AIsa Content & Sentiment

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 cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false), so the description needn't explain risks. However, it adds little behavioral context beyond what annotations provide – it doesn't say if the response is static or dynamic, nor how the filters relate to the search endpoints (e.g., whether they must be passed as parameters).

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 and free of redundancy. It could be more specific about the nature of the returned filters, but it's efficient.

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

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

Given the tool has no parameters, an output schema exists, and annotations cover behavior, the description is nearly sufficient. The only minor gap is not explaining how the filters map to the Content Analysis endpoints' query parameters.

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

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

There are zero parameters, so the baseline is 4. The description correctly conveys that no inputs are required, though it doesn't add any parameter-related meaning beyond that.

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 ('filters that can be used with Content Analysis API endpoints') and implies the verb 'find'/'list'. It distinguishes from siblings like get_dataforseo_content_categories and get_dataforseo_content_languages by naming 'filters', though it doesn't explicitly differentiate from these sibling metadata 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 says 'necessary information about filters' but gives no guidance on when to call this tool versus alternatives, whether it's a prerequisite for other endpoints, or how the returned filters should be used.

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 structurally. The description adds the useful fact that the category set is Google's taxonomy and that a CSV download exists, but the CSV sentence is dangling ('by this link' with no URL supplied), which adds confusion rather than clarity.

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, front-loaded on the resource, which is good. But the opening sentence is filler and the closing sentence references a link that is not present, so not 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?

With an output schema present, the description need not explain return values, and with zero parameters there are no input semantics to cover. It is nearly complete for a simple read-only list endpoint; the only gap is the unactionable CSV reference and the missing routing versus 'list_categories'.

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 and the baseline of 4 applies. Schema description coverage is 100% and the empty object confirms no inputs are expected.

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?

It names the resource clearly ('full list of available categories') and adds the origin ('Google product and service categories'), which mostly separates it from generic siblings. It does not, however, distinguish itself from the sibling 'list_categories', which an agent could easily pick instead.

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 endpoint versus the near-identical 'list_categories' sibling, nor any prerequisite or context. Usage must be inferred purely from the title and resource 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, openWorldHint, and destructiveHint=false, so the safety profile is fully covered. The description's only added behavioral claim is that the response is JSON with a 'tasks' array, which duplicates what the output schema already provides rather than adding new context such as static-ness, caching, or rate 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?

It is short and front-loaded, but the second sentence describing the JSON 'tasks' array is filler since an output schema exists; that sentence consumes half the description without earning its place.

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

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

For a zero-parameter lookup with an output schema and full annotation coverage, the bar is low and the essentials are technically present. However, the description explains only the return envelope (already in the output schema) and omits the actual purpose and scope of the language list, leaving it minimal rather than 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 by rule the baseline is 4. The description appropriately says nothing about parameters, and there is nothing for it to clarify.

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 indicates the tool returns a list of languages, matching the title, but it phrases this as 'you will receive the list of languages by calling this API,' which borders on restating the call itself rather than stating the purpose. It does not differentiate this lookup from the analogous sibling lookups (get_dataforseo_content_locations, get_dataforseo_content_categories) or explain what the languages are used for in the Content Analysis API.

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

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

There is no when-to-use guidance and no mention of alternatives, despite a cluster of sibling lookup tools that an agent must choose between. The agent is left to infer that this is a static enumeration of supported languages.

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 nothing beyond this – no note that it is a static reference list, no rate-limit or caching context, no mention that it takes no input.

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?

It is a single short sentence, so there is no bloat in length, but the only content is the redundant phrase 'You will receive the list of locations by this API call'. It is concise without being informative; the front-loaded element merely echoes the title.

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

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

Because there are no parameters and an output schema exists, the description need not describe return values, so the minimal text is arguably sufficient. However, it never clarifies what 'locations' means (geographic/market targets for content analysis) or how the result is meant to be consumed, leaving ambiguity among near-identical sibling list 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 the baseline of 4 applies and there is nothing for the description to compensate for. The description neither confuses nor enriches an empty schema.

