cloro

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral context not captured in annotations: some engines block certain countries, which explains why results may vary by model. This context supplements the structured annotations 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?

The description is two sentences, front-loads the core purpose, and devotes the second sentence to the only optional nuance. There is no redundancy or filler.

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

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

For a zero-required-parameter read-only listing tool with an enum-only optional filter, the description covers purpose, the optional filter's effect, and behavioral nuance. No output schema is provided, but the description already states that the output is ISO 3166-1 alpha-2 country codes, so the context is sufficiently complete.

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

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

The input schema already provides full coverage with a clear description for the model parameter and an enum of valid values. The description adds value by explaining why the parameter matters — filtering by engine-specific country support and warning that some engines block certain countries.

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

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

The description clearly states the action 'List' and the specific resource: 'ISO 3166-1 alpha-2 country codes supported for geo-targeting.' This makes the tool's purpose distinct from the sibling list_states and the various scrape_* 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 the main use case — retrieving countries for geo-targeting — and provides clear guidance for the optional model parameter: pass a model to get engine-specific availability. It does not explicitly name alternatives or state when not to use this tool, so it falls just short of a 5.

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

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

Annotations already cover the safety profile: readOnlyHint, idempotent, and non-destructive. The description adds that only state-targeting countries are accepted and that output plugs into scrape tools, but it does not describe result format, pagination, or other behavioral details. Given the annotations, this is acceptable but not outstanding.

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, front-loaded with the core action and resource, and a second sentence for the limitation. No redundant or filler 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?

For a simple single-parameter listing tool, the description covers purpose, the main constraint, and the connection to sibling scrape tools. It does not spell out the exact return shape, but the phrase 'List the state codes' sufficiently implies an array of code values. The absence of an output schema makes this a minor gap only.

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

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

Input schema coverage is 100% and the only parameter, country, already has a clear description and enum. The tool description adds context about how the result is used, but it does not add new parameter-level semantics beyond the schema. Baseline 3 applies because the schema carries the load.

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 a specific action and resource: listing state codes for country-level geo-targeting, and explicitly connects to the 'state' parameter used by scrape tools. It distinguishes itself from siblings like list_countries and scrape_* tools by specifying exactly what it returns and why.

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

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

The description implies when to use this tool: when you need valid state codes for a country before calling a scrape tool. It also states a constraint (only countries with state-level targeting are accepted). It does not explicitly name alternatives or exclude cases, but the context is clear enough.

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

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

Annotations already cover readOnly, openWorld, and non-destructive behavior. The description adds useful operational context: the tool submits a prompt, geo-targets from a chosen country, and can return richer payloads like markdown, search queries, shopping results, and ads. This goes beyond the annotations without contradicting them.

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

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

The description is two sentences with no filler. The primary action and return contents are front-loaded, and the usage rationale is concise. Every sentence earns 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?

For a tool with nested include flags and no output schema, the description covers the core purpose and the main optional response fields. It falls short slightly on explaining the undocumented 'legacy' parameter and exact payload implications, but overall an agent can reasonably select and invoke 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 80%, so most parameters are already documented. The description adds value by naming the optional output types (markdown, search queries, shopping results, ads) and noting state targeting, but it doesn't explain the 'legacy' boolean or individual include subfields, and the schema largely carries the weight.

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 ('Submit a prompt to ChatGPT') and its return value (full answer with text, cited sources, and optional markdown, search queries, shopping results, ads). It also distinguishes this tool from siblings by focusing on ChatGPT and brand/source mentions, making the resource explicit.

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 context for when to use it: 'Use this to see how ChatGPT answers a prompt and which brands/sources it mentions.' It doesn't explicitly name alternatives like scrape_gemini or state when not to use it, but the ChatGPT-specific framing and sibling list make the intended scope 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?

The annotations already indicate a safe, read-only, non-destructive operation, so less descriptive burden is placed on the tool. The description adds useful behavioral context: requests are geographically targeted from a chosen country/state and the response includes cited sources, which is not inferable from the annotations.

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: two sentences with no filler. It front-loads the primary action and result, then adds a focused use-case sentence that intrinsically justifies the tool.

