HasData

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

No annotations are provided, so the description carries the behavioral burden. It mentions pagination via nextPageToken and output fields, but lacks details on rate limits, data freshness, or any authentication requirements.

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

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

Two efficient sentences: first states the main function, second lists results and use cases. No wasted words, 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?

Given no output schema, the description reasonably covers inputs and outputs. Could elaborate on pagination details (e.g., how to obtain nextPageToken), but overall sufficient for a search tool.

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?

Input schema has 100% description coverage for all 8 parameters. The description adds context by grouping parameters (e.g., guest composition) and listing output fields, but does not significantly expand on parameter meaning beyond the schema.

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

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

The description clearly states it 'searches Airbnb for available stays by location and date range' and lists detailed return fields. It distinguishes itself from siblings like 'getAirbnbPropertyDetails' by focusing on listings 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?

Provides explicit use cases such as travel planning, market analysis, and pricing research. However, it does not mention when not to use or compare with the property details tool.

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 provided, so description carries full burden. It describes output but does not disclose side effects, authorization needs, rate limits, or whether it's read-only.

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?

Well-structured with a one-line summary, output list, and use cases. Slightly verbose but 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?

For a simple tool with one parameter and no output schema, the description covers purpose, output, and usage context. Missing behavioral info, but overall adequate.

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

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

Schema coverage is 100%, and the description only reiterates that the URL must be valid. Adds minimal 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 clearly states the tool fetches full Airbnb property details by listing URL, listing specific fields returned. It distinguishes from sibling tools by referencing the listing-search endpoint for discovery.

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

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

Explicitly lists use cases (travel planning, deep-dive research, enrichment, compliance audits) and mentions the listing-search endpoint as alternative. Could be more explicit about when not to 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?

No annotations are provided, so the description must cover behavioral aspects. It describes the tool's actions (fetching, returning fields) and conditional behavior for otherSellers and delivery pricing. However, it lacks details on error handling, rate limits, authentication needs, or response structure when outputs are missing, leaving gaps for a complete behavioral picture.

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 front-loaded with the tool's name and a concise action statement. It then lists returned fields and use cases efficiently. While slightly lengthy as a paragraph, each sentence serves a purpose; bullet points could improve scannability but are not required.

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

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

With no output schema, the description adequately covers key return fields (title, brand, price, etc.) and optional conditional outputs (other sellers, delivery-zone pricing). It explains input parameters and their effects, but the absence of exact response structure or field presence guarantees leaves minor gaps for an agent seeking complete clarity.

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%, but the description adds meaningful context beyond schema descriptions. It explains the role of otherSellers (triggers sellers block), deliveryZip and shippingLocation (affect pricing), and domain/language (cross-locale support). This enhances understanding beyond raw 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 tool's purpose: fetching product details by ASIN from a chosen Amazon domain. It lists specific fields returned (title, brand, price, etc.) and use cases (product research, monitoring, etc.), distinguishing it from sibling tools like search or reviews which serve different functions.

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 clear context for when to use the tool (e.g., product research, price monitoring, catalog enrichment). It implies when to use optional parameters like otherSellers=true, but does not explicitly mention when not to use or directly name alternatives among siblings, though sibling names suggest distinct purposes.

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 mentions 'Paginated fetch' and details the output fields, but does not disclose rate limits, authentication requirements, or potential failures (e.g., invalid ASIN). It implies read-only behavior but does not state it. The description adds some transparency but lacks completeness on non-functional aspects.

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 paragraph but is well-organized: purpose first, then filters, then output, then use cases. It is informative without redundancy. It could be slightly more structured (e.g., bullet points) but remains 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?

Given 10 parameters, no output schema, and no annotations, the description provides a comprehensive overview. It explains the paginated nature, all filter dimensions, the return fields (including title, body, star rating, etc.), and includes practical use cases. The agent has sufficient context to use this tool effectively.

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 10 parameters have descriptions in the input schema (100% coverage), so the schema already provides baseline meaning. The description adds value by grouping filters thematically and listing examples (e.g., 'star rating (1-5, positive, critical)') and the output fields. This enriches the semantic understanding 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 starts with a clear verb+resource: 'Get Amazon Product Reviews'. It distinguishes from sibling tools like getProductDetails or getSearchResults by focusing on reviews with pagination and filters. The purpose is unambiguous and specific.

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 lists explicit use cases (voice-of-customer analysis, sentiment extraction, etc.) and the various filter parameters imply when to use each. However, it does not explicitly state when not to use the tool or compare to alternatives, though the sibling list suggests other Amazon-specific tools exist.

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 available, so the description carries the full burden. It explains the return values (organic results, related suggestions, filter facets) and parameters (pagination, domain, zip, sort). However, it does not explicitly state that the operation is read-only or disclose any side effects, rate limits, or authentication needs.

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

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

The description is a single, dense paragraph that front-loads the primary action and then lists return details and use cases. It is not overly long and conveys all necessary information without redundancy. Slight improvement could be breaking into bullet points for readability, but it remains 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 complexity (7 parameters, no output schema), the description covers the tool's purpose, parameters, return fields, and use cases thoroughly. Minor gaps exist: it does not explain pagination mechanics (e.g., page numbering limits) or any rate limits. Still, it is above average in completeness.

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

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

Schema description coverage is 100%, so the baseline is high. The description adds context by grouping parameters (e.g., 'delivery zip/location scoping') and summarizing acceptable sort orders, but does not provide new details beyond what the schema already specifies. It also lists return fields not in the schema, which adds value.

