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

With no annotations provided, the description carries full disclosure burden. It transparently notes that response field names are inconsistent between sample and code examples, and captures both in 'response_summary'. Also clarifies that credits are not applicable. This reveals important behavioral quirks beyond basic functionality.

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 and not front-loaded. It starts with purpose but then dives into detailed notes about documentation inconsistencies, which could be summarized more briefly. Every sentence does not earn its place; extraneous details clutter the message.

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 details return fields (though inconsistently). For a simple status-check tool with zero parameters, it covers essential aspects, but the lengthy digression on documentation conflicts may confuse rather than clarify.

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?

No parameters exist, so the baseline is 4. The description does not need to add parameter details. It instead provides useful information about the response structure, adding value beyond the empty 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 monitors Scrapingdog account usage, specifically remaining API credits and active concurrent connections. This verb+resource combination is unique among sibling tools (primarily scrapers), leaving no ambiguity about its purpose.

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

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

No explicit guidance on when to use this tool vs alternatives. However, among siblings with different scraping functions, the context strongly implies it's for account status checks. Still, lacking when-not or alternative recommendations.

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 discloses credit cost and clarifies parameter behavior (prefix effectively required), but does not mention read-only nature or rate limits. Overall, it provides useful behavioral context beyond basic purpose.

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

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

The description is concise and front-loaded with purpose. It includes necessary details about credits and return format without unnecessary verbosity. A slight amount of extra text could be trimmed, but overall efficient.

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

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

Given that there is no output schema, the description explains the return format (flat array of suggestion objects). All parameters are covered both in schema and description, and the effective requirement for prefix is noted. Fairly complete for a tool of this complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that prefix is effectively required, describing the return format, and providing context for parameters like last_prefix and suffix. This goes 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?

The description clearly states it retrieves keyword suggestions from Amazon's autocomplete feature based on partial search terms. It uses specific verbs and resources, and is distinct from sibling tools like amazon_search and google_autocomplete.

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 mentions it's useful for keyword research and search-driven features, and notes that prefix is effectively required. It implies when to use but does not explicitly state when not to use or name alternatives, though context from sibling tools makes it 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, description carries full burden. Discloses no pagination and returns all active offers, also provides return structure. However, lacks disclosure of whether tool is read-only, auth needs, rate limits, or other behavioral traits.

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 relatively concise, front-loading purpose. Includes helpful notes on credits and pagination with return structure example. A few extra details could be trimmed but overall efficient.

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

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

Given absence of output schema, description compensates by providing detailed return structure. Covers key aspects like no pagination and credit note. Adequate for a single-resource tool with 4 parameters.

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

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

Schema description coverage is 100%, so baseline 3. Description does not add much beyond schema for parameters; it mentions 'for a given ASIN' but schema already describes asin. No additional semantic enrichment for parameters.

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

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

Description uses specific verb 'Retrieves detailed offer data for a given ASIN' combined with resource 'ASIN' and includes key aspects like pricing, availability, seller details, delivery. Clearly distinguishes from sibling tools like amazon_product (product details) and amazon_reviews (reviews).

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?

Implies usage when needing offer data for a specific ASIN, mentions no pagination and returns all active offers, but does not explicitly state when to use vs alternatives or 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?

With no annotations provided, the description carries full burden. It discloses credit costs (1 vs 5 credits), explains domain/country independence, notes no pagination, and outlines return fields. Missing details like error handling or rate limits but adequate for a 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?

The description is well-structured with a main purpose sentence, then notes on credits and domain/country, and a return structure summary. It is informative without being overly verbose, though slightly long due to the return format details.

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 5 parameters, no annotations, and no output schema, the description provides significant context: return structure (partial), credit costs, domain/country semantics, and no pagination. It could be more complete with error handling but covers the essentials for a product lookup.

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 is 3. The description adds value by explaining the domain/country interaction and credit cost implications, which go beyond the schema descriptions. This extra context justifies a score of 4.

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 comprehensive product data from any Amazon product page using its ASIN. This specific verb+resource+identifier distinguishes it from sibling tools like amazon_offers, amazon_reviews, and amazon_search which focus on specific subsets of 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 provides clear context on when to use this tool (single product lookup) and explains the relationship between domain and country parameters. However, it does not explicitly contrast with sibling tools or state when not to use it, leaving 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?

With no annotations, the description takes responsibility for behavioral disclosure. It mentions credit cost (5 API credits per request), pagination, concurrency recommendation, and return format. Missing details on error handling or input validation, but overall 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 efficient: 4-5 sentences, front-loaded with purpose, followed by important notes. No redundant or irrelevant 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 9 parameters and no output schema, the description provides a return format example, which adds completeness. It covers key aspects like pagination, concurrency, and input alternatives but lacks details on potential errors or 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 coverage is 100%, so the description adds minimal value beyond what is in the schema. It groups parameters (asin+domain+page) and notes the url shortcut, but does not elaborate on each parameter's meaning. Baseline score 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 scrapes customer reviews from Amazon product pages, with explicit filtering options. It distinguishes itself from sibling tools that target other platforms (e.g., google_maps_reviews, walmart_reviews) by specifying Amazon.

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 clear usage guidance: pagination via page parameter (starts at 1), single concurrency recommendation, and alternative ways to provide input (asin+domain+page vs url). However, it does not explicitly differentiate from other review scraping tools or state 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?

Since no annotations are provided, the description carries the full burden. It discloses credit costs per country and premium, and explains pagination behavior (page parameter and informational pagination array). It does not mention rate limits, error handling, or side effects, but for a read-only search tool, these are less critical.

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 main sentence, credit details in brackets, pagination note, and return format. It is slightly verbose but front-loaded with the primary purpose, making it effective.

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 thoroughly details the return structure with example fields. It covers all necessary aspects of the tool's functionality, including multiple parameters and their effects, making it complete for an agent to 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 coverage is 100%, with each parameter documented. The description adds value beyond the schema by explaining credit cost implications for country and premium, and clarifying that the page parameter starts at 1 and the pagination array is informational.

