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

No annotations provided, but the description clearly explains the output and the continuation mechanism, making behavior 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 detailed and informative, though slightly verbose with repeated examples; it is still 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?

The description fully explains what the tool does, its output, and its multi-turn continuation feature, making it complete for the given 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 covers all parameters with descriptions; the tool description does not add additional meaning beyond the schema, so a neutral score.

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

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

Clearly states it gets AI Mode SERP results from Google, distinguishing from other Google SERP tools like AI Overview.

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

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

Provides explicit use cases and examples, but does not explicitly compare with alternative tools, so slightly less than perfect.

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 block is lazy-loaded, the token is valid for only 4 minutes, and it returns specific content. While it doesn't explicitly state read-only behavior, the description implies a safe fetch operation.

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 well-structured: a clear title line, a brief explanation of what is fetched, the returned contents, and explicit use cases. No redundant or extraneous text.

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

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

Given the simple input (one parameter, no nested objects) and no output schema, the description is complete. It explains the purpose, parameter source and validity, expected return contents, and appropriate use cases, giving the agent everything needed to decide and invoke correctly.

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

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

The sole parameter, pageToken, is fully described in the input schema ('Token from aiOverview block... Valid for 4 minutes'), and the tool description repeats this same information without adding new meaning. Since schema coverage is 100%, baseline is 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's action ('Get AI Overview Results') and resource (Google AI Overview block), distinguishes it from sibling tools by specifying it's a follow-up using a pageToken, and details the returned data (answer text, source URLs, subtopic sections).

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 provides use cases: 'tracking AI citations in SEO, fact-checking answers against sources, and LLM retrieval pipelines grounded in live Google results.' It also implies the prerequisite of having a pageToken from a prior SERP call, making it clear when this tool is appropriate.

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

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

No annotations are provided, so the description carries the full burden. It states the tool 'scrapes' data, implying a read-only operation, but does not mention rate limits, authentication, or potential blocks. The description is adequate but not fully transparent about operational constraints.

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 well-structured, starting with a brief summary, followed by functional details and use cases. It avoids excessive verbosity while covering essential aspects.

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, the description provides sufficient context about functionality, parameters, and intended use cases. It does not describe return values, but no output schema is provided, so that is not required. It is complete enough for an agent to decide when and how to invoke 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?

The schema already provides descriptions for all 8 parameters, including detailed explanation of htichips filters. The tool description adds no additional parameter semantics beyond what is already in the schema, so it meets the baseline but does not exceed it.

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

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

The description clearly states the tool scrapes the Google Events vertical, listing specific filters and use cases. This distinguishes it from generic search and other vertical tools, such as general SERP or image 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?

It explicitly mentions use cases like event-discovery chatbots and local aggregators, implying when to use this tool. It does not name alternative tools, but the context makes it clear this is for event-specific searches.

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 describes the read-like behavior (expands, returns) and mentions pagination and optional store expansion, but it does not explicitly state that it is a read-only operation or disclose any potential side effects, auth requirements, or rate limits. The description is informative but not fully explicit.

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

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

The description is concise and well-structured, with the core purpose front-loaded. It includes a useful list of return fields and use cases without excessive verbosity.

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

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

Given there is no output schema, the description adequately explains what is returned (offers, specs, images, ratings, nextPageToken). It also covers key parameters and use cases. It does not mention error handling or token lifecycle, but these are not critical for a simple read 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 schema already documents all parameters. The description adds minor context (e.g., moreStores expands from 3–5 to up to 13, nextPageToken paginates) but largely repeats what the schema says. It adds value but not significantly 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 states a clear verb and resource: it expands the Google Shopping Immersive Product pop-up given a token. It distinguishes itself by focusing on the immersive product view, but it does not explicitly name sibling tools or contrast with them, so it misses the top score.

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

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

It provides clear use cases (price-comparison bots, merchant discovery, dropshipping research) but does not explicitly state when to avoid it or name alternatives. The context is clear but exclusions are absent.