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 it runs a keyword search on Amazon and returns organic search results with detailed fields (ASIN, title, price, etc.). This distinguishes it from sibling tools like getProductDetails (which retrieves a single product page) or getProductReviews.

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

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

The description explicitly lists use cases: SERP monitoring, keyword tracking, competitor discovery, ASIN harvesting, and building research agents. It implies when to use (search queries) but does not explicitly state when not to use or suggest alternatives, though the use cases are detailed.

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 burden. It accurately describes the returned data but does not mention potential issues like rate limits, authentication, or error handling. It implies read-only public access but lacks explicit behavioral traits beyond data content.

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 front-loaded with the main action and return fields, then lists use cases. It is somewhat verbose but well-structured, with no wasted sentences.

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

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

Given no output schema, the description compensates by listing return fields explicitly. However, it lacks details on error scenarios, request format, or edge cases. For a simple GET tool, it is fairly complete but could be more 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%, so baseline is 3. The description mentions parameters by context (sellerId, domain, language) but adds no additional semantic detail beyond what the input schema already provides.

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 it fetches the public storefront profile for an Amazon seller by sellerId, specifying the resource and action. It lists returned fields and explicitly distinguishes from sibling tools like hasdata_amazon_seller_products_getSellerProducts.

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 clear use cases (seller due-diligence, vetting, counterfeit workflows) but does not explicitly mention when not to use or name alternative tools. This is good guidance but lacks 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. It indicates this is a read operation (GET) and explains pagination behavior and return fields. However, it does not mention rate limits, authentication requirements, or error handling, which would be helpful 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 concise at around 100 words and front-loaded with the purpose. It covers key elements without unnecessary fluff. It could be slightly more structured (e.g., bullet points), but it is effective as a paragraph.

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 no output schema, the description adequately explains the return values (ASIN, title, price, etc.) and pagination. It is complete for typical usage. It lacks details on error states or edge cases, but the complexity is moderate and the coverage is satisfactory.

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

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

Schema coverage is 100%, so the baseline is 3. The description does not add significant parameter-level details beyond the schema, though it contextualizes the sellerId and domain. The description of return values is useful but does not directly enhance parameter understanding.

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 'Get Amazon Seller Products' and elaborates that it returns a paginated listing of a seller's catalog with specific fields. It distinguishes from sibling tools by mentioning competitor analysis and per-ASIN deep-dives, which are not covered by other Amazon tools like getProductDetails or getSearchResults.

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 lists concrete use cases: mapping competitor assortment, detecting new SKUs, brand protection, price intelligence. It also suggests follow-up actions (per-ASIN deep-dives). However, it does not explicitly state when not to use this tool or mention alternatives, so there is room for improvement.

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?

Discloses return structure (organic results, answer boxes, pagination) and key parameters (geo, filters, device). With no annotations, it adequately conveys read-only behavior and capabilities, though doesn't mention rate limits or error handling.

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

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

Two-sentence paragraph, front-loaded with action, efficiently covers purpose, parameters, returns, and use cases. No redundant or missing 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?

Given no output schema or annotations, the description compensates by listing return fields and pagination behavior. It covers major use cases but omits potential error scenarios; still sufficient for agent understanding.

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 11 params are described in schema (100% coverage). The description rephrases them in context but adds limited new semantic detail; 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 clearly states it fetches Bing SERPs, lists specific result types (organic, answer boxes, related searches), and outlines use cases (SEO rank tracking, SERP monitoring). It is distinctly a Bing tool among Google siblings, making 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?

Explicitly states when to use (SEO, Bing-specific audits), but lacks explicit when-not-to-use or direct comparison to Google SERP tools. The mention of 'Bing-specific' indirectly guides selection.

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

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

No annotations; description discloses basic data fetched but not behavioral traits like error handling or rate limits. Adequate for a simple read 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?

Two efficient paragraphs, front-loaded with purpose and data fields. No wasted words.

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

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

Covers inputs and outputs adequately for a tool with one parameter and no output schema. Lists returned fields to guide 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 covers the single parameter fully; description does not add extra meaning beyond what schema provides.

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?

Clearly states the tool fetches a specific Glassdoor job posting by URL and enumerates returned fields. Distinguishes from sibling listing 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?

Provides use cases but does not explicitly state when not to use or compare to alternatives like the listing tool.

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 describes the output structure (array of jobs with fields) and pagination via nextPageToken, but does not mention rate limits or auth requirements. For a read-only listing tool, this is sufficient but could be more explicit.

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 paragraph that front-loads the purpose and is reasonably concise. However, it could be slightly more terse without losing clarity.

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 (5 parameters, no output schema), the description adequately explains the output fields and pagination, providing sufficient context for an AI agent to use it correctly.

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

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

The input schema has 100% description coverage, so the schema already documents all parameters. The description adds value by explaining the purpose of parameters in context (e.g., sort options), but does not provide additional detail beyond the schema.

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

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

The description clearly states 'Get GlassDoor Job Listings' and explains it searches by keyword and location, distinguishing it from sibling tools like hasdata_glassdoor_job_getJobDetails and other listing 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 explicitly says when to use it: 'to build job feeds, monitor hiring trends, power candidate sourcing tools, and collect URLs for downstream full-detail scraping via the Glassdoor Job endpoint.' This provides clear guidance on its purpose and alternative 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 carry the full burden. It describes what the tool does but does not disclose behavioral traits such as rate limits, authentication requirements, or any side effects of scraping. This is a significant gap for a 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 two sentences: the first captures the core function and parameters, the second lists distinct use cases. Every sentence adds value, and it is front-loaded with essential 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?

Given no output schema, the description explains the return fields (title, URLs, dimensions, etc.) and mentions pagination via ijn. It covers the key aspects of the tool, though it lacks details on response format or pagination limits.

