AIsa AI Visibility

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

Annotations already declare destructiveHint=true and openWorldHint=true, so safety is covered. The description adds valuable behavior: independence of items (one failure doesn't cancel others) and per-call billing. These are not derivable from annotations and help the agent set expectations.

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

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

Three short sentences with zero filler. The action and limit are front-loaded. The phrase 'tool-router's batch_use' is redundant since it restates the tool name, but it's a minor flaw. Overall it is concise and well-structured.

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

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

With annotations covering destructive behavior and an output schema presumably describing results, the description covers the key operational aspects: concurrency limit, independence, and billing. It doesn't mention error reporting formats, but those likely live in the output schema. It is sufficiently complete for a batch 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 the schema fully documents each parameter. The description adds no parameter-specific details. The calls parameter's description already explains the structure and batching context, so the baseline of 3 applies; the description doesn't need to compensate.

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 action (execute) and resource (operations) with a concrete limit (up to 20) and concurrency. It doesn't explicitly name the sibling 'use' for single operations, but the distinction is clear enough from the concurrency and limit. The redundancy of 'tool-router's batch_use' is minor.

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

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

The description gives no guidance on when to use this tool versus the sibling 'use' tool. The schema note about 'steps at the same execution_level of a plan go in one batch' is helpful, but it lives in the schema, not the description. The description only implies batching via concurrency but doesn't state when to choose it over the single-operation alternative.

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, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is fully covered. The description adds only that the response is JSON with a 'tasks array', which duplicates what the existing output schema already provides, and discloses nothing new about auth, rate limits, or freshness of the model list.

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 purpose is front-loaded in the first sentence, but the second sentence is boilerplate ('As a response of the API server, you will receive JSON-encoded data containing a tasks array with the information specific to the set tasks') that is both redundant with the output schema and awkwardly worded, and 'by calling this API' adds nothing.

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-parameter list endpoint with an output schema, the description is functionally adequate: the agent knows it returns a model list. It is nonetheless incomplete on the one thing that matters for disambiguation here, namely that this returns ChatGPT models specifically rather than the Claude/Gemini/Perplexity sets offered by siblings.

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 tool takes zero parameters, so there is nothing for the description to clarify and the baseline of 4 applies. No parameter-level guidance is needed or expected here.

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 resource ('list of available Chat GPT AI models') and a clear read action, so an agent knows what it returns. However, it never distinguishes this tool from its near-identical siblings (get_dataforseo_ai_claude_llm_responses_models, get_dataforseo_ai_gemini_llm_responses_models, get_dataforseo_ai_perplexity_llm_responses_models), leaving the provider scope to be inferred from the name alone.

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

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

There is no statement of when to call this tool versus the other provider model-list endpoints, nor any mention of the related post_..._llm_responses_live endpoints it presumably feeds. The only usage signal is the implicit 'call this API' phrasing, which gives no context or exclusions.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is fully covered. The description adds nothing beyond that: the 'JSON-encoded tasks array' detail is a return-value statement already supplied by the output schema, with no mention of auth, rate limits, or error behavior.

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

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

Two sentences, front-loaded with the outcome, but they are wordy and redundant ('you will receive ... you will receive'). The second sentence largely restates what the output schema already conveys, so it does not fully earn 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 zero-parameter, read-only listing tool with full annotation coverage and an output schema, the description covers what an agent needs to select and call it. Only the missing link to the live siblings keeps it short of 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 tool takes zero parameters, so per the rubric the baseline is 4. There is nothing for the description to disambiguate, and the empty schema is self-evident.

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: it returns the list of available Claude AI models. The 'Claude' qualifier implicitly separates it from its ChatGPT/Gemini/Perplexity model-list siblings, though it never names them explicitly. Clear purpose but no sibling differentiation stated in the text.

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 only says what happens when you call it ('you will receive the list...'). There is no when-to-use guidance, no mention of the live/query siblings it feeds into, and no exclusions. The agent must infer that this is a discovery call made before invoking post_dataforseo_ai_claude_llm_responses_live.

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, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds only that the server returns JSON-encoded data, which duplicates what the output schema already conveys and offers no extra behavioral context such as cost, rate limits, or model-availability caveats.

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, but the second is padded and awkwardly phrased ("information specific to the set tasks"), which is boilerplate rather than useful front-loaded content. The core fact, that this returns the Gemini model list, is stated first, which is the one redeeming structural trait.

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 no-parameter list endpoint with an output schema and full annotation coverage, the description is minimally sufficient. However, its return-value sentence is vague and arguably misleading for a models list (a generic "tasks array" rather than model entries), so it neither adds value nor stays quiet.

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 tool takes zero parameters, so the baseline is 4. There is no parameter semantics to explain and the description does not mislead on inputs.