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 burden of behavioral disclosure. It transparently describes the return payload (title, snippet, source publisher, published date, thumbnail, URL) and the navigation tokens, and notes that storyToken supports sorting by relevance/date. It does not mention limits, errors, or rate restrictions, but for a read-oriented GET endpoint the description provides solid 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 compact, front-loaded with the core purpose, and every sentence adds useful information about retrieval modes, output, and use cases. It is dense but not bloated, and it avoids unnecessary repetition of schema 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 the 8-parameter schema with full descriptions and no output schema, the description adequately covers what the tool returns, the main input modes, and appropriate usage scenarios. It does not cover pagination or result limits, but the schema and stated output fields give an agent enough to invoke and interpret the tool correctly.

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

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

Schema description coverage is 100% and every parameter has a meaningful description with mutual-exclusion constraints. The tool description adds value by grouping the parameter types and explaining the story-cluster sort, but it mostly paraphrases what the schema already documents. Baseline 3 is appropriate since the schema does the heavy lifting.

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

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

The description clearly states a specific verb ('Retrieves Google News results') and resource, listing the distinct retrieval modes (free-text query, topicToken, sectionToken, publicationToken, storyToken) and the returned fields. It distinguishes itself from the general google_serp_serp_getSearchResults sibling by focusing specifically on Google News and its token-based navigation.

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 enumerates intended use cases: 'news monitoring, brand/PR tracking, topical aggregators, publisher-specific feeds, and drilling into full story coverage.' It does not explicitly name a sibling as an alternative or state when not to use this tool, but the use-case framing gives an agent clear selection context.

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

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

No annotations are provided, so the description carries the full burden. It clearly frames the operation as read-only ('Pulls') and discloses searchType-dependent return content: title, images, price, ratings, specs, merchant offers, and review text. It does not cover rate limits, auth, or errors, but the core behavioral contract is present.

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

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

The description is compact and front-loaded: a one-line summary, then the detailed behavior, then use cases. Every sentence contributes value, and there is no meaningful 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?

For a 9-parameter tool with no output schema, the description provides a strong high-level output contract and common use cases, while the schema covers parameter details. The main gap is that optional searchType has no documented default behavior, and pagination guidance relies entirely on the schema rather than the description.

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

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

Schema description coverage is 100%, and the schema already explains filters, pagination, enums, and parameter constraints. The description paraphrases searchType and filters but adds no new parameter-level semantics, so the baseline of 3 applies.

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

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

The description states a specific verb and resource: 'Pulls detailed product data from Google Shopping by productId' with searchType variants and filters. This clearly distinguishes it from sibling search tools like shopping_getSearchResults, which are search-oriented rather than product-ID lookups.

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

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

The description explicitly lists task-based use cases: price intelligence, catalog enrichment, review mining, competitor spec comparison, and building shopping assistants. It does not name exclusions or alternative sibling tools, but it gives clear context for when this tool is appropriate.

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

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

With no annotations, the description carries the behavioral disclosure burden. It does disclose that this is a scraper, enumerates filtering capabilities, and lists returned elements including organic results, ads, knowledge graph, People Also Ask, local pack, and AI Overview pageToken. However, it does not mention operational caveats such as rate limits, authentication needs, blocking risks, or pagination limits beyond offset/num.

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-line summary and then organized into capability, return-value, and use-case sections. It is long, but the length is justified by the tool's 19 parameters and rich feature set; the examples are concrete rather than filler.

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

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

For a complex 19-parameter tool with no annotations and no output schema, the description is fairly complete: it explains what the tool returns and gives practical invocation examples. It does not discuss alternatives or limitations, but an agent has enough information to select and call the tool correctly.

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

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

Schema description coverage is 100%, so all 19 parameters are already documented in the input schema. The description only groups them into capability families like location/uule, country, language, tbm, and offset/num pagination, adding no parameter-level meaning beyond what the schema already provides.

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

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

The description names the operation ('Get Google Search Results') and identifies the tool as a 'full-featured Google Search scraper', listing parameter families and returned SERP components. It clearly conveys that this is the generic Google web-search endpoint rather than an image, news, or shopping variant, though it does not explicitly distinguish itself from the sibling serp_light 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 gives concrete use cases: SEO rank tracking, keyword research, SERP-feature monitoring, competitor analysis, grounding LLMs, and person/company data enrichment with example queries. It does not explicitly state when not to use it or when to prefer serp_light, google_images, or AI-overview siblings, so it stops short of full exclusion guidance.