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

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

Schema description coverage is 100%, so the baseline is 3. The description paraphrases the filters and pagination but does not add substantial new meaning beyond what the schema already provides. It references tbs filters but does not explain them beyond the schema.

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

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

The description clearly states the tool scrapes Google Images for a query with advanced filters and lists the returned fields. It distinguishes itself from siblings by explicitly being an image search tool, unlike text or other media 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 provides explicit use cases such as visual-asset discovery, ML/CV dataset collection, and brand monitoring, giving clear context for when to use the tool. However, it does not mention when not to use it or explicitly call out alternatives.

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 burden. It discloses pagination with nextPageToken and lists returned fields (rating, text, date, place name, etc.), but does not mention rate limits, data freshness, authentication requirements, or how many results per page beyond what the num parameter 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 paragraph that starts with the purpose, then enumerates returned fields and use cases. It is front-loaded and efficient, though slightly verbose with the list of use cases.

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 no output schema, the description adequately covers what is returned (rating, text, date, place name, photos, owner responses) and mentions pagination. It lacks explicit total count or error handling but is sufficient for a 5-param tool.

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 clear descriptions for all 5 parameters. The description text reiterates 'language/country targeting and nextPageToken pagination' but does not add significant meaning 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 explicitly states 'Lists all Google Maps reviews authored by a specific Local Guide / contributor by contributorId', clearly distinguishing it from siblings like getMapReviews which likely fetches reviews for a place. It includes specific use cases such as reviewer reputation checks and fake review detection.

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?

Provides explicit use cases like 'reviewer reputation checks, detecting fake/bot review patterns, local-guide activity analysis, and building review-author profiles for trust scoring'. However, it does not explicitly state when not to use this tool or mention alternatives.

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?

The description details pagination via nextPageToken, filtering by categoryId with specific values, and the return structure (image URL, thumbnail, upload date, etc.). This clearly conveys behavior beyond the schema.

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

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

The description is a single concise paragraph that front-loads the main action. It includes necessary details without redundancy, though bullet points could enhance readability.

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 no output schema, the description fully explains the return data (URL, thumbnail, upload date, uploader, photoId) and covers pagination and filtering. All key aspects are addressed.

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 descriptions for each parameter. The description adds value by specifying categoryId filter options (all, latest, menu, etc.) and explaining pagination mechanics.

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

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

The description states 'Get Place Photos' and explains it fetches photo gallery by dataId or placeId. It clearly distinguishes from sibling tools like getPlaceDetails and getMapReviews, which serve different purposes.

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

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

Explicit use cases are listed, such as restaurant-menu extraction and venue visual audits. However, it does not explicitly mention when not to use or contrast with other photo-related 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 carries the full burden. It lists the data returned but does not discuss rate limits, permissions, data freshness, or any side effects. However, it is read-only and not misleading.

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 efficiently structured with a clear purpose first, then a list of returned fields, then use cases. It is concise but not overly terse.

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, the description compensates by listing all returned fields. It addresses core use cases, though it could mention error handling or data structure details.

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 context about optional localization but does not significantly extend the parameter meanings beyond the schema.

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

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

The description clearly states the verb ('Fetches') and resource ('full Google Maps place data by placeId'), and lists specific use cases like local SEO audits and POI enrichment, which effectively distinguishes it from sibling tools like 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 when to use (when you have a placeId) through the parameter explanation, but does not explicitly contrast with search or other tools, nor does it provide when-not-to-use 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 the burden. It details the return fields (author, rating, text, etc.) and mentions pagination. It does not discuss rate limits or authentication, but as a read-only fetch, the disclosure is adequate.

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 well-structured with a clear title, a paragraph on behavior, a list of return fields, and use cases. It is concise enough for the complexity and front-loads the purpose, though the first line repeats the title.

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 6 parameters and no output schema, the description covers the tool's purpose, parameters, return fields, and use cases. It lacks explicit mention of the difference from the contributor reviews sibling, but overall is fairly 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 coverage is 100%, so baseline is 3. The description adds value by explaining how sort options work (e.g., qualityScore), the use of topicId filter, and pagination token. It also groups parameters like dataId/placeId as alternatives.

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 Google Maps reviews for a place by dataId or placeId, with pagination, sorting, and filtering options. It distinguishes from sibling tools (e.g., contributor reviews) by focusing on place reviews and listing specific features.

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 explicit use cases such as reputation management, sentiment mining, competitor benchmarking, and feeding LLMs. It does not directly compare to the sibling contributor reviews tool but implies when to use this via the listed 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, the description carries full disclosure responsibility. It describes the return format (local pack list with fields) and pagination via 'start'. It does not mention rate limits or authentication, but is transparent about what the tool does and returns. No contradictions.

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 concise with a clear title and a single paragraph. It front-loads the purpose and uses bullet-like listing in the last sentence. Every sentence adds value without unnecessary verbosity.

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

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

Given 6 parameters and no output schema, the description is fairly complete, listing return fields and use cases. However, it does not specify the structure of the output (e.g., array or object) or error handling, which is a notable gap for agent invocation.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds context about 'optional GPS coordinates' for `ll` and 'offset-based pagination' for `start`, but these are already in the schema. It does not significantly enhance meaning beyond the schema definitions.

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 'Get Google Maps Search Results' and specifies it runs a search by keyword plus optional GPS coordinates. It lists the return fields and distinguishes itself from sibling tools like place details, reviews, and photos endpoints, 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?