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 search result listings from Amazon for any query, listing specific data points (titles, prices, ratings, etc.) and pagination links. It distinguishes itself from sibling tools like amazon_product, amazon_reviews, etc., which focus on single product details or reviews.

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 credit costs and pagination notes but does not explicitly state when to use this tool versus alternatives. The context of sibling tools implies usage, but the description lacks direct guidance on scenarios or exclusions.

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

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

No annotations provided, but the description compensates fully. It discloses credit cost (5 API credits), pagination behavior (offset-based via pn/rn), localization (ct parameter), advanced filters (mimicking Baidu operators), and the ability to return HTML instead of parsed JSON. This is rich 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 (4 sentences) and well-structured: purpose, cost, pagination, localization, advanced filters, return format. Every sentence provides essential information with no 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 no output schema, the description specifies the exact return format (Baidu_data array with title, link, snippet, rank). With 11 parameters and complex behavior (pagination, localization, operators), the description covers all key aspects, making it complete for selecting and invoking the 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 each parameter has a description. The tool description adds value by grouping parameters (q5, q6, bs, oq, f, gpc) as 'advanced filters' and explaining their combined purpose, and clarifying pagination semantics. This synthesizes usage context beyond individual 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 'Scrape Baidu search engine results pages' with specific verb+resource, and supports Baidu-specific search operators. It distinguishes itself from sibling search tools (e.g., google_search, bing_search) by being Baidu-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 gives clear context on when to use the tool (for scraping Baidu results) and details pagination/localization settings, but does not explicitly state when not to use it or compare to alternatives. The sibling list includes many search tools, so the name and description sufficiently differentiate it.

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

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

With no annotations provided, the description carries the full burden. It discloses the credit cost, mutual exclusivity of mkt and cc, and the return format. However, it omits details like rate limits, auth requirements, or error handling. The added context is valuable but not exhaustive.

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 reasonably concise with two sentences plus a notes section. It front-loads the purpose and adds essential details. Slight repetition of credit cost could be removed, but overall efficient.

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

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

Given 9 parameters and no output schema, the description covers the return format and key parameter interactions. It lacks details on pagination behavior or error cases, but for a search tool it provides sufficient context for an 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?

Schema coverage is 100%, baseline 3. The description adds meaning beyond the schema by explaining that lat/lon set a geographic starting point, mkt and cc are mutually exclusive, and first adjusts starting position with a default. This improves usability beyond the 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 it retrieves organic search results from Bing with customizable parameters, using a specific verb and resource. It implicitly distinguishes from sibling tools like Bing Shopping and other search engines by explicitly naming Bing 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 provides no explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it or compare to related sibling tools like google_search, duckduckgo_search, etc.

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 transparency burden. It reveals the credit cost, mutual exclusivity constraint, and return structure (JSON snippet). It does not mention idempotency or side effects, but as a read tool, this is acceptable.

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

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

The description is a single sentence plus a note on credits and mutual exclusivity, plus a return snippet. It is fairly concise, though the return snippet could be more structured. No 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 the lack of output schema, the description provides a return structure example. It covers credit usage and mutual exclusivity. It omits rate limits and error handling, but for a search tool, this 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%, so parameters are documented there. The description adds minor value: notes mutual exclusivity (not in schema) and efirst offset behavior. Most parameter meaning is already in schema, so baseline 3 is appropriate.

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

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

The description clearly states 'Retrieves shopping results from Bing', specifying the resource and action. It lists features like market targeting, pagination, and filters, distinguishing it from sibling shopping tools (e.g., google_shopping, walmart_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 mentions mutual exclusivity of mkt/cc, pagination via efirst, and filters, providing clear usage context. However, it lacks explicit guidance on when to choose this tool over alternatives like google_shopping or ebay_search.

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 discloses that no browser automation is required, the credit cost, and details of the return format (structured content blocks). However, it does not mention potential rate limits, error handling, or any required authentication.

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 no wasted words. It is front-loaded with the core action, then provides key details (credits, return format) in a clear, well-structured manner.

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 no output schema, the description adequately explains the return value. It covers credits and behavioral notes. It is mostly complete for a simple 2-parameter tool, though it could mention error scenarios or timeouts.

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 return format and that the prompt is the input, providing context beyond the schema descriptions. It clarifies the structure of the response, aiding interpretation.

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 sends a prompt to ChatGPT and returns a structured JSON conversation. The verb 'sends' and resource 'ChatGPT' are specific, and the description distinguishes from sibling tools that cover other platforms.

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

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

The description implies use when you want a ChatGPT conversation, but does not explicitly state when to use this tool vs alternatives, nor does it mention when not to use it. The sibling context helps, but the description itself lacks 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 full burden. It describes return format and pagination but does not explicitly state read-only nature or any side effects. The documentation note is about a copy-paste artifact, not behavioral.

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 detailed and well-structured, front-loading the core purpose and then adding notes. It is slightly lengthy but each part adds 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 thoroughly explains the return format and pagination tokens. It covers query, filters, region, pagination, and HTML retrieval, making it self-contained 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?

All 5 parameters are documented in schema (100% coverage). The description adds extra context: custom date range format, region documentation reference, and clarification on the html param, providing 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 it retrieves organic search results from DuckDuckGo and lists key features (region codes, date filters, pagination), differentiating it from sibling search tools like Google or Bing.

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

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

No explicit guidance on when to use DuckDuckGo vs alternatives (e.g., Google, Bing). Mentions credit cost differences but does not provide decision criteria for tool selection.

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

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

With no annotations, the description carries full burden. It discloses the output format (parsed JSON or HTML), mentions credit cost, and describes the return structure in detail. However, it omits potential error states or rate limiting.

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 core purpose and includes important notes. It is moderately concise; the return schema section is lengthy but useful. Minor redundancy (credits noted twice). Still efficient overall.