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

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

The core purpose, security profile, and return value are covered. The nested include flags and helper tools list_countries/list_states are documented in the schema rather than the description, which is acceptable given 100% schema coverage and read-only annotations.

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

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

The input schema has 100% coverage with clear descriptions for all four parameters. The description adds no parameter-specific guidance beyond what the schema already provides, so the baseline score of 3 is appropriate.

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

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

The description names a specific verb (submit a prompt) and resource (Microsoft Copilot) and adds distinguishing scope: geo-targeting by country/state and returning cited sources. It clearly separates this tool from sibling scrapers by model and from list_countries/list_states by focusing on actual generation.

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

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

The description provides a concrete use case: 'Use this to see how Microsoft Copilot answers a prompt and which brands/sources it mentions.' It does not explicitly say when not to use it or name alternatives like scrape_chatgpt, but the intended 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?

Annotations already cover the read-only, non-destructive profile. The description adds context about geo-targeting and the cited-sources output, which is useful beyond the annotations, but it does not go deeper into response size, rate-limit behavior, or the effects of the 'include' flags.

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 deliver the core action, output, and the exact research use case. There is no filler or repetition; each clause earns 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?

For a 4-parameter tool with no output schema, the description gives a solid overall picture: input prompt, geo-targeting, optional state, and the main output. It does not describe the response structure in detail, but that is mitigated by the 100% schema coverage and the fact that the output's 'cited sources' nature is stated.

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 parameter semantics are already fully documented. The description mainly restates the idea of a prompt and geo-targeting without adding meaning beyond the schema's own 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?

States a specific verb and resource: 'Submit a prompt to Google Gemini' — and explicitly describes the returned artifact: 'the answer with cited sources.' It names the model being scraped, which distinguishes it from sibling scrapers like scrape_chatgpt and scrape_perplexity.

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 phrase 'Use this to see how Google Gemini answers a prompt and which brands/sources it mentions' gives clear when-to-use context for brand/source research. It does not explicitly state when not to use it or name alternatives, but the model-specific framing combined with sibling names provides usable routing guidance.

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

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

Annotations already mark it read-only and non-destructive; the description adds meaningful behavioral detail: it emulates different devices, supports multi-page results, geo-targets via country/gl, and discloses that URL mode overrides structured fields. There is no contradiction with the annotations.

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 with the core action, followed by mode selection, device support, and optional features. Every sentence contributes useful information without redundancy.

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

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

Given the tool's complexity — 10 parameters, nested include objects, multiple modes, and no output schema — the description covers invocation well. It lacks explicit output-structure details beyond "organic results," but the essential selection and calling context is present.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by explicitly tying include.aioverview and include.paaAioverview to their extraction behavior and by summarizing the structured vs. URL ownership semantics, which helps the agent understand parameter interactions 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 names a specific verb and resource: "Run a Google search" that "return[s] organic results." It clearly separates itself from siblings like scrape_google_news and scrape_google_ai_mode by focusing on standard Google organic results with optional AI Overview extraction.

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 guidance on choosing between the two invocation modes — structured query/country vs. a full URL — and explains when the URL owns query, location, and pagination. It does not explicitly compare against sibling tools, but the mode-selection guidance is clear enough for correct invocation.

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

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

Annotations already cover read-only/open-world/non-destructive behavior. The description adds useful behavioral context by stating that the tool returns an AI answer with cited sources and supports location/UULE targeting and desktop/mobile emulation, which goes beyond the annotation fields.

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 focused sentences with front-loaded purpose and minimal waste. The first sentence establishes what the tool does and returns; the second summarizes key targeting/emulation capabilities.

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 detailed schema plus annotations cover most operational details, and the description explains the core return value ('AI answer with cited sources'). Minor contextual gaps remain around the optional include payload options, but the schema partially covers those and no output schema exists.

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

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

Schema description coverage is 100%, so the baseline is 3. The description's mention of 'location or UULE targeting' and 'desktop/mobile emulation' restates what the schema already documents and does not add deeper semantic guidance beyond the parameters.

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

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

The description names a specific verb+resource: 'Submit a prompt to Google AI Mode' and a concrete expected output: 'return the AI answer with cited sources.' This distinguishes it from sibling scraper tools by naming the exact Google product and output type.