The description explicitly lists use cases such as local lead generation, competitor density mapping, market expansion research, and feeding placeIds into other endpoints. While it doesn't explicitly state when not to use it, the mention of alternatives (e.g., Maps Place, Reviews, Photos endpoints) provides implicit 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 must fully cover behavioral traits. It describes the return structure but does not mention auth requirements, rate limits, data freshness, idempotency, or safety (e.g., non-destructive nature). For a read operation, these are notable omissions.

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 paragraph that front-loads purpose and return components, then lists use cases. While informative, it could be more structured (e.g., bullet points) for easier scanning. Overall, it is concise given the amount of 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?

With no output schema, the description adequately explains return values (conversational text, source links, subtopics, follow-ups, token). It covers the main output components but lacks details on error handling, pagination, or edge cases. Given the tool's complexity and 7 parameters, it is fairly 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 some context (e.g., subsequentRequestToken for multi-turn continuation, use of q for person queries) but does not significantly enhance understanding beyond the schema. It covers all parameters implicitly but not explicitly.

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 purpose: 'Get AI Mode SERP Results' capturing Gemini-powered AI Mode answers. It details specific return components (conversational response text, cited source links, subtopic breakdowns, follow-up suggestions, subsequentRequestToken) and provides concrete examples, clearly distinguishing it from sibling tools like standard Google Search or AI Overview.

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 extensive use cases (e.g., next-gen search interfaces, AI-answer monitoring, citation tracking, person/company data enrichment) and examples (e.g., 'Who is the CEO of HasData?'). It implicitly suggests when to use this tool over others, but lacks explicit 'when not to use' guidance or comparison with alternative 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?

Discloses token validity of 4 minutes and describes return fields (answer text, sources, subtopic sections). No annotations provided, so description carries burden; lacks details on error handling or rate limits.

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

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

Three sentences, front-loaded with name and immediate purpose. Every sentence adds value with zero 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?

Describes what the tool returns explicitly. Lacks mention of error behavior (e.g., expired token) but for a simple fetch tool with one parameter, it is sufficient without output schema.

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

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

Schema covers 100% of parameters with description. The description adds context: token source ('from `aiOverview` block') and validity period, enhancing schema info.

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 Google AI Overview results via a pageToken. It distinguishes from sibling Google SERP tools by noting it's a follow-up call and specifying SEO/LLM use cases.

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?

Explains it is a follow-up to Google SERP API and lists use cases (SEO, fact-checking, LLM pipelines). Does not explicitly mention when not to use, but context is clear.

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

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

With no annotations, the description carries full burden. It explains the tool scrapes the Google Events vertical, returns specific fields (title, date, venue, links, description, thumbnail), and mentions filters (date, virtual-event, domain/country/language, pagination). However, it does not disclose rate limits, authentication requirements, or whether it is read-only. The description is decent but lacks deeper behavioral context.

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

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

The description is four sentences, front-loaded with the title and a comprehensive overview of capabilities. It could be more concise by separating use cases, but it remains efficient and informative without unnecessary fluff.

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 no output schema, the description explicitly lists the returned fields (title, start date/time, venue, links, description, thumbnail), which is helpful. It covers key parameters and pagination. However, it omits potential limitations like result count or response size. Overall, it is fairly complete for a scraping tool.

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 baseline is 3. The description briefly summarizes the filters (date, virtual-event toggle, domain/country/language targeting, pagination) but adds little beyond the schema's detailed parameter descriptions (e.g., `htichips` already lists all filter options). The description does not provide new semantic value that the schema lacks.

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 Google Events results with specific filters, distinguishing it from sibling tools like general search, news, or shopping tools. The verb 'Get' and resource 'Google Events Results' are specific and unambiguous.

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

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

The description lists explicit use cases: event-discovery chatbots, local aggregators, calendar sync, competitive monitoring, and pulling upcoming shows/conferences. It provides context for when to use this tool, though it does not explicitly state when not to use it or mention alternatives. However, given sibling tools, the purpose is clear.

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

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

With no annotations, the description adequately explains the behavior: it is a read-only operation that returns product details and paginated store offers. It specifies optional parameters like moreStores and nextPageToken, but lacks info on authentication or rate limits.

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 concise, front-loaded with the core purpose, and uses a single effective paragraph. Every sentence adds value without 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?

Given no output schema, the description adequately covers return data (multi-store offers, specs, images, ratings, nextPageToken). It fully addresses the tool's complexity with 3 parameters and clear use cases.

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

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

Schema coverage is 100%, and the description adds context by explaining the token source, the effect of moreStores (up to ~13 merchants vs 3-5), and nextPageToken for pagination. This enhances understanding beyond the schema.

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

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

The description clearly states it expands the Google Shopping Immersive Product pop-up, specifying the required token and return data. It distinguishes itself from siblings like product search tools by focusing on immersive product information with multi-store 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?

Explicit use cases are listed (price-comparison bots, merchant discovery, dropshipping research, aggregating offer lists), providing clear guidance. However, it does not mention when not to use or directly compare to siblings.

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

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

No annotations are provided, so the description carries the full burden. It describes the return fields (title, snippet, source, date, thumbnail, URL) and navigation tokens. It is transparent about the read-only nature, though it does not mention rate limits or pagination. No contradictions.

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 concise (5 sentences) and front-loaded with purpose. Each sentence adds value, explaining tokens and use cases without redundancy. Efficient 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 tool with 8 parameters and no output schema, the description covers the main use cases (news monitoring, brand tracking, aggregators, etc.) and explains the tokens. It omits pagination details but is otherwise complete for agent decision-making.

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?