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

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

With no annotations provided, the description carries the full burden, and it does disclose key traits: it omits rich blocks, returns only organic results, and supports specific filters. However, it doesn't mention error handling, rate limits, or potential staleness of data—common concerns for scraping tools. It adds value but isn't 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 well-structured and front-loaded: it states the core purpose, then omissions, capabilities, return format, and use cases. Every sentence contributes value, but it's slightly long given the detailed schema already available. Still, 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 12 parameters, detailed schema, and no output schema, the description covers the essential decision-making info: what it returns, key filtering options, and typical use cases. It doesn't explicitly explain pagination mechanics (though schema does) or edge cases, but the provided context is sufficient for an agent to select and invoke 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%, so baseline is 3, but the description groups parameters into meaningful categories (location/uule, country, language, filters, pagination), giving agents a high-level understanding of how they combine. It adds conceptual clarity beyond the schema's individual parameter 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 is a lightweight Google Search scraper that returns only organic results, explicitly listing what it omits (AI Overview, knowledge graph, PAA, etc.) and what it returns (title, link, snippet, position). This makes it readily distinguishable from siblings like serp_getSearchResults or news/shopping 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?

Explicit use cases are given: high-volume keyword monitoring, bulk rank tracking, backlink discovery, and any workflow where only the ten blue links matter. This implies when not to use it (when rich SERP blocks are needed), though it doesn't explicitly name the alternative tool like hasdata_google_serp_serp_getSearchResults. Clear context but no formal exclusion.

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 responsibility for disclosing behavioral traits. It does not explicitly state whether the operation is read-only or if there are side effects, rate limits, or permissions. The phrasing 'Scrapes Google Shopping listings' implies a passive retrieval, but it lacks an explicit statement about non-destructiveness or data handling.

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

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

The description is concise and well-structured, with a clear opening statement, a summary of output, and a list of use cases. It avoids redundancy and stays focused, making it easy for users to quickly grasp the tool's function and applications. The structure is efficient with no unnecessary 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?

Despite lacking an output schema, the description compensates by enumerating the expected return fields (e.g., product title, price, merchant) and explaining how to apply filters via 'hasdata_link'. It also covers parameter interactions for shoprs and pagination. Given the tool's moderate complexity, the description provides a complete picture for invocation without leaving critical gaps.

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

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

The schema already provides detailed descriptions for all 10 parameters, covering their semantics thoroughly. The tool description does not add significant extra meaning beyond summarizing the overall purpose and mentioning a few output fields. Since schema coverage is 100%, the baseline score of 3 is appropriate, as the description adds marginal value.

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

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

The description clearly states that the tool scrapes Google Shopping listings for a search query and returns detailed product information. It explicitly mentions the output fields, such as product title, price, and merchant, making the tool's purpose unambiguous. The name 'Get Shopping Search Results' further reinforces its function.

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

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

The description provides specific use cases like e-commerce price tracking, catalog building, and promotion discovery, which guide when to employ the tool. It also hints at integration with other tools by mentioning feeding product IDs into the Product API or tokens into the Immersive Product API. However, it does not explicitly contrast it with sibling tools, leaving a small gap in direct alternative 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 behavioral disclosure burden. It explains that the tool scrapes a live Google carousel and returns specific fields, implying a read-only network operation. It does not mention rate limits, auth, or failure modes, but these are less critical for this non-mutating scraper.

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 short and front-loaded, with the core scraping behavior in the first sentence and output fields and use cases following. The opening heading repeats the tool name and the use-case list is slightly expansive, but the overall text is efficient and relevant.

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 9-parameter tool with no output schema, the description compensates by listing return fields and mentioning pagination plus location/country/language options. It does not specify result count, maximum page, or exact response structure, but the schema fully documents the parameters and required query.

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

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

The input schema already describes 100% of the parameters, so the baseline is 3. The description adds only high-level grouping such as 'country (gl/cr), language (hl/lr), device type' without clarifying relationships, formats, or usage beyond what the schema already provides.

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

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

The description uses a specific verb ('scrapes') and a specific resource ('Google Short Videos carousel'), and names the content sources (TikTok, YouTube Shorts, Instagram Reels). This clearly distinguishes it from sibling SERP tools like general search or news 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 provides explicit use cases such as 'short-form content discovery, viral-trend monitoring, influencer research, cross-platform video aggregation' and sourcing clips for LLM responses. However, it does not explicitly contrast with sibling tools or state when not to use this tool.

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