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: retrieving the list of available Gemini AI models. It implicitly distinguishes itself from the ChatGPT, Claude, and Perplexity model-listing siblings by naming Gemini, but never explicitly frames itself as the Gemini-specific alternative.

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

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

There is no indication of when to call this versus the sibling model-list tools (chat_gpt, claude, perplexity) or when it is a prerequisite for the corresponding live endpoints. The agent must infer the routing entirely from the tool name.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds nothing beyond that – no note on static vs dynamic data, caching, or what the codes are used for downstream.

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?

A single short sentence with no padding. It could be tightened (dropping the tautological 'by calling this API' and leading with the resource), but it is not bloated.

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 an output schema present, return values need not be described, and annotations cover the safety profile. For a zero-parameter lookup tool the description is minimal but functionally sufficient, though it could hint at how the returned languages relate to sibling scraper 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?

The tool takes zero parameters, so there is nothing for the description to clarify; the baseline for a parameterless tool is 4. No misleading parameter claims are made.

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 identifies the resource (list of languages) but does so by nearly restating the title 'Gemini LLM Scraper Languages List'. The verb is passive ('You will receive') rather than a clear action, and it never distinguishes itself from the sibling locations-list 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?

There is no when-to-use guidance, no mention of prerequisites or call ordering, and no reference to alternatives such as get_dataforseo_ai_gemini_llm_scraper_locations or the scraper live endpoints that would consume these language codes.

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=true, idempotentHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is fully covered by structured data. The description adds only the country-filter context and says nothing about completeness of the returned list, pagination, or coverage — leaving the annotation bar unmet only marginally.

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 short sentences with the result statement front-loaded. The phrasing 'You will receive the list of locations by this API call' is slightly padded but not wasteful enough to penalize heavily.

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 zero parameters, an output schema present, and annotations covering the safety profile, the description only needs to frame what the tool provides and why. It does that adequately, though it omits the scraper/Gemini context that would make the tool's scope unambiguous.

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 tool takes zero parameters, so the baseline is 4; there is nothing for the description to document beyond the empty schema it supplies.

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 returns a list of locations. It does not, however, distinguish this tool from close siblings such as get_dataforseo_ai_gemini_llm_scraper_languages or the various *_locales tools, so an agent must rely on the tool name to disambiguate.

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?

'You can filter the list of locations by country when setting a task' implies the downstream purpose (obtaining valid location values for task configuration), but there is no explicit when-to-use guidance, no prerequisite statement, and no routing to an alternative sibling tool.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds only 'full list', which is minimal behavioral context and does not disclose auth needs, rate limits, caching, or pagination. With annotations covering the safety profile, little value is added beyond the name.

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

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

A single front-loaded sentence with no wasted words. It states the resource and scope immediately.

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?

Output schema exists, so return values need not be explained, and annotations cover safety. The description is sufficient for a parameterless lookup, though it could have better connected this endpoint to its consumers among the sibling post_* keyword 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?

Zero parameters, so the schema cannot be a source of parameter confusion; baseline for 0 params is 4. The description correctly does not invent parameter 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?

States a specific verb (get) and resource (full list of locations and languages) scoped to the AI Keyword Data API, which distinguishes it from similarly named locale tools such as get_dataforseo_ai_llm_mentions_locales. It does not explicitly name alternatives, so 4 rather than 5.

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?

Offers no when-to-use, prerequisites, or alternatives. The phrase 'Using this endpoint' is introductory, not guidance. An agent must infer that this is a discovery call to run before using AI keyword data endpoints like post_dataforseo_ai_keyword_volume_live.

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 declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds no behavioral context beyond these annotations—it does not state that it returns a static reference list, whether authentication is required, or any other operational detail.

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

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

The description is a single sentence, but it opens with filler ('Here you will find all the necessary information') rather than leading with the specific purpose. It is concise but not optimally 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 simple metadata nature of the tool, an output schema, and rich annotations, the description is minimally adequate. However, it fails to specify which LLM Mentions endpoints the filters apply to or what kind of filter information is returned, leaving scope ambiguous.

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 tool takes no parameters, so the baseline is 4. The description does not need to explain parameters, and the schema is empty.

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 that it provides information about filters for the AI Optimization LLM Mentions API, but it is vague and essentially restates the title. It lacks a specific verb and does not distinguish this tool from sibling endpoints that also relate to LLM Mentions.

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

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

No guidance is given on when or why to use this tool versus alternatives. It does not mention prerequisites, when to call it, or how it relates to the sibling live endpoints.

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 readOnly, idempotent, non-destructive, open-world, so the safety profile is covered. The description adds only that the list is 'full', with no note on whether this is static reference data, caching, or auth requirements. With annotations carrying the behavioral burden, a 3 is appropriate.