With 100% schema description coverage, baseline is 3. The description adds value by explaining how parameters interact (e.g., sectionToken only with topicToken or publicationToken). This goes beyond the schema's individual descriptions, aiding the agent in correct usage.

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 'Get Google News Results' and elaborates on retrieval via free-text query or various tokens (topic, section, publication, story). It distinguishes this tool from sibling tools like general web search, images, shopping, etc., by specifying it's for Google News.

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

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

The description provides guidance on when to use each parameter and their restrictions (e.g., 'Not allowed with q, storyToken, or publicationToken'). It implies use for news-specific queries but does not explicitly contrast with sibling tools. Adequate for an informed agent.

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

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

No annotations are provided, so the description carries the full burden. It describes the data returned (title, images, price, ratings, etc.) and the parameters that affect behavior, but does not mention rate limits, authorization, or idempotency. The description is adequate for a read-only 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 long, front-loading the purpose and then elaborating on capabilities. It is informative without verbosity, though the second sentence is somewhat dense.

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

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

No output schema exists, but the description mentions key return fields (title, images, price, etc.). It covers all major aspects of the tool's functionality given its complexity, though it could be slightly more detailed on filter combinations.

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% (baseline 3). The description adds value by explaining how searchType and filter parameters work, listing example filters and their effects, which goes beyond the schema's enum descriptions.

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

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

The description clearly states the tool retrieves detailed product data from Google Shopping by productId, with searchType and filters. It distinguishes itself from sibling tools like general search or shopping search by focusing on product information for a specific ID.

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 explicit use cases ('price intelligence, catalog enrichment, review mining, competitor spec comparison') but does not explicitly state when not to use or mention alternative tools among siblings.

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

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

No annotations are provided, so the description carries the full burden. It details the return includes organic results, ads, knowledge graph, and other rich features. However, it omits behavioral details like authentication, rate limits, or cost.

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 verbose, listing many capabilities and use cases. While well-organized, it could be more concise to improve readability. The front-loading of the purpose is good, but the bulk of text may overwhelm an AI agent.

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 19 parameters and no output schema, the description compensates by describing return features and many use cases. However, it lacks explanations of parameter interactions (e.g., location vs uule) and does not fully cover output format details.

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

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

Schema coverage is 100%, so baseline is 3. The description adds extra context for parameters like tbs (with examples), start (pagination note), and use cases for person/company enrichment, enhancing understanding beyond the schema.

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

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

The description clearly states the tool's purpose as 'Get Google Search Results' and elaborates with a comprehensive list of capabilities, including organic results, ads, knowledge graph, and more. It distinguishes itself from siblings like the 'light' version and other specialized SERP 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 provides explicit use cases such as SEO rank tracking, keyword research, and person/company data enrichment. However, it does not explicitly state when not to use this tool or directly compare to sibling tools like the light version or news-specific tool.

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 provided, so description carries full burden. It explains the lightweight nature, what is omitted, and lists supported filters (location, country, language, domain, safesearch, time/date, pagination). It does not mention rate limits or auth but covers key behavioral traits for a scraper 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 concise paragraphs with no wasted words. The first paragraph explains what the tool does and what it omits; the second lists use cases. It is front-loaded with the core 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 no output schema, the description covers what is returned: 'title, link, snippet, and position per result.' It also explains filtering and pagination behavior. It does not document error handling or quotas, but for a lightweight scraper with 12 params (1 required), it is reasonably 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 coverage is 100% (all 12 parameters have descriptions in the schema). The description does not add significant new meaning beyond what the schema provides; it summarizes categories of filters (location/uule, country, language, etc.) but the schema already describes each param individually. 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 clearly states the tool 'Get Google Light Search Results' and specifies that it returns only organic results and basic pagination, omitting rich SERP blocks like AI Overview, knowledge graph, PAA. This differentiates it from the sibling tool 'hasdata_google_serp_serp_getSearchResults' which presumably returns full SERP.

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

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

The description explicitly says to use this tool 'for high-volume keyword monitoring, bulk rank tracking, backlink discovery, and any workflow where only the ten blue links matter.' This implies when to use it and when not to (when rich results are needed). It does not explicitly name alternative tools but context signals include a full SERP sibling.

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 explains the return data and filter mechanics, but omits details like authentication, rate limits, or error conditions. The behavioral disclosure is adequate but not comprehensive.

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 paragraph that front-loads the purpose, then details parameters and use cases. Every sentence provides meaningful information without 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?

With 10 parameters and no output schema, the description covers the key return fields and filter usage, but lacks detailed response structure (e.g., types, nesting) and pagination handling beyond the `start` parameter. It is sufficient for basic use but not fully complete.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the `shoprs` parameter with filter chip usage and `hasdata_link` navigation, and by providing examples for `tbs`. This extra context justifies a higher 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 identifies the tool as retrieving Google Shopping search results for a query, specifies the resource (Google Shopping listings), and details the return fields and use cases. It distinguishes from siblings like general web search tools by focusing specifically on shopping data.

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

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

The description lists use cases (price tracking, catalog building, etc.) and mentions feeding data into Product APIs, but it does not explicitly state when to use this tool versus alternative sibling tools (e.g., general SERP or product info tools). The guidance is implied 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 describes what is returned (title, thumbnail, etc.) but lacks details on rate limits, authentication, data freshness, and pagination behavior beyond 'page-based pagination'.

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 front-loaded with the purpose and uses a single paragraph for details. It efficiently lists use cases without fluff, though it could be more 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?

With 9 parameters and no output schema, the description explains return fields and use cases. However, it does not specify pagination details or response structure, leaving some gaps for completeness.

