema is partially compensated by the examples of returned metadata.

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 profileId parameter is well described with examples ('elonmusk', 'nasa'). The tool description itself adds no additional parameter semantics beyond what the schema already provides, so baseline 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 uses a specific verb 'scrape' and clear resource 'public X (Twitter) profile's metadata' with examples (bio, followers, counts). It distinctively identifies the tool's purpose, differentiating it from generic web scraping or other profile tools like linkedin_profile.

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 whenever X profile metadata is needed but does not explicitly state when to choose this tool over alternatives or provide exclusions. No mention of alternatives like web_scrape or search tools, leaving the contextual decision to the agent without explicit guidance.

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 are absent, so the description carries the full burden of behavioral disclosure. The description simply restates the action ('Get') without mentioning any constraints, side effects, data scoping, or result behavior. There is no information about pagination, whether the full video list is returned, or whether channel metadata is complete. This provides no transparency beyond what the tool's name already implies.

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 concise sentence with no redundant wording. It is front-loaded with the action and resource. However, it is borderline under-specified for a tool with multiple output types, so it earns a 4 rather than 5 for lacking structural breakdown.

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 lack of an output schema and the complexity of returning both 'details' and 'videos', the description should clarify what constitutes 'details' and whether videos are a paginated list or summary. No such detail is provided. The description is adequate only for high-level understanding, but incomplete for practical use.

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%: all three parameters (channel_id, country, language) have descriptive text. The description adds no extra meaning beyond the schema, but the baseline of 3 applies because the schema already documents parameter semantics thoroughly. The tool description itself does not enhance understanding of the parameters.

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

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

The description 'Get a YouTube channel's details and videos' uses a specific verb ('Get') and a clear resource ('YouTube channel') while indicating two types of output (details and videos). This distinguishes it from sibling tools like youtube_search (search across videos), youtube_video (single video metadata), and youtube_comments, making the tool's purpose 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 offers no guidance on when to use this tool vs alternatives. It does not mention prerequisites (e.g., the channel_id requirement), nor does it contrast with youtube_search or youtube_video. Users are left to infer usage from the name, which is insufficient.

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 the full burden of disclosing behavioral traits. It only says 'Get comments', with no information about pagination, sorting, authorization, rate limits, or error behavior. This is a minimal action description, not a transparent behavioral 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 a single, front-loaded sentence with zero redundant words. It is concise and directly states the tool's function.

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 and no annotations, the description leaves unknown the response structure (e.g., what fields each comment contains), pagination behavior, and potential errors. For a simple tool with well-documented parameters, it provides the minimum for invocation but lacks the contextual enrichment needed for confident use.

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 each parameter (v, country, language) is already well-documented with examples and defaults. The tool description adds no additional parameter semantics, so the baseline of 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?

Description uses the specific verb 'Get' and identifies the resource as 'comments for a YouTube video', clearly distinguishing it from sibling tools like youtube_search or youtube_transcripts. The purpose is immediately obvious 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?

No guidance is provided on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description merely states what it does, leaving the agent to infer usage context without additional direction.

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 carry the full burden of disclosing behavioral traits. The description only mentions 'search results' and 'metadata' but does not clarify pagination, rate limits, result ordering, or any constraints. This minimal transparency leaves significant gaps for an agent relying on this description.

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 with no redundant information. It is succinct but could arguably include a few more useful details (e.g., pagination) without becoming overly verbose, settling just above the minimum viable standard.

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 absence of an output schema and annotations, the description is insufficiently complete. It does not outline what a response may contain beyond 'video titles and metadata,' nor does it mention pagination tokens (sp) or other behavioral aspects. The description feels underspecified for a tool with 4 parameters and no structured output metadata.

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 description coverage is 100%, with each parameter already explained (search_query, sp, country, language). The description adds no additional parameter-specific details beyond the schema, so the 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 clearly states the tool's function: 'Retrieve YouTube search results with video titles and metadata.' It uses a specific verb (retrieve) and a specific resource (YouTube search results), which distinguishes it from sibling tools like youtube_video or youtube_transcripts that target specific content types.

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 like google_search or youtube_transcripts. The description only states what the tool does, leaving it to the agent to infer appropriate use cases without explicit context 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?

With no annotations, the description carries full disclosure burden. It mentions the timestamps feature, but does not disclose important behavioral details such as whether the operation is read-only, what happens if no transcript is available, or any format requirements. This leaves significant gaps for an agent to anticipate 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 a single, clear sentence with no wasted words. It is front-loaded with the verb and resource, making it immediately understandable.

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, the description is mostly complete: it states the output (transcript) and a key feature (timestamps). However, with no output schema, a bit more detail on the response structure or potential errors would make it more complete. Still, it is adequate for basic usage.

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 descriptions cover 100% of parameters, so the baseline is 3. The description adds no additional parameter semantics beyond what the schema already provides, such as the format for video IDs or the purpose of country/language codes.

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

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

The description clearly states the tool's function: 'Get the transcript (with timestamps) for a YouTube video.' It uses a specific verb ('Get') and identifies a distinct resource (transcripts), which differentiates it from sibling tools like youtube_search or youtube_video.

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 (when you need a transcript), but does not explicitly state when to use this versus alternatives. There is no mention of exclusions or alternative tools, though the context of sibling tools makes the purpose fairly obvious.

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 carry the full burden. It only says 'Get details and metadata' without disclosing what fields are returned, error behavior, rate limits, or any special requirements. This leaves the tool's behavior as a black box.

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 conveys the essential purpose with no filler or redundant information.

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, low-complexity tool, the description plus well-documented schema is minimally viable. However, the absence of an output schema or any mention of return values means the agent cannot know what 'details and metadata' actually includes.

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 has 100% coverage with descriptive parameter explanations (e.g., v as 'YouTube video id', country as ISO code, language as code), and the description adds no extra parameter context. Baseline 3 is appropriate since schema does the heavy lifting.

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 'Get details and metadata for a single YouTube video' uses a specific verb and resource, clearly distinguishing this tool from siblings like youtube_search, youtube_transcripts, and youtube_channel by focusing on a single video's metadata.

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 when a known video ID is available and details are needed, providing clear context. However, it does not explicitly state when not to use it or mention alternative tools for search, transcripts, or channel info.

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