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?

A single sentence with no padding or redundancy. The lead-in 'Using this endpoint you can' is mildly boilerplate, but the content is front-loaded and compact.

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-parameter reference lookup backed by an output schema, the description covers the essentials: what the list contains and which API it belongs to. Return format is handled by the output schema, so nothing critical is missing.

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 tool takes no parameters, so the baseline of 4 applies. There is nothing for the description to clarify beyond confirming the endpoint requires no input.

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 (get) and resource (full list of locations and languages for the LLM Mentions API), so the agent knows exactly what it returns. It does not, however, distinguish itself from similar siblings like get_dataforseo_ai_keyword_locales or get_dataforseo_ai_gemini_llm_scraper_languages/locations, so an agent could second-guess which locale set applies.

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 only asserts what the endpoint returns; it gives no when-to-use guidance, no prerequisites, and no alternatives. The implied use (discover valid locales before calling the mentions endpoints) must be inferred entirely by 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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive, so the safety profile is fully covered. The description adds that the payload is JSON-encoded with a tasks array, but since an output schema exists this adds only marginal context beyond the structured data.

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 first sentence carries the purpose, but it opens with filler ('You will receive... by calling this API'). The second sentence about a tasks array is generic boilerplate that duplicates what the output schema already provides.

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 zero-parameter read endpoint with an output schema and full annotation coverage, the description is essentially sufficient — the return values need not be explained. Only the lack of when-to-use framing keeps it short of full 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 tool takes zero parameters, so there is nothing for the description to disambiguate. Baseline of 4 applies for a parameter-less tool.

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 resource and action: returning the list of available Perplexity AI models. The entity (Perplexity) implicitly distinguishes it from the ChatGPT/Claude/Gemini model-list siblings, but the text itself does not explicitly compare or exclude 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?

There is no guidance on when to call this versus the sibling model-list endpoints or the live responses endpoints. Usage is only implied by the tool's purpose, with no prerequisites, sequencing, or exclusion 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful context: it is free, stops before any spend, and explains how price.model varies (quoted, list, dynamic, composed). It also clarifies that suggested_max_price_usd has headroom. This goes beyond the annotation flags and gives the agent a clear model of what happens.

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

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

The description is moderately long but well structured: it opens with the core purpose, then explains the price model in a dedicated paragraph. No redundancy or filler. It front-loads the most critical information (contract fields) and then gives necessary detail about price semantics. Slightly dense but not 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?

The tool has an output schema, so return values need no description. The description covers the key behavioral aspects (no spend, pricing models, max_price headroom) and clarifies edge cases like routes without a price. For a read-only informational tool, this is complete enough 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 description coverage is 100%, so parameters are already documented. The description adds some nuance, such as how arguments affect pricing and that required parameters may be needed before a price can be quoted. It also clarifies with_quote's purpose (one round trip, spends nothing). These are useful but not essential given the schema's completeness.

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

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

The description states a specific purpose: returning the full contract of one or more operations, including schemas, read_only, side_effects, price, and known_pitfalls. It clearly distinguishes this from executing operations (use, batch_use) and from discovery (search, list_categories). The verb 'get' and the noun 'details' align with the title, and the first sentence is 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 implies the tool is used to assess an operation before spending (e.g., 'A quote authenticates like a call but stops before any spend'), and the schema says 'One operation_id from search', hinting at a flow. However, it never explicitly states when to choose this over siblings like use or search, nor does it give exclusions. The guidance is implied, not stated.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context beyond annotations: the tool is free, requires no key, and can direct users to a category-specific endpoint that lists tools directly rather than through search.

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

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

The description is moderately detailed but every sentence adds useful information: output scope, cost/auth, sibling differentiation, and endpoint usage. It is slightly longer than strictly necessary but remains well-structured and front-loaded with the core purpose.

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

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

For a zero-parameter, read-only tool with an output schema and safety annotations, the description is complete. It covers what the tool returns, the free/no-key access model, and provides the category endpoint for specialized use, leaving no essential gap for an agent to call it correctly.

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

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

The tool has zero parameters, so the baseline is 4. The description includes a <category> placeholder only in the endpoint examples, not as a tool parameter, which is appropriate supplementary guidance rather than a parameter-semantics gap.

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 what the tool does: it presents the AIsa catalogue at a glance, including categories, servers, tool counts, and a dedicated category endpoint. It also distinguishes itself from search by explaining that the endpoint lists tools directly instead of via 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 usage context: use list_categories for a catalogue overview, and use the provided endpoint when you only need one category. It explicitly contrasts with search ('instead of via search') and notes tool-router has no equivalent, although it does not exhaustively cover all sibling alternatives.