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

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

Schema description coverage is 100%, so all parameters are documented in the schema. The description adds no additional parameter semantics beyond what the schema already provides, resulting in a 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 clearly states 'Get Short Videos Search Results' and explains it scrapes the Google Short Videos carousel for specific platforms, distinguishing it from general SERP tools and other sibling 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 provides specific use cases like short-form content discovery, viral-trend monitoring, and influencer research, but does not explicitly state when not to use it or mention alternative 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?

The description mentions that deepSearch takes longer but does not disclose other behavioral traits like rate limits, authentication needs, or side effects. Without annotations, the description carries full burden; it covers basic behavior 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 front-loaded with a clear purpose and flows into parameter details. It is somewhat verbose but efficiently covers all key aspects without 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?

Given the complexity (31 parameters, no output schema), the description explains input parameters and return values comprehensively, including price, legs, CO2 emissions, and tokens. It is complete for an agent to understand inputs and outputs.

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 baseline is 3. The description adds context by listing parameter types in prose (e.g., 'passenger mix, travel class, bags, max price, sort order'), enhancing understanding beyond the schema definitions.

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 'Get Google Flights Results' and elaborates on specific flight search types and parameters. It distinguishes itself from sibling tools as the only flight-related tool, making 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 provides explicit use cases such as 'travel-planning agents, fare monitoring, corporate travel dashboards, emission-aware trip optimization' but does not specify when not to use or mention alternative tools, though none exist among siblings.

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

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. While it states the tool 'Pulls' data (implying a read-only operation), it does not specify authentication requirements, rate limits, idempotency, or data freshness. The description is insufficient 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 two paragraphs: the first explains what the tool does, the second lists use cases. It is front-loaded with the core purpose. It could be slightly more concise, but all sentences are 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?

Given the tool's complexity (8 parameters, no output schema), the description covers the main functionality and return types (interest-over-time, geo-map, related topics/queries). However, it does not explain the output structure in detail, lacks pagination or limit information, and does not address how multiple queries affect results. It is adequate but not 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 baseline is 3. The description adds some value by grouping parameters and noting that dataType affects query count (single vs multiple queries), but it does not significantly augment the already detailed schema descriptions for parameters like tz, cat, geo, and date.

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 'Get Google Trends Data' and enumerates the specific capabilities (geo targeting, region granularity, date range, category, time zone, Google property, dataType). It distinguishes this tool from siblings, all of which are for other platforms (e.g., Amazon, Airbnb).

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 a clear list of use cases ('keyword/content strategy, demand forecasting, seasonality analysis, topic discovery, campaign timing, and adding live-trend signals') which implies when to use. Since there is no sibling tool for Google Trends, explicit exclusions are unnecessary, but the description lacks guidance on limitations or prerequisites.

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 provided. The description does not disclose any behavioral traits beyond functionality (e.g., rate limits, authentication, read-only nature). It lists returned fields, providing some transparency, but lacks detail on constraints or side effects.

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?

Description is a clear paragraph with front-loaded purpose statement. It is slightly wordy but contains no redundancy. Could be shortened without losing 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?

For a simple single-parameter fetch tool, the description lists all returned fields and use cases. No output schema exists, but the field list compensates. Missing details like URL format or error handling, but overall adequate.

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% (one parameter 'url' with description). The description reiterates that the URL is for the job vacancy but adds no additional semantics or format hints beyond what the schema provides.

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?

Clearly states the tool fetches a single Indeed job posting by viewjob URL and lists specific return fields (title, company, location, salary, etc.). Distinguishes itself from the sibling listing tool by specifying 'single job posting' and referencing the 'Indeed Listing endpoint'.

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?

Describes use cases such as ATS/CRM enrichment and compensation benchmarking. However, does not explicitly state when not to use it or mention alternatives beyond the listing endpoint. The context is implied from the single-URL input.

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 describes the return structure (array with specific fields) and pagination via the 'start' parameter. However, it does not disclose any behavioral traits like rate limits, authentication requirements, or side effects.

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 front-loaded with the tool's name and purpose, then provides a concise paragraph. It is structured and each sentence adds relevant information without being overly verbose.

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 no output schema, the description compensates by listing returned fields (title, company, location, salary, etc.) and explaining pagination. It covers key use cases and parameters adequately for a search endpoint.

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 baseline is 3. The description adds minimal extra value beyond the schema, briefly explaining the purpose of sort, start, and domain but not providing new semantic details.

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 it returns Indeed job listings with search by keyword and location, and mentions sorting and pagination. It distinguishes from sibling tool getJobDetails by specifying it returns a list rather than 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 provides explicit use cases (e.g., job-market dashboards, hiring-trend analysis) and mentions generating URL lists for the Indeed Job endpoint. It implies when to use this tool vs. getJobDetails but lacks explicit exclusion 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?

Although no annotations are provided, the description clearly indicates it fetches a public profile and lists returned fields. It does not mention rate limits or errors, but for a read-only tool, the transparency is adequate.

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 concise, with two short paragraphs front-loading the purpose and then listing use cases. Every sentence adds value without 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?

Despite lacking an output schema, the description enumerates all returned fields and provides practical use cases, making it complete for this simple tool with one parameter.

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 the handle parameter well-defined. The description only restates 'by username (handle)' without adding significant new information beyond the schema.

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

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

The description clearly states the tool fetches a public Instagram profile by username, lists specific data returned (full name, biography, etc.), and is distinct from sibling tools which cover other platforms like Airbnb, Amazon, and Google.

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 explicit use cases such as enriching CRM records, verifying influencer reach, monitoring competitors, and building datasets, offering clear guidance on when to use this tool.

