AIsa SEO & AI Visibility

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

Adds substantial context beyond the readOnlyHint/openWorldHint/destructiveHint annotations: the call is free, it requires the Authorization header, and it discloses result semantics ('ok is true only when the balance was read; otherwise each entry of errors says what to do in next_step'). That tells an agent how to interpret partial failure, which annotations cannot convey.

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 sentences, front-loaded with what the agent gets, followed by cost/auth, then call triggers, then result interpretation. No sentence is redundant and none could be dropped without losing 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?

For a zero-parameter read tool with an output schema already present, the description covers the remaining gaps: cost, auth requirement, invocation triggers, and the ok/errors/next_step contract. Nothing an agent needs to call or interpret it correctly 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 zero parameters, so the description has nothing parameter-level to compensate for, and the schema is fully covered. A 4 rather than 5 reflects that it adds no input-syntax detail, though none is possible 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 concrete resource set (identity/key, account balance, Go-to-Market wallet, usage over 1/7/30 days) rather than restating the name. It is clearly distinguishable from every sibling, which are all Reddit, batch, or search tools. An agent knows exactly what it will learn by calling it.

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

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

Explicit triggering conditions are given: 'Call it to verify a new connection or to explain a 402.' That is a strong when-to-use statement. It stops short of 5 because it names no alternative tool or a when-not condition, though the sibling list offers no obvious competing diagnostic.

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

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description is not required to restate it. It adds genuine operational context: the batch_use prohibition, exact-name/prefix requirements, and session reuse semantics. It does not cover failure handling when names are invalid, keeping it below a 5.

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?

Front-loaded with the core action and size limit, and every sentence carries workflow value. The session_id sentence duplicates the schema's own parameter description, which is minor redundancy rather than bloat.

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 read-only batch retrieval tool with an output schema, the description supplies the orchestration context an agent needs (source of names, next steps, session handling) without needing to explain return values. Nothing essential to correct invocation 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?

Schema description coverage is 100%, so the baseline is 3. The description adds real meaning beyond the schema by constraining names to those returned by search and warning against a client prefix such as mcp__aisa__, which is not encoded in the schema.

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

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

States a specific verb and resource ('Retrieve schemas for 1 to 20 exact tool names') and pins the source of those names to search. This clearly separates it from siblings like search (finds tools), batch_quote, and batch_use.

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

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

Explicitly sequences the workflow: build arguments from successful schemas, then call batch_quote, and 'Do not call batch_use yet.' This is an explicit when-to-use and when-not-to-use instruction naming the alternative siblings.

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

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

Goes well past the readOnly/idempotent annotations by explaining that quotes are price observations rather than authorization grants, and precisely delimiting what auto_execute_eligible does and does not waive (user prohibition, narrower scope, tighter budget). It also guarantees no execution fallback, which is the key behavioral risk for this tool.

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

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

Purpose is front-loaded and most sentences carry distinct rules (no fallback, eligibility limits, naming, session reuse). It is on the long side and the argument-shape constraint is stated twice in spirit, but little is truly wasted.

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-value documentation isn't required, yet the description still flags the two response fields that matter for a downstream decision. Combined with session and naming guidance, an agent has everything needed 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?

At 67% schema coverage the schema already documents each parameter, so the description earns credit only for added meaning: exact-name rules for 'tool', session_id reuse semantics, and the 1-to-20 argument-shape parallel with batch_use. It doesn't clarify search_id beyond what the schema states.

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 ('Obtain AIsaServices cost quotes for 1 to 20 published tools') plus the crucial scope qualifier 'without executing them.' It is immediately distinguishable from the sibling batch_use, which executes rather than quotes.

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

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

Explicitly names the parallel sibling ('Same input as batch_use') and draws the boundary ('Never falls back to execution'). It also gives operational conditions: use names exactly as search returns them, strip any mcp__aisa__ prefix, and reuse session_id from Search for the task.

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

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

Annotations already cover mutability, destructiveness, and open-world behavior. The description adds critical business logic: billable calls, quote dependency, the meaning of auto_execute_eligible (waives only cost confirmation, not user prohibitions), and no silent retry. It doesn't detail rate limits or return format, but with an output schema present, that's acceptable.

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

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

The description is dense and front-loads the cost warning, but it packs many separate instructions into one block, making it less scannable. Some sentences like 'A missing or failed quote is not free' add value, but the overall flow could be better 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?

Given the destructive, open-world annotations and the presence of an output schema, the description covers the essential preconditions and behavioral constraints. It could mention error handling or partial failure behavior, but the core guidance is sufficient for correct invocation.

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

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

Schema coverage is 67%, and the description adds important semantics: tool names must be exact without client prefix, session_id reuse guidance, and 1-20 call limit. It doesn't explain search_id, but the schema description for search_id is already clear enough.

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

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

The description clearly states the verb and resource: 'Execute 1 to 20 independent published-tool calls', and the title matches. It distinguishes itself from batch_quote by requiring a quote first, but doesn't explicitly contrast with the singular 'use' sibling, though that's implied by 'batch'.

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

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

The description gives explicit when-to-use and when-not-to-use conditions: only after a successful batch_quote with auto_execute_eligible=true or explicit user authorization. It also states re-quote conditions and prohibits silent retries, leaving little ambiguity.

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 genuinely new behavioral context not in the annotations: error responses (4xx/5xx) are not charged, which matters for an agent deciding whether to retry or validate inputs before spending money.

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 functional sentence is front-loaded, concise, and information-dense. The appended pricing block is boilerplate rather than definitional content, which slightly dilutes the structure but does not bury the core statement.

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 and rich annotations, the description only needs to cover purpose and cost behavior, both of which it does. Missing only a word on which related domain tool to choose when the agent wants broader metrics.