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

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

The description implies a safe read operation ('retrieve structured responses'), but annotations declare readOnlyHint=false and destructiveHint=true. It also omits live-generation cost implications, open-world web search behavior, and non-idempotency.

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

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

One clear sentence with no wasted words, and the purpose is front-loaded. It is extremely thin, but it is not bloated or poorly structured.

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

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

For a complex live ChatGPT generation tool with a nested request array, destructive/open-world annotations, and many sibling endpoints, the description is too minimal. The output schema covers returns, but selection guidance, prerequisites, and behavioral caveats are absent.

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 no meaning beyond 'input parameters'. Although nested schema fields have rich descriptions, the sole top-level required parameter (body) is undocumented in both the description and the schema's top-level coverage, so the description fails to compensate for the request-construction complexity.

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: retrieve structured responses from a ChatGPT model. However, it does not distinguish this tool from sibling LLM response endpoints for Claude, Gemini, or Perplexity beyond naming ChatGPT, nor from the ChatGPT scraper 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?

Provides no explicit when-to-use, when-not-to-use, prerequisites, or alternatives. The phrase 'based on the input parameters' only restates that the tool takes parameters.

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 destructiveHint=true, readOnlyHint=false, and openWorldHint=true, so the safety profile is covered by structured data. The description adds nothing behavioral — it never explains what 'live' implies (immediate task execution, credit consumption), does not address the destructive/non-idempotent posture, and gives no rate-limit or cost context. Minimal value beyond 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?

Two compact sentences, front-loaded, with no filler or redundancy. Nothing needs to be cut; the issue is under-specification rather than 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?

An output schema exists so returns need not be described, but for a POST live-scraper with a nested array body and destructive/non-idempotent annotations the description is far too thin. It omits how the array of request objects works, the required keyword, and how it relates to the near-identical sibling tools, leaving the agent unable to invoke it confidently.

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?

Coverage is effectively 0% for the single top-level 'body' parameter, so the description must compensate. It names only location and language as modifiers and omits the actual required field (keyword), plus tag and force_web_search, and gives no format or required/optional guidance. It does not fill the gap the schema leaves at the parameter level.

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 the resource (ChatGPT LLM scraper) and that it returns 'results from ChatGPT searches,' but the verb 'provides results' is generic and largely restates the title. It does not distinguish this tool from siblings like post_dataforseo_ai_chat_gpt_llm_scraper_live_html or post_dataforseo_ai_chat_gpt_llm_responses_live, so an agent cannot tell which ChatGPT variant to pick.

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 when-to-use guidance is given. It mentions that results vary by location and language, but never says when to prefer this over the HTML variant or the responses endpoint, nor any prerequisites for calling it. Usage must be inferred entirely from the name.

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

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

Annotations declare readOnlyHint=false and destructiveHint=true, but the description describes only a read-only retrieval of a raw HTML page. This is a direct contradiction, so the description misrepresents the tool's behavioral profile.

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?

Single sentence, front-loaded with the tool's purpose, with no padding or redundancy.

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

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

Given a required nested array body, several optional parameters, an output schema, and contradictory annotations, the description is far too thin. It neither explains the array payload nor addresses the destructive/read-only inconsistency.

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?

Top-level schema description coverage is 0%; the description only repeats 'keyword, language, and location' and omits the array-of-objects structure, the optional tag, expand_citations, and force_web_search fields. It does not compensate for the low coverage.

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 ('provides') and resource ('raw HTML page of the results') scoped to keyword, language, and location. It distinguishes from the structured-data sibling by specifying 'raw HTML page', but does not name that sibling or otherwise route the agent explicitly.

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 when-to-use guidance, no alternatives, no prerequisites or exclusions. The agent must infer that this is the HTML variant of the ChatGPT scraper from the name alone.

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

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

Annotations already declare readOnlyHint=false, destructiveHint=true and openWorldHint=true, so the description's only job is to add context beyond that. It adds none: no mention that this is a billed/consumptive live call, no latency or quota expectations, no note that web_search and use_reasoning materially change cost and required max_output_tokens. The word 'retrieve' sits awkwardly against destructiveHint=true and openWorldHint=true, though it does not explicitly claim a read-only 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?

A single sentence with the endpoint concept front-loaded, so nothing is padded. However 'based on the input parameters' is pure filler that consumes the back half of the sentence without informing the reader.

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 a large nested request body, model-specific feature flags (web_search, use_reasoning, message_chain) and real cost implications, one generic sentence is inadequate. An output schema exists so return values need not be described, but the missing billing/consumption and model-listing prerequisites leave the definition under-specified.

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 reported as 0% for the single top-level 'body' parameter, and the description does nothing to compensate — it never explains that body is an array of request objects that are executed together, nor does it characterize model_name, user_prompt or the mutual exclusions. The nested schema fields are in fact richly documented, but an agent gets no help from the description on the top-level wrapper, which is the actual gap.