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 disclose behavior. It mentions pagination and the types of listings but does not cover aspects like rate limits, authentication, data freshness, or whether it performs web scraping. The lack of behavioral details is a gap.

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

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

The description is moderately concise, starting with the core purpose and expanding with return fields and use cases. It could be slightly shorter but every sentence contributes value.

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 (3 parameters, no output schema), the description lists return fields comprehensively and explains use cases. It does not specify output structure, but it is adequate for the stated purposes.

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 descriptions for all three parameters. The description adds context (e.g., zipcode, pagination) but does not provide additional semantics beyond the schema definitions.

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 retrieves Redfin real estate listings, specifies the search parameters (zipcode, listing type, pagination), and lists the return fields. It distinguishes from the sibling hasdata_redfin_property_getPropertyDetails by mentioning deeper detail retrieval.

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 explicit use cases (market research, lead generation, trend analysis) and suggests feeding URLs into the Redfin Property endpoint for deeper details. However, it does not explicitly exclude scenarios or compare with other listing tools like Zillow.

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 states the tool is a read operation (fetches) and lists output fields, but does not disclose potential error conditions, authentication needs, rate limits, or any side effects. Adequate but not thorough.

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 paragraph that front-loads the purpose and uses a bullet-like list for data fields. It is concise and avoids unnecessary words, though it could be more 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?

Given the tool has one simple parameter and no output schema, the description covers the main purpose, input, and output comprehensively. It lacks details on error handling or limitations, but for a straightforward tool it is quite 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 coverage is 100% with a clear description of the 'url' parameter. The description adds the context that it fetches by URL, but does not significantly extend beyond the schema description. 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 clearly states it fetches detailed Redfin property data by URL, listing many specific fields. It distinguishes from sibling tools like hasdata_redfin_listing_getRealEstateListings (which likely searches/listings) and other property detail tools from Zillow, etc.

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 includes explicit use cases (CMA reports, due-diligence, valuation models, etc.) which inform when to use. However, it does not explicitly state when not to use or compare with alternatives, though the context is clear.

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

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

With no annotations, the description carries full burden. It discloses the operation is read-only ('Lists collections'), describes pagination (limit, page), and mentions returned fields. However, it does not address rate limits, authentication requirements, or error handling, which is adequate but not exceptional.

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 concise with two clear paragraphs. The first states the core action and parameters; the second provides usage guidance. No superfluous sentences, every part adds value.

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 no output schema, the description fully lists returned fields (id, title, handle, etc.) and links to the sibling tool for products. It covers pagination, parameters, and use cases comprehensively for a simple listing tool.

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 marginal value beyond the schema (e.g., 'up to 250' for limit), but does not significantly enhance understanding. 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 explicitly states 'Lists collections from any public Shopify storefront URL', using a specific verb and resource. It also distinguishes from sibling tools by noting that returned handles can be used for the Shopify Products endpoint, providing clear differentiation.

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

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

The description provides explicit use cases: 'Use the returned handles as input to the Shopify Products endpoint... or to map a competitor's merchandising taxonomy'. It clearly indicates when to use the tool, though it does not explicitly state when not to use it.

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

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

With no annotations, the description carries the burden. It describes the return fields, pagination (page/limit up to 250), and optional collection filter. It is transparent about being a read operation on public URLs, but lacks details on rate limits or error handling.

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 well-structured with a clear first sentence stating the purpose, followed by details. It is slightly verbose but not excessively so, and all sentences add value.

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 no output schema, the description lists output fields and explains pagination, filter, and use cases. It is adequately complete for a data retrieval tool.

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. The description adds context like 'up to 250' for limit but mainly repeats schema info. It provides use case context but no new parameter details beyond what the schema already explains.

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 'Get Shopify Store Products' and explains pulling products from public Shopify store URLs with optional filters. However, it does not explicitly differentiate from the sibling tool `hasdata_shopify_collections_getCollections`.

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 lists several use cases (competitive price monitoring, catalog mirroring, etc.) but does not provide explicit guidance on when to use this tool versus alternatives or when not to use it.

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

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

With no annotations, the description carries full burden. It discloses key behaviors: proxy usage, JS rendering, resource blocking, screenshots, output formats, and data extraction. However, it omits potential side effects like rate limits or authentication needs.

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

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

The description is a single dense paragraph that efficiently conveys comprehensive information. It could be more structured (e.g., bullet points) for easier parsing, but it remains relatively concise given the tool's complexity.

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

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

The description covers main use cases and output types but lacks details on error handling or limits. Given the tool's 20 parameters and advanced features, it provides sufficient context for an AI agent.

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

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

Schema coverage is 100%, so baseline is 3. The description provides a high-level overview and some extra context for complex parameters like jsScenario and aiExtractRules, but does not significantly enhance per-parameter understanding beyond the schema.

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

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

The description clearly states the tool is a 'Universal web scraper' and explicitly differentiates it from sibling tools by recommending it as a fallback for sites without a dedicated API.

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 specifies use cases such as fallback for sites without an API, JS-heavy SPAs, bypassing bot protections, and capturing screenshots. It implicitly suggests using dedicated scrapers when available, but lacks explicit 'when not to use' 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, the description carries the full burden. It states the tool 'scrapes' data, implying read-only behavior, and lists the data returned. However, it does not disclose potential issues like rate limits, authentication requirements, or whether the scraping might be blocked. The description is adequate but lacks deeper behavioral context.

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

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

The description is concise and front-loaded with the tool's name and action. It uses two short paragraphs: first listing the returned fields, then providing use cases. Every sentence adds value, and there is no redundant or extraneous information. Excellent structure for quick comprehension.