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

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

Given no output schema, the description provides a detailed return structure. Parameters are fully described. It covers credits and URL structure. Missing guidance on error handling or edge cases, but adequate for typical 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 coverage is 100%, so baseline is 3. The description adds value by explaining the URL structure (product identity embedded) but largely restates schema descriptions for the html parameter. Infomation adds minimal extra 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 the tool scrapes an eBay product listing page by URL to retrieve specific fields. It uses a specific verb ('scrape') and resource ('eBay product listing page'), and is easily distinguishable from siblings like ebay_search.

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

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

The description implies usage for scraping known product pages but does not explicitly state when to use this vs alternatives (e.g., ebay_search). It provides a note about URL structure but no when-to-use or 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?

Discloses credit cost per request and describes the return structure in detail. No annotations provided, so description carries full burden; it adequately informs the agent about behavior (a read scrape with cost implications).

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?

Efficient use of three sentences plus structured Notes and Returns sections. No redundancy; every sentence adds value. Front-loaded with purpose and cost, then usage details, then output format.

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

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

Despite having no output schema, the description provides a detailed schema for the return object. Covers credit cost, URL construction, and parameter semantics. Fully adequate for a simple two-parameter 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%, but description adds practical context: explains that url must be a full pre-built search URL, gives example, and clarifies that domain sets locale. This goes beyond what the schema offers.

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

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

States specific verb ('scrape'), resource ('eBay search result pages'), and what is returned (product titles, item IDs, etc.). Clearly distinguishes from sibling tools like ebay_product by referring to search pages, not product pages.

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 instructs to pass a full eBay search URL and explains how to build it using eBay's filters. Notes that the domain determines locale. Lacks explicit when-not-to-use or alternatives, but guidance is strong and actionable.

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

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

With no annotations, description carries full burden. It mentions credit consumption and notes about URL identity, but does not disclose rate limits, error handling, or behavior for invalid URLs. Some transparency but incomplete.

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 relatively concise with organized sections (credits, notes, returns). Could be slightly more concise, but this is effective.

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 annotations or output schema, description provides a detailed return structure and credit info. Lacks error behavior and prerequisites, but is largely 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% (both parameters described). Description adds value by explaining credit cost and return format, enhancing understanding beyond the schema. Baseline 3, plus extra context.

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 explicitly states it scrapes Flipkart product pages via URL and lists the data retrieved (title, brand, pricing, etc.). It clearly distinguishes from sibling tools like flipkart_search (which searches) and other product scrapers.

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?

Description provides credit cost and notes about URL structure, but does not explicitly state when to use this tool versus alternatives (e.g., flipkart_search for search, or amazon_product for Amazon). No exclusion criteria or context for selection.

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

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

No annotations are provided, so the description carries the full burden. It discloses the credit cost (5 API credits) and the return format, but does not mention failure modes, rate limits, or explicitly state it is read-only. 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 concise, front-loaded with purpose, and every sentence adds value. It includes a note about credits and a sample return structure, all within a few 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 the moderate complexity and lack of output schema, the description provides a complete picture: purpose, parameter usage, return format, and cost. The example return structure compensates for the missing 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?

The description adds value beyond the schema by clarifying that the 'url' parameter must be a fully built search URL with q= parameter. The schema already covers both parameters with 100% coverage, and the description reinforces usage without adding redundancy.

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 scrapes Flipkart search result pages and lists the returned fields (titles, prices, etc.). It distinguishes from siblings like flipkart_product by specifying search results.

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

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

The description explicitly says to pass a full pre-built Flipkart search URL via the 'url' parameter, providing clear usage guidance. It does not mention when not to use or list alternatives, but 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 API credit cost (5 per request), pagination mechanism, endpoint path quirk, and return structure. While it omits auth or rate limits, it covers key behavioral aspects well.

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 efficiently structured: front-loaded purpose, followed by usage notes, pagination, endpoint correction, and return schema. 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 11 parameters, no annotations, and no output schema, the description is remarkably complete. It explains alternative parameter groups, pagination, cost, and return format. Users have enough to use tool correctly without external docs.

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 params described). The description adds value beyond schema by explaining usage relationships (advertiser_id vs text, political_ads+region), pagination token origin, and endpoint path note, raising it above baseline 3.

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

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

The description clearly states it pulls ad data from Google Ads Transparency Center, specifying lookup methods (advertiser ID or keyword/domain) and multiple filters. It distinguishes itself from sibling Google scraping tools by focusing on ad transparency, 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 gives explicit usage conditions: provide either advertiser_id or text, political_ads requires region, pagination via next_page_token. It lacks explicit comparison to siblings, but given no direct competition, this is sufficient.

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 the return format including specific fields (shopping_results, inline_images, text_blocks, references, local_results) and notes the credit cost per request. It also explains the mutual exclusivity of geo-targeting parameters. However, it does not mention authentication requirements 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 a single clear sentence followed by structured notes on parameter constraints and return format. It is front-loaded with the core purpose and credits. The return format details are extensive but necessary for a tool returning structured data. Could be slightly 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?

Given the lack of output schema and annotations, the description provides a thorough explanation of the return structure and key parameter interactions. It covers credit costs and mutual exclusivity. However, it does not explain what 'AI Mode' entails or how it differs from other Google search tools, which would enhance 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?

The schema has 100% parameter description coverage, so the baseline is 3. The description adds value by explaining the mutual exclusivity of uule and location, and that country further localizes the search alongside these parameters. This additional context improves 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 that the tool searches Google with AI Mode enabled and returns structured results with reference sources and text blocks. It distinctly identifies the action and resource, differentiating from siblings like 'google_search' which likely returns standard search results.

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

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

The description mentions credit cost and mutual exclusivity of parameters (uule and location), but does not provide explicit guidance on when to use this tool versus alternatives (e.g., standard Google Search or AI Overview). The context of when to prefer AI Mode over other Google tools is implied but not stated.

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

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

Discloses cost, single-use and time-limited nature, and return format. No annotations provided, so description carries full burden. Missing failure modes for expired URLs.

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

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

Two sentences plus concise notes on credits and output. Front-loaded with purpose, minimal waste.

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 single parameter and no output schema, description details when, why, how, cost, limitations, and expected JSON structure. Very 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 has 100% coverage with a description. Description adds context about the URL source and expiration, enhancing 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?