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 concrete verb ('retrieve') and resource ('structured responses from a specific Claude model'), which is enough to tell it apart from the GPT/Gemini/Perplexity response siblings that appear in the namespace. It stops short of explicitly stating the sibling relationship or that the model must be chosen from a separate Models endpoint, but the core purpose is 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?

There is no guidance on when to use this live endpoint versus the sibling 'live' variants for other model families, versus the corresponding models-listing endpoint, or versus batch_use. The trailing clause 'based on the input parameters' carries no routing information.

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

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

Annotations already declare openWorldHint=true, destructiveHint=true, and idempotentHint=false, so the safety profile is covered by structured data. The description adds nothing beyond that: no cost implications (the web_search schema note mentions pricing), no rate limits, no auth requirements, and no statement of what 'live' implies versus batch.

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?

A single front-loaded sentence with no filler, which is appropriately terse. It is arguably under-specified rather than bloated, but as pure conciseness it is clean.

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

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

The tool has a complex nested request body (prompt, model_name, message_chain, temperature, reasoning, token limits) and a POST with destructive/non-idempotent annotations. One generic sentence is not enough to orient an agent; output schema existence excuses explaining return values, but nothing else about this multi-field request is covered.

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?

Top-level schema description coverage is 0% – the sole parameter 'body' is an array of request objects with no description at the parameter level. The description says only 'based on the input parameters,' which adds no meaning; it never explains that body accepts a list of prompts/tasks. The nested item fields are richly documented in the schema, but the description itself does not compensate for the top-level gap.

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 ('retrieve structured responses from a specific Gemini AI model'), so the agent knows this is a generation/retrieval call against Gemini. It implicitly distinguishes itself from the ChatGPT/Claude/Perplexity live-response siblings by naming Gemini, but never explicitly contrasts them or explains the live-vs-scraper distinction.

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

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

There is no when-to-use guidance, no exclusions, and no mention of the alternatives (the ChatGPT/Claude/Perplexity live-response endpoints or the Gemini models endpoint). The only routing hint ('you can receive the list of available LLM models by making a separate request...') lives in the nested schema, not the description.

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=false, destructiveHint=true, openWorldHint=true, and idempotentHint=false, but the description adds nothing behavioral on top of that — it says nothing about cost per request, live-vs-task semantics, rate limits, or why a 'scraper' would be flagged destructive. For a POST live endpoint with an unusual annotation profile, this is a real gap.

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

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

Two short sentences with no padding and the core purpose front-loaded. It is efficient, though the second sentence mostly restates filtering dimensions already implied by the tool name.

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?

An output schema exists, so return values need not be described, but the tool has a nested array-of-objects body with multiple anyOf constraints and no usage or behavioral context is supplied to compensate. Given the complexity and the long list of near-duplicate Gemini siblings, this description is insufficient for correct selection and 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 description coverage is reported as 0%, so the description must compensate, and it only alludes to 'location', 'language', and 'keyword' generically. It does not explain the anyOf requirement (exactly one of location_name/location_code/location_coordinate and one of language_name/language_code), the tag field, or the %-decoding rules for keyword.

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 resource (Gemini LLM scraper) and says it returns structured results filtered by location, language, and keyword. However, it never distinguishes this tool from its close siblings post_dataforseo_ai_gemini_llm_responses_live or post_dataforseo_ai_gemini_llm_scraper_live_html, so an agent cannot tell which Gemini endpoint to pick without opening the schemas.

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

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

There is no statement of when to use this tool versus the LLM responses live endpoint, the HTML scraper variant, or any of the other AI optimization tools. The only hint of context is that results depend on location/language/keyword, which is a description of output, not of usage conditions.

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

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

Annotations already declare openWorldHint=true, non-idempotent and destructiveHint=true, so the safety profile is structured. The description usefully adds the return format (raw HTML page) but does not explain the live/POST nature, cost implications, rate limits, or why a scraper is marked destructive, leaving an odd gap the agent must guess at.

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?

A single front-loaded sentence with no filler; the keyword/language/location requirements appear immediately. It loses a point only because the opening clause mostly restates the tool name before delivering value.

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

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

An output schema exists so return values need not be described, and annotations carry the safety profile. However, the description omits the HTML-vs-structured-variant selection rationale and any cost/live-execution context, which matters for a paid open-world scraper.

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?

Coverage for the top-level 'body' parameter is reported as 0%, but the nested item schema documents keyword, language, location, tag and expand_citations at length. The description only echoes keyword/language/location and omits expand_citations and tag, so it adds little beyond the schema's own field docs.