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 a single parameter and no output schema, the description reasonably covers the tool's purpose, input, and output by listing many returned fields. It also explains when to use it. However, it could be slightly more specific about the output format (e.g., structure of the response) but is sufficiently complete for 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 input schema has 100% coverage with one parameter 'url' described as 'The YellowPages URL of the place.' The description adds minimal additional meaning beyond confirming it is a single URL. Since the schema already documents the parameter, the description provides no extra semantic value, warranting a 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 clearly states it 'Get Yellow Pages Place Details' and specifies it scrapes a single business listing URL, returning a comprehensive set of fields. This distinguishes it from the sibling 'hasdata_yellowpages_search_getSearchResults' which presumably collects URLs, making the tool's specific 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 provides explicit use cases such as hydrating leads, building a B2B database from URLs collected via the Search endpoint, and validating business legitimacy. It implies when to use (after obtaining a URL from Search) but does not explicitly state when not to use or mention alternatives. The guidance is clear and useful.

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, description must disclose behavior. It states search scope, return fields, sort options, domain targeting, and page-based pagination. It lacks details on rate limits, auth requirements, or result count per page, but provides adequate transparency for a typical search 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?

Two clear sentences with a use case sentence appended. Front-loaded with purpose. No fluff, though the use case sentence could be integrated to be more 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?

For a search tool with no output schema, the description lists key return fields (name, URL, phone, etc.) and mentions pagination. It adequately covers the core functionality and ties to the sibling place endpoint for enrichment.

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

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

Schema coverage is 100%, so baseline is 3. Description adds context for sort enum, domain default, and page pagination, but does not add significant new meaning 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?

Description clearly states it runs a YellowPages business search by keyword and location, and lists return fields. It explicitly distinguishes from the sibling `hasdata_yellowpages_place_getPlaceDetails` by mentioning feeding URLs into that endpoint for enrichment.

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?

Provides explicit use cases like B2B lead generation and geo-targeted list building. However, it does not specify when not to use or mention alternatives beyond the sibling place endpoint.

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 provided, but description correctly implies a read-only operation ('fetches'). Lists return fields for transparency. Lacks details on error handling or rate limits, but is adequate for a simple fetch.

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?

Very concise, well-structured, front-loaded with main action, and no unnecessary words. Every sentence serves a 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?

No output schema, but description lists all major return fields. For a single-entity fetch, it is sufficiently complete. Could mention response format but not necessary.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining placeId can be ID or alias with examples, and domain includes default mention, adding context beyond the schema.

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

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

The description clearly states it fetches a single Yelp business by ID or alias with domain targeting. It distinguishes from search tools by specifying 'single' and listing detailed return fields.

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?

Provides explicit use cases like enriching leads and monitoring competitors. Does not explicitly state when not to use or mention alternatives like the sibling search tool, but the context is clear enough.

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, description carries full burden. It details return fields, pagination, and map radius behavior. Missing information on rate limits or authentication, but for a search tool, it is reasonably transparent.

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

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

Concise, well-structured description: first sentence states purpose, subsequent sentences add detail on parameters and use cases. No wasted words; every sentence contributes.

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 no output schema, description covers returned fields and pagination. It provides sufficient context for an agent to use the tool effectively, though could mention result limits or data freshness.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining the 'l' parameter format (g:lon1,lat1,lon2,lat2), offset pagination for 'start', and domain targeting. Enhances understanding beyond 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?

Clearly states the tool retrieves Yelp business search results by keyword and location, distinguishing it from sibling tools like hasdata_yelp_place_getPlaceDetails. Uses specific verb 'Get' and enumerates use cases.

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?

Explains when to use the tool (search by keyword/location, pagination, map radius) and suggests using returned aliases for the Place endpoint. Lacks explicit when-not-to-use or alternatives, but provides solid contextual 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, the description must cover behavioral traits. It mentions pagination (page parameter) and return fields but omits rate limits, authentication requirements, or any side effects. Adequate but not fully transparent.

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

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

Well-structured with a clear title and summary. Information is front-loaded. Slightly verbose with enumeration of filters, but overall concise enough for the complexity of the tool.

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 33 parameters and no output schema, the description covers the return fields and typical use cases. Lacks details on pagination metadata or total results, but sufficient for many agents.

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

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

Schema coverage is 100%, so baseline 3. The description adds value by summarizing filter categories (price, beds, etc.) and mentioning pagination, helping agents understand available options beyond schema descriptions.

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

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

The description clearly states it retrieves Zillow real estate listings and specifies the search and filter capabilities. It lists return fields but does not distinguish from similar sibling tools like Redfin listings, missing a clear differentiation.

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

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

The description provides usage examples (dashboards, agent leads, etc.) but does not indicate when not to use it or contrast with alternatives. No explicit exclusion criteria or guidance on choosing between Zillow and Redfin.

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?

Discloses optional behavior (agent email extraction) and cost implication, but no annotations provided; description lacks details on rate limits, authentication, or side effects.

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?

Concise, no wasted words. Structured with a brief summary followed by detailed output list. Could be slightly more 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?

Lists major return fields compensating for lack of output schema. Could mention response format (structured data) but sufficient for an agent to understand data returned.

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?

Adds meaning beyond schema: explains email extraction increases cost. Schema coverage is 100%, so description adds useful context, especially for the optional 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?

Clearly states it fetches Zillow property details by URL/zpid, listing returned fields. Distinct from sibling listing tools by focusing on a single property page.

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?

Mentions specific use cases like valuation models, CMA generation, and investor underwriting, providing context for when to use. However, does not explicitly exclude use for listings or compare to sibling tools.

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