Description clearly states it fetches Google AI Overview using a follow-up URL from Google Search API. Distinguishes from sibling tools like google_search.

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

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

Explicitly states when to use (only when primary response indicates secondary fetch), notes URL is single-use and time-limited (2 minutes), and provides credit cost.

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?

Description discloses key behaviors: returns suggestions with relevance scores, based on country and language, and notes no pagination. However, it lacks explicit mention of read-only nature, rate limits, or error handling. Since no annotations are present, this is acceptable 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 short but includes filler ('Credits: Not specified in documentation') that adds no value. Could be tightened by removing that line.

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 specifies the return format with fields. It covers the core functionality well, though it omits details like field descriptions ('type' unclarified) and possible empty results.

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 all parameters with descriptions, and the description reinforces their purpose (query, country, language). No additional semantics 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?

The description clearly states it returns Google Search autocomplete suggestions for a query, including relevance scores. It differentiates from generic search tools but does not explicitly distinguish from sibling autocomplete tools like google_trends_autocomplete.

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

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

No guidance on when to use this tool versus alternatives like google_search or other autocomplete tools. No prerequisites or exclusions mentioned.

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 provides the return structure and input format, disclosing behavioral traits. However, it lacks information 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?

The description is front-loaded with the main purpose and is mostly concise, though the credits line is superfluous and could be removed for better conciseness.

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

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

Given the tool's complexity and lack of output schema, the description adequately covers input format and return structure, but could be improved by noting error behavior or response format limitations.

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 reinforcing the query format and defining the return structure, but does not significantly enhance 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 retrieves Google Finance market data including stock price, price movement, and related market/news instruments. This distinguishes it from sibling tools like google_search which are more general.

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 implicitly indicates usage for financial data retrieval and specifies the query format requirement, but does not explicitly contrast with alternatives or state 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 carries the full burden. It discloses the cost (5 API credits per request), the two-step flow (multi-step process), mutual exclusions, and parameter interactions (e.g., booking_token ignores dates/filters). It also describes the return format. This gives good insight into the tool's behavior, though it could mention error handling or rate limits beyond credits.

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 informative but somewhat verbose and contains redundancy (credits mentioned twice: 'Costs 5 API credits per request. [Credits: 5 API credits per request]'). The structure is front-loaded with purpose but then mixes parameter notes and return format. Could be more concise by removing repetition and organizing flow better.

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 (29 parameters, multi-step flow), the description covers the essential two-step process, mutual exclusions, and a sample return format. It lacks detailed explanation of multi-city JSON structure and full enumeration of type values (only mentions round trip constraints). Still, it provides substantial context, and no output schema is present, so the return format description helps.

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 each parameter is already documented. The description adds value by explaining interactions (e.g., return_date required only for type=1, mutual exclusions) and the multi-step flow involving departure_token and booking_token. This context helps understand parameter usage 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 retrieves Google Flights results for one-way, round-trip, and multi-city searches, with sorting/filtering and booking token support. It specifies the resource (Google Flights) and action (retrieves), and distinguishes it from sibling tools like google_hotels or general google_search.

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

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

The description provides clear context on when to use the tool, including a two-step flow for full itineraries, parameter requirements (e.g., return_date required for type=1), mutual exclusions (exclude/include airlines, departure_token/booking_token), and the return format. However, it does not explicitly state when NOT to use the tool or alternatives, but the sibling list makes it obvious.

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

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

With no annotations provided, the description fully discloses behavioral traits: credit cost (5 API credits), date format (YYYY-MM-DD), filter mutual exclusivity between hotels and vacation rentals, pagination via next_page_token, and detail lookup via property_token. Example return structure is also included.

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 efficient paragraph front-loaded with purpose, then credit cost, then parameter constraints, then pagination, then return structure. Every sentence adds value with no 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 26 parameters, 3 required, no output schema, the description covers core flows (search and detail lookup), filter constraints, pagination, and sample output. Could be enhanced with rate limit info, but overall adequate 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?

Schema coverage is 100% with parameter descriptions. The description adds context beyond schema: mutual exclusivity of certain filters, credit cost, pagination mechanism, and property_token usage. This justifies a score above the baseline of 3.

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

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

Description clearly states the tool retrieves Google Hotels search results including property listings, pricing, ratings, amenities, and detailed property info via property_token. The verb 'retrieves' and specific resource 'Google Hotels search results' make the purpose unambiguous and distinguishable from siblings like google_search or google_flights.

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?

Description provides usage guidelines like credit cost, date format, and filter compatibility notes (e.g., hotels filters not supported for vacation rentals). It explains pagination and property token usage. However, it does not explicitly contrast with alternative tools like google_search for general hotel queries.

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

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

No annotations are provided, so the description carries the full burden. It discloses the cost (10 credits), return structure, and important parameter interactions (mutual exclusivity of date parameters, chips from suggested_searches). It does not cover rate limits or authentication, but given the complexity, it provides substantial behavioral insight.

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 informative and front-loaded with purpose, but it contains a slight redundancy (credit mentioned twice). It is structured with clear notes, but could be more concise. Overall, it earns its place 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 the complexity (23 parameters, no output schema, no annotations), the description covers key behavioral aspects: cost, return format, and parameter constraints. It does not detail every edge case, but it provides sufficient contextual completeness for an agent to use the 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?

Schema coverage is 100%, so the schema already documents all parameters. The description adds meaningful semantic guidance beyond the schema, such as the chips discovery mechanism and the constraints on time filtering parameters. This extra context helps an agent use the parameters correctly, elevating it above the baseline of 3.

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

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

The description clearly states the tool retrieves Google Images search results including specific fields like titles, thumbnails, source links, and dimensions. It distinguishes from sibling tools such as google_search, google_videos, and google_lens, which handle different media or search types. The verb 'retrieves' is specific and actionable.

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 mentions cost (10 API credits per request) and provides notes on time filtering and chips usage. However, it does not explicitly contrast with siblings or state when to use this tool vs. alternatives like google_search or google_lens. While some guidance is implied, it lacks explicit when-to-use/when-not-to-use direction.