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 operation and resource: it 'provides a raw HTML page of the results' for a keyword, language and location. The 'raw HTML' framing distinguishes it from the structured-output siblings (post_dataforseo_ai_gemini_llm_scraper_live, post_dataforseo_ai_chat_gpt_llm_scraper_live_html), but it never explicitly names or contrasts those siblings.

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

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

There is no statement of when to choose the HTML endpoint over the plain scraper or the LLM responses endpoints. The only indirect guidance is the pointer to 'see the List of Languages'/'see the List of Locations', which routes the agent to lookup tools but says nothing about selecting this tool.

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

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

Annotations already declare destructiveHint=true, readOnlyHint=false, and openWorldHint=true, so the agent knows this is a non-idempotent live call. The description adds nothing about cost/credit consumption, rate limits, or what makes a 'live' call different; it only describes the data semantics, not the tool's behavior. The read-flavored word 'provides' is mildly at odds with the destructive hint but does not rise to a contradiction.

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?

A single front-loaded sentence with no filler or repetition. It is efficiently sized, though the brevity comes at the cost of substance rather than being a model of tight completeness.

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 nested array body, no parameter guidance, and a crowded sibling set of similar live endpoints, one sentence is insufficient. The existence of an output schema excuses it from explaining return values, but it still needs to say how to call it and when to prefer it over batch_use or other live variants.

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 single body parameter is not explained at all in the description, and the measured schema description coverage is 0%. The description never mentions the keywords array, the location_name/location_code or language_name/language_code requirement, or the 1000-keyword limit, leaving the description to add no parameter meaning.

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: it 'provides search volume data for your target keywords' and clarifies the data is estimated AI-tool usage, distinguishing it from generic SEO volume tools. It does not differentiate itself from the many sibling 'live' AI endpoints or from batch_use, so it stops short of a 5.

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

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

There is no when-to-use guidance, no mention of prerequisites (location/language requirement), and no comparison to alternatives such as batch_use or the other live AI endpoints. The agent must infer usage entirely from the name.

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

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

Beyond the annotations, the description discloses significant operational behavior: the endpoint is 'roughly eight times the flat rate billed', target type errors are rejected, and a rejected request 'still returns HTTP 200' with outcome in tasks[0].status_code. This is rich, non-obvious context that helps an agent interpret failures and cost.

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

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

The description is dense but well-structured, front-loading the core purpose and then adding cost, type constraints, response envelope, and sibling routing. Every major clause earns its place; minor noise like 'Measured at 7.0 KB' and emoji formatting slightly reduces polish but does not hurt comprehension.

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

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

For a live aggregate endpoint, the description covers the key operational facts: response envelope, HTTP behavior, cost, target format, filter source, and sibling alternatives. Since an output schema exists and the input schema documents the remaining body fields, nothing essential is missing 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?

The description adds critical parameter semantics not in the schema: target must be an array of objects with domain or keyword objects, and it gives exact rejection messages for invalid forms. It also points to the available-filters endpoint for filter fields. The schema already documents most nested fields, so the description does not need to repeat them.

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 opens with a specific outcome: 'How often assistants mention your target, as totals rather than individual answers', naming both the resource and the distinction from detailed mentions. It then explicitly differentiates the tool from sibling endpoints, so an agent can identify it without opening schemas.

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 routing guidance: this is 'the headline visibility number', while post_dataforseo_ai_llm_mentions_search_live 'shows the answers behind it' and cross_metrics_live 'compares several targets at once'. It implies aggregate-total usage rather than detail retrieval, though it does not explicitly state when not to use it.

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

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

The description adds substantial behavioral context beyond the annotations: pricing ($0.101, roughly eight times the flat rate), response envelope behavior (`tasks[0].result`, `tasks[0].status_code`), and the fact that a rejected request still returns HTTP 200. It also identifies the source of valid filter fields. Nothing in the description contradicts the `destructiveHint: true` annotation.

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 shape, then the high-cost warning and error-envelope behavior. Every sentence earns its place, and the emoji callouts make the two most important pitfalls (cost and target shape) visually salient without bloating the 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 complex endpoint with nested arrays inside a single `body` parameter, the description covers the key operational facts an agent needs: target shape differences, cost, rejection behavior, envelope structure, filter source, and the relevant sibling for comparison. An output schema exists, so omitting detailed return-value documentation is acceptable.

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 schema description coverage reported at 0%, the description compensates strongly by clarifying that `target` must be an array of `{"domain": ...}` or `{"keyword": ...}` objects and that bare strings or arrays of strings are rejected with specific error messages. It also connects `aggregation_key` to the grouping behavior and points to `get_dataforseo_ai_llm_mentions_available_filters` for valid filter fields.