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 for when to use this tool: when an agent needs a Google AI Mode response, optionally geo-targeted by country/location/UULE or with device emulation. It does not explicitly name alternatives or exclusion cases, but the scenario is clear enough among the sibling scraper 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?

Annotations already declare readOnlyHint and destructiveHint false, so safety profile is covered. The description adds behavioral context like country-specific searches, device emulation, and multi-page fetching, which are useful. However, it does not disclose response format, pagination behavior, or potential rate limits, leaving gaps beyond what annotations provide.

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

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

The description is two sentences, front-loading the core purpose and key capabilities. There is no fluff or repetition; every phrase adds value. It is appropriately sized for a tool with a well-covered 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 a complex 7-parameter tool with a nested include object and no output schema, this description is incomplete. It does not explain the return structure, pagination semantics, or the purpose of the include flags. An agent would need to infer response details, which is a significant gap for a scraping tool.

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

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

Schema description coverage is 100%, so all parameters have documented meaning. The description does not add any parameter-specific details beyond what the schema already provides; it only loosely implies country and pages via phrasing. Baseline 3 is appropriate as the schema carries full parameter documentation.

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 runs a Google News search and returns news results, with support for device emulation and multi-page results. It distinguishes itself from siblings by naming the Google News resource explicitly, though it does not directly reference an alternative like scrape_google. The purpose is unambiguous given the tool name.

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

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

The description provides no guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. There is no reference to siblings or conditions like 'use this for news-specific queries vs. general web search', leaving the agent without routing 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?

Annotations already cover readOnlyHint, openWorldHint, and destructiveHint, so the bar is lower. The description adds useful behavioral context beyond those annotations: the prompt is geo-targeted and the response includes cited sources and brand/source mentions. It does not discuss rate limits or payload details, but those are partially covered by the input schema.

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

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

The description is two tight sentences that immediately state what the tool does and when to use it. There is no filler, repetition, or tangential detail.

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

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

Given the schema already documents all parameters and the annotations cover safety traits, the description does enough to orient an agent. It clearly explains result content (answer with cited sources), but does not specify response formatting or the support/country validation workflow beyond the schema. Still, the combination of schema, annotations, and description 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?

The input schema provides 100% coverage of parameters with descriptions for prompt, country, state, and include. The tool description adds general behavioral context (geo-targeting and cited sources) but no additional parameter-specific meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly names the action (submit/prompt), the target resource (Grok/xAI), and the differentiating characteristic (geo-targeting country/state, returning cited sources). It distinguishes itself from sibling scrape_* tools by specifying Grok as the source.

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?

"Use this to see how Grok (xAI) answers a prompt and which brands/sources it mentions" gives a clear reason to choose this tool. It does not explicitly say when not to use it or name alternatives, but the usage context is strong enough to avoid major confusion with sibling tools.

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

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

Annotations already declare readOnlyHint and destructiveHint, but the description adds behavioral context by explaining that the request is geo-targeted from a chosen country/state and that the response includes cited sources and mentioned brands. There is no contradiction between the description and annotations.

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

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

The description is two sentences, front-loaded with the core action and outcome, and includes a practical usage note. Every sentence carries useful guidance; there is no filler or repetition.

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

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

There is no output schema, but the description covers the essential return value: the answer with cited sources and mentioned brands/sources. It is reasonably complete for an agent deciding whether and how to call the tool, though it could mention the optional include flags or geo-targeting constraints more directly.

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 documents all parameters at 100% coverage, including prompt, country, state, and include flags, so the baseline is 3. The description only lightly reinforces the country/state geo-targeting idea; it does not add meaningful new parameter semantics 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 specific verb ('Submit'), a specific resource (Perplexity), and a clear outcome: return the answer with cited sources. It also distinguishes this tool from sibling scrape tools by naming Perplexity explicitly and situating the intended use case.

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 a clear use case: 'Use this to see how Perplexity answers a prompt and which brands/sources it mentions.' This tells the agent when to choose this tool, but it does not explicitly mention when not to use it or compare it against direct alternatives like scrape_chatgpt or scrape_copilot.

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