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

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

No annotations provided, so description must carry burden. Discloses return structure and pagination behavior but does not mention rate limits, authentication, or side effects (though read-only assumed).

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 substantive sentences plus return format. Front-loaded with purpose. Extraneous credit note but overall 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?

Completeness is high given 5 parameters and pagination complexity. Covers pagination, parameter usage, return structure. No output schema, so return format is helpful.

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 all 5 parameters. Description adds context: explains sori and stores collaboration, and source of page_token. Also gives return format.

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 scrapes Google's immersive product popup view for a specific product, returning brand info, price range, and per-store listings. Distinguishes from sibling google_shopping by focusing on popup view.

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 pagination guidance: enable `stores=true` and increment `sori` by cumulative sellers. Notes that page_token comes from google_shopping. Lacks explicit alternatives or 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?

With no annotations, the description carries the burden. It effectively discloses the cost (5 API credits), pagination behavior (next_page_token), and the return format. This goes beyond the schema by adding behavioral context. It does not contradict any annotations (none provided). However, it could mention error conditions 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 mostly concise with two sentences and a return type example. However, it includes a redundant phrase '[Credits: 5 API credits per request]' that repeats the earlier statement, slightly reducing efficiency. Still, it is well-structured 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 the tool has 10 parameters, no output schema, and no annotations, the description provides adequate context: cost, pagination, and return structure. It lacks explanation of error handling, rate limits, or parameter interactions. It is complete enough for basic use but has gaps for a 10-parameter 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%, so the baseline is 3. The description adds minimal extra meaning beyond the schema; it clarifies that query should be like 'jobs in london' and that next_page_token comes from a prior response. Most parameters remain as described in the schema, so no significant additional semantics.

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

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

The description clearly states it retrieves Google Jobs search results with specific fields (job titles, company names, etc.), distinguishing it from general search tools like google_search. However, it does not explicitly differentiate from other job-specific tools in the sibling list (e.g., linkedin_jobs_search, indeed_scraper), so it loses a point for lack of explicit 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 guidance on pagination using next_page_token and mentions the cost (5 credits), which helps in usage. However, it does not specify when to use this tool versus alternatives, nor does it mention prerequisites or limitations. The guidance is implied but not 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?

Discloses cost (5 API credits), parameter type nuance (string but boolean-like), and returns structure. With no annotations, this is valuable behavioral context. Lacks mention of idempotency or rate limits, but sufficient for a 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?

Concise and front-loaded with the main action and credits. Could be more structured (e.g., bullet points), but no wasted sentences.

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

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

Given 7 parameters with 100% schema coverage and no output schema, the description provides meaningful extra context (credits, type warning, return format) to support agent decision-making. Complete enough for typical 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 coverage is 100%, but description adds clarity on boolean-like parameter values and the URL format for the required parameter. This goes beyond the schema descriptions, aiding 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?

Description clearly states it performs reverse image search via Google Lens, supporting product, visual, and exact matches. It differentiates itself from sibling tools like google_search and google_images by specifying the Lens endpoint and result types.

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

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

No guidance on when to use this tool versus alternatives, such as when to choose Lens over generic image or web search. No explicit when-not-to-use scenarios mentioned.

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 discloses credit cost and mutual exclusivity but lacks details on authentication, rate limits, error handling, or pagination beyond the 'page' parameter.

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 first sentence stating purpose, followed by credits and notes. The structure is good, though the credits line is redundant.

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 9 parameters and no output schema, the description provides a reasonable return structure and constraints. However, it misses potential error scenarios and pagination behavior beyond the 'page' 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%, so the schema already documents parameters. The description adds critical mutual exclusivity note and return format, which goes 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 retrieves Google Local business listings with specific fields like ratings, reviews, addresses, etc. It distinguishes from siblings like google_search (web search) but does not explicitly differentiate from similar tools like google_maps_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 mentions cost (5 credits) and the mutual exclusivity of 'location' and 'uule', providing some constraints. However, it does not provide when to use this tool versus alternatives like google_maps_search.

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 retrieval behavior, optional filtering, pagination mechanism, and return format. No mention of side effects or authentication, but for a read-only tool, it's sufficiently 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 main purpose upfront, then notes and return format. The credit note is minor but not excessive. Every sentence provides value; could be slightly shorter but remains efficient.

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

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

Given no output schema, the description provides a good summary of return structure and pagination. Covers category filter workflow. Lacks error handling or limits, but is fairly complete for a photo 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?

Schema coverage is 100%, and the description adds substantial meaning: explains how to obtain and use category_id, notes data_id origin from search endpoint, and describes next_page_token usage. This goes well beyond the 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 photos for a Google Maps location using a data_id, with optional category filtering. It distinguishes from sibling tools like google_maps_search and google_maps_reviews, though it doesn't explicitly name alternatives.

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

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

Provides explicit instructions for category filtering (two-step process) and pagination (cursor-based with ready-made URL). Mentions data_id source from google_maps_search. Lacks explicit when-not-to-use or alternatives, 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?

No annotations are provided, so the description carries the full burden. It discloses the return format and fields, but lacks information on error handling, rate limits, authentication, or what happens if no place is found. The credit note is vague.

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 clear sections and front-loaded purpose. The credit note is slightly extraneous, but overall it's efficient and provides necessary detail in a readable format.

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 number of parameters (4), no output schema, and no annotations, the description is comprehensive. It explains the input parameters' relationships, describes the output fields in detail, and covers the core functionality adequately for an 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?

With 100% schema coverage, baseline is 3. The description adds value by explaining the alternative required-parameter paths (type+data_id vs place_id) and clarifying that place_id can be used independently, which goes beyond the schema's field 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 a complete business profile for a specific Google Maps location, with explicit details on what fields are included. This distinguishes it from sibling tools like google_maps_search (which returns lists) and other specific data 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 explains two lookup modes (type+data_id vs place_id) and clarifies the parameter requirements. While it doesn't explicitly say when to use this versus alternatives, the context and sibling tool names imply it's for individual place details after a search.