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 first sentence states a specific action ('Compares'), resource ('mention metrics across several targets'), and grouping mechanism (`aggregation_key`), so an agent immediately understands the endpoint's purpose. It further distinguishes itself from siblings by noting it takes `targets` as a list rather than the single `target` its siblings take.

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 names `post_dataforseo_ai_llm_mentions_aggregated_metrics_live` as the per-target alternative and adds a cost-based decision rule: running that tool once per target costs the same, so 'use this when the comparison itself is the point.' This gives an agent a concrete when-to-use and when-not-to-use guideline.

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 declare destructiveHint=true and readOnlyHint=false, but the description adds substantial behavioral context: the endpoint is 'among the most expensive endpoints in this provider' with a measured cost, the `target` field rejects bare strings and arrays of strings, a rejected request still returns HTTP 200, and pagination uses `search_after_token` not `offset` past the first pages. This goes far 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 dense but front-loaded with the core purpose, then covers cost, target format, envelope, and sibling routing. Every sentence adds value. It is longer than ideal, but the length is justified by the high-stakes cost warning and the non-obvious `target` format. The structure is logical: purpose → return → cost → parameter warning → envelope → alternative.

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 (1 body parameter with many nested fields), the description covers the essential operational facts: cost, target format, envelope parsing, pagination, and the sibling for counts. The output schema exists, so return values need not be enumerated. The description is complete enough for an agent to call this correctly and avoid the most common pitfalls.

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 0%, so the description must compensate. It does explain the critical `target` parameter shape ('array of objects, each `{"domain": "..."}` or `{"keyword": "..."}`') and the filter source. However, it does not enumerate the other parameters (limit, offset, platform, language_code, etc.), leaving the agent to read the schema for those. The description adds meaning for the most error-prone parameter but not all.

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 opens with a specific verb and resource: 'The individual answers in which an assistant mentioned your target.' It clearly distinguishes this from the sibling `post_dataforseo_ai_llm_mentions_aggregated_metrics_live` by stating 'For counts rather than the mentions themselves use...' This is a precise, non-tautological statement of what the tool returns.

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 names the alternative for counts (`post_dataforseo_ai_llm_mentions_aggregated_metrics_live`) and states the condition for choosing it. It also warns about the `target` parameter format, the envelope structure, and the pagination behavior. This is explicit when/when-not 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?

The description adds meaningful non-obvious behavior: response size, cost magnitude, rejection still returning HTTP 200, and the required array-of-objects target shape. It does not contradict the annotations, though it does not explicitly address the destructiveHint=true annotation beyond the cost warning.

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 dense but front-loaded with the core purpose and cost warning. Some phrases are decorative ('the list to pitch') and the `total` / `items` mention is cryptic, but nearly every sentence adds practical 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?

It covers the response envelope, error semantics, response size, cost, and sibling routing, and an output schema exists for return-value details. It does not mention pagination or rate limits, but those are not critical given the schema and the explicit warning about cost.

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 helps with the trickiest part of the body parameter: `target` must be an array of `{domain}` or `{keyword}` objects, and filter fields are sourced from another tool. However, most parameter details already live in the schema, and the description does not compensate for the reported 0% top-level schema coverage beyond target shape and filter references.

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 precise resource ('domains assistants cite most') and ties it to the title's 'Top Domains'. It also differentiates from the sibling top-pages endpoint in the final sentence.

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 says this is 'the list to pitch' and directs page-level queries to post_dataforseo_ai_llm_mentions_top_pages_live. The cost warning also helps an agent decide whether this endpoint is appropriate for casual 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?

The description adds substantial behavioral context beyond the basic annotations: cost warning ($0.101, eight times the flat rate), target array type pitfalls (bare string rejected, array of strings rejected), envelope parsing and status code behavior, and dependency on available_filters endpoint. These are not in the annotations and are critical for correct invocation. No contradiction with 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 long (~300 words) but every sentence holds value: cost, type pitfalls, envelope, filter source, sibling comparison. It is front-loaded with the purpose and cost, then flows into gotchas. It is denser than ideal but appropriate for a complex, expensive tool with multiple pitfalls.

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

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

The tool has an output schema, so return format is covered there. The description addresses all critical operational details: cost, target format, envelope parsing, filter source, and sibling differentiation. Nothing essential for a correct call is missing, and the description even preempts common errors.

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's parameter descriptions are extremely detailed (e.g., target with examples, platform, locations), so the description doesn't need to restate them. However, the description adds essential parameter-specific guidance: target must be an array of objects (and warns against bare string or array of strings) and points to get_dataforseo_ai_llm_mentions_available_filters for filter fields. This compensates meaningfully for the 0% schema coverage in the description.

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 returns the individual pages assistants cite most for a target, with `total` and `items`. It also distinguishes itself from the sibling top_domains endpoint by specifying the use case (which article to update/pitch vs. which publisher). This is a specific verb+resource with clear differentiation.