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

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

The description essentially restates the title ('List of Locations for Content Analysis API') with the filler phrase 'by this API call', adding no verb, scope, or qualifier beyond it. An agent learns no more than it already knew from the name. It does not distinguish this from the many similarly named 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 indication of when to call this versus get_dataforseo_content_languages, get_dataforseo_content_categories, or get_dataforseo_content_available_filters. No prerequisites, no workflow context (e.g., that locations feed into a subsequent search call) are given.

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 adds no behavioral context beyond what the annotations already state (readOnlyHint=false, destructiveHint=true, idempotentHint=false, openWorldHint=true) — no auth requirements, rate limits, cost, or freshness/live-computation behavior. The phrase 'will provide you with data' frames it as a plain data retrieval, which sits uneasily with destructiveHint=true, though it makes no explicit read-only claim, so this is a transparency gap rather than a formal 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; the purpose is stated immediately. It is efficient, though arguably under-specified rather than optimally concise.

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

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

An output schema exists, so return values need no explanation, and the input schema is rich. But for a live POST endpoint with a destructive annotation and 8+ optional filters, the description omits prerequisites, the live/non-idempotent nature, and any routing guidance, leaving it only minimally sufficient.

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 coverage is effectively 0% (the single 'body' array parameter carries no description), but the nested item properties (category_code, date_from/date_to, page_type, date_group, rank_scale, search_mode, initial_dataset_filters) are documented in detail in the schema itself. The description only echoes category and date-range semantics at a high level, adding little, but the schema does most of the work.

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 resource (citations in the target category) and a scope (indicated date range), so the agent knows what the endpoint returns. It does not, however, distinguish this tool from close siblings such as post_dataforseo_content_phrase_trends_live, post_dataforseo_content_sentiment_analysis_live, or post_dataforseo_content_search_live, which all sit in the same Content Analysis family.

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 sibling trend/search/sentiment tools as alternatives. The agent must infer from the name alone that this is the category-level trends endpoint rather than phrase trends or search.

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

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

The description frames the tool as pure data retrieval ("provide you with data"), while annotations declare readOnlyHint=false, destructiveHint=true, and idempotentHint=false. Nothing in the text reconciles this, and no other behavioral traits (task queuing, per-call cost, live-vs-cached behavior) are disclosed.

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 zero filler or repetition. It is efficient, though the terseness here reflects under-specification rather than disciplined economy.

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 explained. But for a live POST endpoint with a rich nested filter/body structure and destructive/readOnly ambiguity, one sentence leaves the agent without enough to call it correctly or safely.

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 single required `body` array is undocumented at that level. The description only gestures at "the indicated date range," adding no meaning about keyword format, date_group, rank_scale, search_mode, or the filter syntax beyond what the (admittedly detailed) nested item schema already 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?

"Provide you with data on all citations of the target keyword" names a resource (keyword citations) and a scope (date range), so it is not a tautology. However, it never mentions trends, phrase trends, or the fact that this is a content-analysis endpoint, so it does not differentiate itself from siblings like post_dataforseo_content_search_live or post_dataforseo_content_sentiment_analysis_live.

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, when-not-to-use, or alternative-tool guidance. The only contextual hint is "for the indicated date range," which describes scope rather than selection criteria, leaving the agent to guess between this and the other content_*_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?

The description frames the tool as a passive information provider ('will provide you with ... data'), which reads as a safe retrieval operation, while the annotations declare readOnlyHint=false and destructiveHint=true. This is a direct mismatch between the stated behavior and the declared safety profile, and the description adds no other behavioral context (no auth, no rate limits, no cost) to resolve it.

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?

It is a single, front-loaded sentence with no bloat, which is structurally fine. It does carry filler ('and other parameters specified in the request') that adds no information, but the overall size is appropriate.

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 nested array body, many optional filters, and a non-trivial sentiment/rank configuration surface, the description explains almost nothing about how to call it. An output schema exists so return values need not be described, but the input side and the operational character (POST payload, batching of keywords, thresholds) are left entirely to the schema.

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

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

The reported schema description coverage is 0%, so by the rubric the description should compensate for parameter meaning, and it does not: it only references 'the keyword and other parameters' without naming or explaining any of the many filter fields. However, the underlying schema itself contains unusually rich per-property descriptions (tag, page_type, rank_scale, filters, thresholds), so an agent opening the schema is not actually left guessing; this offsets the description's silence, keeping it at a baseline 3 rather than lower.