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 the read-only nature (retrieves posts), mentions pagination behavior, and specifies the return structure. No contradictions exist.

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 purpose statement, notes on prerequisites, pagination, and return format. The credits note is slightly extraneous but not detracting.

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 no output schema, the description fully explains the return JSON structure (location_details, post_data, next_page_token). It covers data source and pagination, making it complete for a paginated list 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%, but the description adds value by explaining that data_id comes from google_maps_search and next_page_token from previous responses, which is beyond the schema's basic 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 verb 'Retrieves' and the resource 'posts and updates published on a Google Business Profile listing', and distinguishes it from sibling tools like google_maps_reviews or google_maps_photos by specifying it's for posts.

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 explains how to obtain the required data_id via google_maps_search and that pagination is cursor-based. It provides clear context but does not explicitly state when not to use this tool or list 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?

With no annotations provided, the description fully bears the burden of disclosing behavior. It explains pagination (cursor-based via next_page_token), the exception for results param on first page, and the detailed return structure including locationDetails, topics, reviews_results, and 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 somewhat lengthy but well-structured, front-loading the main purpose and then detailing nuances. Every sentence adds value, though a slight trim could improve conciseness 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?

For a tool with 6 parameters and no output schema, the description provides a thorough explanation of inputs, behavior, and return fields. It covers edge cases, pagination, and the structure of the response, making it fully self-contained.

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 significant meaning beyond the schema: it explains the origin of topic_id, the edge case for results on first page, and the meaning of next_page_token. This helps the agent understand parameter usage beyond syntax.

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 customer reviews for a Google Maps location given a data_id. It uses specific verb 'retrieves' and resource 'customer reviews', distinguishing it from sibling tools like google_maps_search or google_maps_photos.

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 explains that data_id comes from google_maps_search and topic_id from a prior response, providing context for usage. It does not explicitly list when not to use or direct alternatives, but the purpose is clear 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?

With no annotations, the description carries full burden. It details return format, pagination behavior (requires ll), and notes that each result contains follow-up links. It does not mention any destructive or auth-related behavior, but the tool appears 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?

The description is well-structured with a clear purpose first, then notes, then return format. It is somewhat long but flows logically, and every sentence adds value. Could be slightly more 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 no output schema, the description fully details the return JSON structure, pagination, and parameter interactions. It addresses complex scenarios like using place_id independently and pagination requirements.

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 significant context: format details for ll and data, pagination increments, independent use of place_id, and domain/country/language parameter 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 explicitly states it returns Google Maps business listings including rating, reviews, address, etc. for a search query or specific place. It distinguishes itself from siblings (google_maps_photos, etc.) by noting that it provides follow-up links to those endpoints.

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 explains when to use the tool (search query or place_id), and includes pagination requirements. It implies when to use sibling tools via the follow-up links, but does not explicitly state when not to use this tool or provide alternative scenarios.

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 does well by disclosing credits usage, pagination behavior, relative timestamps, and that no id/token concepts are used. It could mention rate limits or error handling, but the core behavior is transparent.

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

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

The description is relatively concise, combining key information in a few sentences and notes. It could be better structured (e.g., bullet points for notes), but it is efficient and front-loaded with the primary 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 12 parameters, no output schema, and no annotations, the description adequately covers the tool's purpose, behavior, and return structure (inline). It does not cover error scenarios or environment requirements, but it is sufficient for basic 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 schema already documents all parameters. The description adds some context (e.g., pagination note, v2 reference) but does not significantly enhance parameter meaning beyond what is in the schema.

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

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

The description clearly states it retrieves news search results from Google News with specific fields (headlines, snippets, etc.). It distinguishes itself from google_news_v2 by mentioning relative timestamps vs absolute dates, and from other search tools by specifying the 'classic search-style scrape' nature.

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 context on pagination (0-indexed), credits, and a clear alternative (google_news_v2) for absolute dates. However, it lacks explicit guidance on when to use this tool over other search tools like google_search or google_news_v2 beyond the date distinction.

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

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

With no annotations, the description fully discloses behavior: it returns a specific result structure, notes undocumented 'story_token' concept, warns about ambiguity, and states lack of pagination parameter. This level of transparency is exemplary for a potentially complex endpoint.

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 key information front-loaded (improvements, search modes). However, it includes a superfluous [Credits] line and the Notes section is verbose, slightly hindering conciseness.

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

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

The description covers return format, parameter constraints, undocumented features, and differences from v1. It lacks explicit mention of error handling or rate limits, but given the tool's complexity, it is largely complete and helpful for an 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 description coverage is 100%, but the description adds value by explaining token concepts, mutual exclusivity, and the undocumented story_token. It provides high-level context beyond the schema, justifying a score above baseline 3.

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

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

The description clearly states it's a faster v2 endpoint for Google News, highlighting key improvements (image URLs, ISO dates). It distinguishes from v1 and explains the different search modes (topic, publication, section tokens vs free-text query), making the 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 clear context on when to use this tool (v2 for faster results, image URLs, ISO dates) and explains mutual exclusivity among parameters. However, it does not explicitly compare to sibling google_news_search or state when to prefer v1, leaving some ambiguity for the 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 exist, so the description carries full burden. It discloses API credit cost (5 credits per request) and the return structure. However, it does not mention error handling, rate limits, or what happens if patent_id is invalid. The credit cost is helpful but incomplete for full behavioral transparency.

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 efficient at 5-6 sentences, front-loading the core purpose and key fields. Uses notes for critical format clarification and cost info. Could be slightly tighter but is well-structured and avoids 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 the tool has three parameters, no output schema, and no annotations, the description covers what the tool does, what it returns, and the crucial format constraint. It does not explain the html or language parameters beyond the schema, but those are standard. Overall adequate for an agent to use 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?

Schema coverage is 100%, with parameter descriptions already clarifying the patent_id format. The description's format note is redundant with the schema. The description adds minimal extra meaning beyond listing return fields, which is not directly parameter semantics. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool retrieves detailed information about a specific patent, listing key fields like title, PDF link, inventors, dates, and prior-art keywords. It is specific and distinguishes from sibling tools like google_patents which is a search 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?