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

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

It explicitly names the alternative 'post_dataforseo_ai_llm_mentions_top_domains_live' and gives the decision condition: use this when you want to know which article to update or pitch, rather than which publisher. It also provides additional usage guidance on the target array format and the envelope behavior (HTTP 200 even on rejection).

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 declare readOnlyHint=false, destructiveHint=true, and openWorldHint=true, indicating a potentially disruptive write-like operation with external effects. The description says 'retrieve structured responses,' which might suggest a read-only operation, creating a mild tension, though 'live' and 'post' in the name suggest generation. The description adds no context on cost, side effects, rate limits, or authentication. With annotations present, the bar is lower, but the description still misses important behavioral context for a destructive, open-world call.

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 that is front-loaded and free of filler. It could be slightly more informative, but it is appropriately sized for a tool whose parameters are well-documented in the 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 the tool's complexity (LLM generation with many optional parameters), the presence of an output schema (which covers return values), and the destructive/open-world annotations, the description is too thin. It does not mention that this is a paid, potentially rate-limited API call, nor does it help the agent select among sibling LLM endpoints. The description is incomplete for an agent to invoke correctly without inspecting the schema and annotations.

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

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

The input schema has 0% description coverage at the top level (body array), but the nested items have full descriptions for each parameter (tag, top_p, model_name, temperature, user_prompt, message_chain, system_message, max_output_tokens, web_search_country_iso_code). The description adds no parameter semantics beyond what the schema provides. According to the rules, when schema description coverage is high for actual parameters, baseline is 3; here the top-level coverage metric is 0% but effective nested coverage is high, so 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 states a specific verb (retrieve) and resource (structured responses from Perplexity AI model), which is clear enough on its own. However, it does not differentiate from sibling tools like post_dataforseo_ai_chat_gpt_llm_responses_live or post_dataforseo_ai_claude_llm_responses_live, which have near-identical descriptions. The agent must infer the distinction from the name alone.

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 the sibling LLM response endpoints (ChatGPT, Claude, Gemini). It does not mention alternatives, preconditions, or exclusions. The agent is left to infer from model naming in the tool name.

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

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

The description discloses meaningful behavior beyond annotations: search spans the entire AIsa catalogue, candidates may belong to modules other than the current one, multiple providers for the same metric are surfaced, and no API key is required. This gives the agent a clear picture of scope and output behavior.

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

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

The description is front-loaded with the core purpose and is dense with useful information: scope, no-auth requirement, response shape, and relationship to the catalogue. Each sentence adds operational value, and the structure makes the tool's behavior predictable.

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 discovery tool with output schema, the description is unusually complete: it explains the response modalities, candidate fields, direct pass-through to `use`, full-catalogue search behavior, and cross-provider visibility. An agent has enough context 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 coverage is 100%, so the schema already documents all parameters. The description adds value by explaining that the category parameter is not a hard boundary—search spans the full catalogue—and that queries are plain-language task descriptions, which clarifies how to use the tool effectively.

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 finds AIsa data operations across many categories via a plain-language query. It distinguishes itself from siblings like get_details and use by emphasizing that search covers the full catalogue, not just the pinned category.

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 conveys when to use search: when you need to discover operations across the full catalogue, even those not in the current tools/list. It also implicitly contrasts with get_details by noting that returned candidates already include input_schema, so they can be passed directly to `use` without an extra call.

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 provide readOnly=false, openWorldHint=true, and destructiveHint=true. The description adds valuable behavior beyond that: billing per call, the BatchCallResult response shape, and the error structure with retryable status. It does not spell out side effects, but the destructive flag is already carried by annotations, so the additional context 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?

Three short sentences, each earning its place: purpose, cost, response shape, and routing guidance. Key behavioral facts are front-loaded, and nothing is redundant with the schema or annotations.

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 an output schema exists and all parameters have descriptions, the tool description is complete enough for correct invocation. It covers cost, return/error contracts, and how routing to this tool differs from calling pinned tools directly, leaving no practical gap.

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

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

Schema description coverage is 100%, and each parameter is already clearly documented: operation_id as returned by search, search_id provenance, arguments matching input_schema, and max_price_usd as a spend guard. The description does not need to add parameter detail, 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 opens with 'Execute one AIsa operation,' a specific verb+resource statement. The word 'one' distinguishes it from the sibling batch_use, and the closing note distinguishes it from calling pinned tools directly. An agent can tell what this tool is for immediately.

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 states when to use the tool: 'this is the way to call anything found through search.' It also gives the alternative: 'Pinned tools in tools/list can also be called directly.' This is clear when-versus-alternative guidance with no ambiguity.

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