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 says the endpoint returns 'rating distribution data for the keyword,' which gives a resource but only loosely a verb, and largely restates the tool name/title ('Rating Distribution API'). It does not distinguish this tool from similar siblings such as sentiment_analysis, summary, search, or phrase_trends beyond the resource noun. An agent can guess the resource but gets no sharp differentiator.

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 statement of prerequisites, and no mention of alternatives among the many sibling tools. The agent must infer the selection context entirely from the resource name. This is the 'no guidance' case.

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

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

The annotations declare destructiveHint=true, readOnlyHint=false, and openWorldHint=true, but the description frames the tool as simply providing citation data, implying a safe read operation. It gives no warning about destructive behavior or side effects, directly contradicting the destructive 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 a single front-loaded sentence with no wasted words. However, it is too terse for a complex POST endpoint with many nested parameters and filters, making the brevity feel under-specified rather than optimally concise.

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

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

An output schema exists, so return values need not be described. But for a complex content-search endpoint with a required request body, pagination, filtering, and destructive annotations, the description omits all usage, behavioral, and parameter context needed 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 sole top-level parameter is a body array with 0% top-level schema description coverage. The description mentions only "target keyword" and does not explain the body array structure, required keyword/offset_token alternatives, filters, pagination, or other nested fields, leaving the parameter largely uncontextualized by 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 states a specific action and resource: it provides detailed citation data for a target keyword. It is clear enough to understand the tool's core purpose, but it does not distinguish this endpoint from related DataForSEO content-analysis siblings such as sentiment analysis or content summary.

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 tool instead of alternatives, no prerequisites, and no exclusions. The agent has no indication of appropriate contexts or limits beyond the terse purpose statement.

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 frames the tool purely as a data provider ('will provide you with sentiment analysis data'), implying a read operation, while the annotations declare readOnlyHint=false and destructiveHint=true. That mismatch misleads the agent about side effects, and the description adds nothing about auth, cost, or per-request limits.

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

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

Two sentences, front-loaded with the purpose, so it is compact. However the trailing docs link with a '?bash' fragment is low-value filler that occupies half the description without adding usable guidance.

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

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

An output schema exists, so return values need not be explained, and the core purpose is conveyed. But for a live, billed, open-world API call the definition omits usage context, cost/latency expectations, and any reconciliation of its read-oriented wording with the destructive annotation.

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 mentions only the 'target keyword' and adds no information about the single top-level body parameter or its nested fields, despite the reported 0% schema-description coverage at the parameter level. It does not compensate for the coverage gap even though the nested schema itself is fairly detailed.

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: returns sentiment analysis data for citations matching a target keyword. It is clearly distinguishable from siblings like post_dataforseo_content_search_live and post_dataforseo_content_summary_live, though it never explicitly names those 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 choose this sentiment endpoint versus the content search or content summary endpoints, nor any prerequisites (credentials, live-call cost, rate limits). The only pointer is an external docs URL, which pushes the decision burden onto 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 declare destructiveHint=true and readOnlyHint=false, but the description says nothing about mutation, side effects, rate limits, or auth needs. The phrase 'provide you with an overview' passively implies retrieval without confirming or clarifying the annotated write/destructive profile, so the description adds no behavioral context beyond the structured 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?

A single front-loaded sentence with no filler. It is efficiently structured, though the brevity edges toward under-specification rather than tight conciseness.

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

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

For a tool with a complex nested array-of-objects body and many filtering options, the description omits when-to-use, parameter shape, and any behavioral nuance. The presence of an output schema excuses it from explaining returns, but it remains too thin given the input complexity.

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

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

Top-level schema description coverage is 0% — the sole 'body' parameter is undocumented at that level (though nested items are richly described). The description contributes no parameter meaning (e.g., that body is an array of per-keyword task objects) and so fails to compensate for the coverage 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 states a resource and action ('overview of citation data available for the target keyword'), which is more specific than the title alone. However, it does not distinguish this tool from siblings like post_dataforseo_content_search_live or post_dataforseo_content_sentiment_analysis_live, leaving the agent to infer what makes a 'summary' unique.

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 endpoint versus the many sibling content/analysis tools, nor any prerequisites or exclusions. The agent gets no routing help at all.

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.