Provides explicit guidance on the required patent_id format, including an example and warning about the format returned by the sibling google_patents search. Implies this tool should be used after obtaining a patent ID from search results. Could be more explicit about when to use vs. other patent-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?

With no annotations, the description fully discloses credits, pagination behavior, patent_id format, and return structure. It does not mention rate limits or error handling, but provides sufficient behavioral details for an AI agent to use the tool correctly.

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: main purpose in first sentence, credits, then key behavioral notes, return format. Every sentence adds value without redundancy. 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 17 parameters, no output schema, the description covers return structure and key behavioral notes. It explains how to use results with a sibling tool, but could mention more about error states or edge cases 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 coverage is 100%, so the baseline is 3. The description adds significant value by explaining date format (type:YYYYMMDD), semicolon usage for multiple query terms, and the patent_id format conversion, which are not fully covered in the 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 the tool searches patent records across Google Patents with advanced filtering, specifying the verb (Searches) and resource (patent records). It differentiates from sibling tool google_patent_details by noting the patent_id format difference and how to pass parameters to that tool.

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 context on pagination, date format, and how to use results with google_patent_details, but does not explicitly state when to use this tool versus alternatives like google_scholar or other search tools. The context is clear but lacks explicit exclusions.

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

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

No annotations are provided, so the description carries full burden. It details credit cost (5 API credits per request), pagination behavior, and return structure (including related_searches, scholar_results, pagination). It does not explicitly state read-only nature, but the context implies it. It is transparent about input-output relations.

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 one-sentence overview, then adds credits, usage notes, and return format. It is relatively dense but well-organized, avoiding unnecessary repetition. The return schema is provided in lieu of an output schema.

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 16 parameters and no output schema, the description provides a complete return object definition and explains inter-parameter dependencies. It covers pagination and language filters adequately, making the tool usable without external references.

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 value by clarifying relationships: query becomes optional if cites is provided, cluster cannot be used with query and cites, and IDs from results tie to parameters. This goes beyond individual 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 'Searches academic papers and scholarly content on Google Scholar' and lists specific capabilities (citation lookups, author/source helpers, year filters, pagination). This distinguishes it from siblings like google_scholar_author (author details) and google_scholar_cite (citation output).

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

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

The description provides explicit notes on how to use IDs from results for other endpoints (e.g., 'id' values feed google_scholar_cite, cluster_id and cites_id are used with cluster and cites parameters). It also explains pagination (0-indexed) and provides return schema. However, it lacks direct comparison with all sibling tools, 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 provided, the description carries full burden. It explains the two modes (default profile vs list_colleagues) and the ambiguity with the sibling endpoint, and provides a return structure. It could mention authentication or rate limits but is still clear.

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

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

The description is structured well with a clear main purpose followed by notes. The ambiguity note is lengthy but important. Front-loaded with core functionality. Could be slightly 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?

Given the absence of an output schema, the description provides a complete return structure and covers all parameters and modes. It addresses the sibling tool relationship and potential confusion, making it fully informative 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 each parameter is described. The description adds context beyond schema by explaining the behavioral differences of view_op values and the relationship between author_id and citation_id. However, it repeats some 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 it retrieves comprehensive author information (name, affiliation, email, publication/citation history) and also exposes a co-author list mode. It explicitly distinguishes from the sibling tool 'google_scholar_author_citation' by explaining the ambiguity and the two modes of the same 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?

The description provides explicit guidance on when to use this tool versus the citation tool, including the note about potential merging. It also mentions how to obtain the author_id from the profiles tool and prerequisites for using citation_id.

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 exist, so the description must fully disclose behavior. It does so by describing the read-only retrieval operation, the return structure, and the required parameters. It could mention rate limits or authentication, but for a simple data retrieval tool, it's sufficiently transparent.

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

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

The description is multi-sentence but each sentence adds necessary information: purpose, endpoint note, citation_id format, and output structure. It is front-loaded with the core action. Slightly verbose but not wasteful.

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 provides a detailed breakdown of the return structure. It covers all required parameters and their interrelation. Missing error handling or edge cases, but sufficient for typical 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?

The input schema has 100% coverage with descriptions, but the description adds significant value by explaining the citation_id format (author_id:opaque_token) and its source (from google_scholar_author's articles). This extra context helps the agent use the parameters correctly.

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 citation metrics (h-index, i10-index, yearly graph) and article citation details, using view_op=view_citation on the author endpoint. This distinguishes it from siblings like google_scholar_author (profile retrieval) and google_scholar_cite (citation format).

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 explains that citation_id is required and obtainable from google_scholar_author, and points to an ambiguity note about the shared endpoint. While it doesn't explicitly list when not to use, it provides enough context to avoid confusion with similar tools.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool requires a specific ID, returns citations and links, and mentions credits not stated. It does not mention auth or rate limits, but as a read-only citation retrieval, this is sufficient.

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 purpose, followed by notes and return structure. It is slightly long but every sentence provides value. Could be slightly more concise but overall 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?

Despite no output schema, the description includes a clear return structure. It provides all necessary information to use the tool correctly, including parameter clarification, return format, and context from sibling tools.

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

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

Schema coverage is 100% with both parameters described. The description adds critical value by clarifying that 'query' is actually a result ID and referencing the id field from google_scholar results, and it explains the 'language' parameter default.

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 formatted academic citations (MLA, APA, etc.) and export links using a Google Scholar result ID. This uniquely identifies its function and distinguishes it from sibling tools like google_scholar (search) and google_scholar_author (author info).

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

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

It specifies when to use (to get citations for a paper given its result ID) and clarifies that the 'query' parameter is a result ID, not free text. However, it does not explicitly mention when not to use or provide alternatives, though the sibling context implies its narrow use case.

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 explains cursor-based pagination, token precedence, credit mention, and the linkage to other tools. Could further disclose rate limits or limitations but is sufficient for basic transparency.

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 informative but slightly verbose with bracketed notes. Every sentence adds value, though the structure could be streamlined by integrating the pagination note into the main flow.

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 3 parameters, no output schema, and no nested objects, the description provides a clear return structure and pagination details. It is complete enough for an AI agent to understand usage, though an explicit return format table would improve it.

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 meaning by noting that 'mauthors' supports query helpers like 'label:', and that 'after_author' takes precedence over 'before_author'. The pagination token retrieval note adds 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 specifies a clear verb ('searches') and resource ('academic researcher profiles on Google Scholar by author name'), and distinguishes from sibling tools like google_scholar_author and google_scholar_author_citation by noting that its author_id is used as input to those endpoints.

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 implicitly guides usage by stating that the author_id feeds into google_scholar_author and google_scholar_author_citation endpoints, and explains pagination tokens. However, it does not explicitly contrast with google_scholar (which searches articles) or provide exclusion criteria.

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

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

No annotations provided, so description carries full burden. It discloses credit costs, pagination inconsistencies (0-based sequential vs. 'start' offset), response shape dynamics (only present SERP blocks), mutual exclusivity of location/uule, and parameter overrides. 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?

Description is long but well-structured: core purpose first, then credits, then pagination clarifications, then parameter notes. Every sentence adds value; minimal redundancy. Slightly verbose but does not waste words.

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

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

Given 23 parameters, no output schema, and no nested objects, description is remarkably thorough. Covers credits, pagination behavior, response shape, parameter interactions, and edge cases. Agent has all needed information for correct invocation.

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

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

Schema coverage is 100% (baseline 3), but description adds significant value beyond schema: pagination inconsistency note for 'page', credit cost for 'advance_search'/'mob_search', mutual exclusivity for 'location'/'uule', override semantics for 'kgmid'/'si', and usage example for 'ibp'. Elevates agent 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?

Description clearly states verb 'scrapes' and resource 'Google Search results' (organic, ads, AI overview, etc.), distinguishing it from sibling tools like bing_search or duckduckgo_search. Specificity about SERP features and credits ensures unambiguous purpose.

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

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

Description explicitly says when to use (scraping Google results without proxy management) and provides usage guidance: credits for standard vs. advanced/mobile search, mutual exclusivity of location/uule, pagination nuance, and override behavior of kgmid/si. Implicitly suggests alternatives via sibling list.

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 discloses credit cost, pagination, filter flow, and return structure effectively.

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 front-loaded with purpose and includes necessary notes, though could be slightly 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?

Covers return structure, pagination, filtering, and credits. No output schema, but provides sufficient detail for a complex 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 coverage is 100%, so baseline is 3. Description adds some context for shoprs and page but doesn't significantly enhance meaning 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?

The description clearly states it scrapes Google Shopping search results including ads, listing, and filter facets. It distinguishes from siblings like bing_shopping or google_search by focusing on Google Shopping.

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 pagination guidance and two-step filter flow, but lacks explicit alternatives or when-not-to-use scenarios.

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

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

With no annotations provided, the description carries full burden. It discloses the return format and pagination uniqueness, but does not mention whether the tool is read-only, requires authentication, or handles errors. This is adequate but not fully transparent for a 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?

The description is a single, focused paragraph with the purpose upfront, followed by a critical pagination note and a clear return structure. No unnecessary words, earning its place.

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

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

The description provides a return structure example, compensating for the missing output schema. It covers the essential behavioral note on pagination. However, it lacks explicit mention of idempotency or error scenarios, which are minor gaps for a simple 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?

Schema coverage is 100% with all 11 parameters described, so the description adds no additional semantic value beyond the schema. The mention of pagination offset is a behavioral note, not parameter-specific. 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 retrieves Google Shorts search results, specifying the type of content (short videos) and listing returned fields (thumbnails, GIF previews, account names, dates). This distinguishes it from siblings like google_videos or google_search which cover general video or web 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 explicitly notes that pagination uses offset-based 'start' unlike other endpoints using 'page', providing clear context for correct usage. However, it does not explicitly state when to use this tool versus alternatives, though the purpose is self-explanatory for short video queries.

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

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

With no annotations, the description carries the full burden. It discloses credit cost, multi-query constraints, region parameter dependencies, and a sample return format for TIMESERIES. However, it omits details like error handling, rate limits, and full structure for GEO_MAP data types, which would enhance transparency.

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 a few sentences, front-loading the core purpose. It includes inline notes and return format, but the structure is dense and could be improved with bullet points for better readability. 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 9 parameters and no output schema, the description covers key behavioral constraints and a sample output for TIMESERIES. It omits full output structures for GEO_MAP types and does not differentiate from many siblings, but is adequate for a read tool with good parameter descriptions.

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 clarifying relationships between data_type and query limits, and region parameter usage with GEO_MAP types. It also provides practical examples for date formats and topic IDs, going beyond the 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 the tool retrieves Google Trends search interest data, listing three specific data types (interest over time, comparative regional breakdown, interest by region) and the capacity for up to 5 queries. It distinguishes from siblings like google_trends_autocomplete and google_trends_trending_now by focusing on historical trends 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 does not explicitly compare this tool to alternatives or state when to use it over siblings. It implies usage through data type descriptions but lacks direct guidance on tool selection, such as 'for autocomplete suggestions use google_trends_autocomplete'.

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 the output structure (JSON with suggestions array) and explains the Freebase-style mid usable in google_trends. However, it lacks details on rate limits, authentication, 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?

Description includes useful output info and credit note, but the credit statement is extraneous for tool invocation. Could be more concise without losing essential guidance.

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 2 params, 100% schema coverage, no output schema, and no annotations, the description is reasonably complete. It explains output structure and the use of mid, satisfying basic needs for a lightweight autocomplete 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%. Description does not add meaning beyond the schema; it mentions query and language but provides no additional constraints, examples, or formatting 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?

Description clearly states it returns Google Trends autocomplete suggestions for a search query, including topics/entities with categorization and a Trends link. Distinguishes from siblings like google_trends and google_autocomplete by focusing on autocomplete for Trends data.

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