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%, with date format and target example already documented in the schema, so baseline 3 applies. The description adds only the DR score range, which describes the return value rather than either parameter.

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 (Return) plus two named outputs (Domain Rating, Ahrefs Rank) and defines DR's 0-100 range, so the agent knows exactly what comes back. It does not distinguish itself from sibling tools like get_ahrefs_site_metrics or get_semrush_domain_overview, which is the only gap.

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

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

No guidance on when to prefer this over the adjacent Ahrefs/Semrush domain tools, and no prerequisites or alternative named. The only contextual statement is a billing note, which is not usage guidance.

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

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds non-obvious billing behavior ('4xx/5xx are not charged') and the $0.08 per successful request cost, which are genuine operational facts the agent cannot get from 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?

The core sentence is front-loaded and dense with real content, and the error-charging note is short. The delimited aisa-pricing boilerplate is standardized noise rather than authored prose, though it does consume space for a single price figure.

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-value structure need not be explained, and both required parameters are fully documented. For a simple two-parameter read tool this is nearly complete; only the lack of any routing context versus the competing Semrush/DataForSEO domain tools leaves a 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% (date format/example and target domain example are documented in the schema), so the baseline is 3. The description only restates 'target domain' and 'a given date' without adding format, normalization, or subdomain/path handling detail.

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?

Specific verb ('Return') plus precise resource ('core Site Explorer metrics for a target domain on a given date'), and the enumerated metric list (organic/paid keyword counts, top-3 count, organic traffic, traffic value) differentiates it from near siblings like get_ahrefs_domain_rating and get_semrush_domain_overview.

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

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

There is no when-to-use, when-not-to-use, or alternative-tool guidance. Given the many Semrush and DataForSEO domain-metric siblings, the agent gets no help deciding between this and get_semrush_domain_overview; only the metric list implicitly suggests a domain-level overview use case.

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

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is lower. The description adds genuine extra context beyond the annotations: usage-based billing with a ~$0.012 default request estimate and a note that final settlement may differ — useful cost behavior an agent cannot get from 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?

The core sentence is reasonably front-loaded but runs on with an awkward 'as well as drill down into exact on-page issues' clause, and it opens with a stray zero-width character artifact. The pricing block is boilerplate but useful, so it earns partial credit rather than penalty.

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 annotations plus a fully documented schema cover safety and input. The remaining gap is that it never states the scan must be complete/that this reads results of a POST task, which is the main thing an agent could get wrong.

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% for the single 'id' parameter, and the schema already explains how to obtain the task identifier from the Task POST response, including an example. The description adds nothing about the parameter, so the 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?

States a clear verb+resource — retrieve the overall summary of an already-scanned site's on-page data by task ID. It also positions itself against the many detailed siblings (it tells you which detailed functions to call next), though the phrasing 'get the overall information on a website' is looser than it needs to be.

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?

Usage is implied: use this to get an overview and then decide which detailed endpoints to call. It does not explicitly name the alternative detailed sibling tools or state a precondition (e.g. the scan task must have finished), so the guidance stays at the inference level.

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 only real contribution is the pricing disclosure ($0.004 per successful request), which is genuinely useful cost context, but the description says nothing about what the call does, what it returns, or any limits.

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

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

The text is short and cleanly formatted, but it is entirely off-topic relative to the tool's function; every sentence is about pricing rather than the operation. Brevity here reflects missing content, not efficient writing.

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-value explanation is not required, yet the description omits purpose, usage context, and parameter meaning for a live external-API tool. It is not remotely complete for an agent to select and invoke it.

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

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

Schema coverage is 0% and the single required parameter 'target' has no description anywhere. The description does not clarify whether 'target' is a domain, URL, or path — the one piece of information that would most help an agent invoke this correctly.

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

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

The description contains no functional statement at all — it is purely a pricing notice. Nothing tells the agent that this tool returns a Semrush backlinks overview for a target, nor how it differs from siblings like get_semrush_backlinks or get_semrush_referring_domains.

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 prerequisite information, and no reference to any alternative among the many sibling backlink tools. The agent has to infer applicability from the tool 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 readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description's only added value is a usage-based billing note ($0.02 default estimate), which is genuine cost context not present in annotations, but nothing is said about scope, database semantics, or result 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?

It is short, but the content is an HTML-comment-wrapped billing banner rather than front-loaded task information. Brevity here reflects missing content, not 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?

An output schema exists so return values need not be explained, but for a two-parameter query tool with 0% schema coverage the description supplies nothing an agent needs to invoke it correctly – no purpose, no parameter meaning, no usage context.

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% for two required parameters (domain, database). With no enums and no schema docs, the description must compensate, and it says nothing about expected domain format or valid database values.

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 contains zero functional content – it is entirely a pricing banner. The only signal about what the tool does comes from the tool name itself, and there is no differentiation from siblings like get_semrush_url_organic_keywords or get_semrush_organic_competitors.

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 named, no prerequisites. An agent has no basis for choosing this over the many other Semrush/DataForSEO keyword tools listed as siblings.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description does add one genuine behavioral fact not in the annotations – a per-request price of $0.001 USD – which is useful cost context, but it discloses nothing about auth requirements, rate limits, or response 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, but the content is boilerplate HTML-comment pricing markup that does not help an agent select or invoke the tool. Length is minimal while relevance is near zero, so it is under-specified rather than 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?

For a domain-overview tool with a required 'domain' parameter and an undocumented 'database' parameter, the description leaves the agent without the essential information needed to call it correctly. The presence of an output schema excuses explaining return values, but nothing else compensates for the missing purpose and parameter detail.

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% for two parameters ('domain' and 'database'), and the description supplies no meaning, format, or defaults for either. It does not compensate for the schema gap at all, and notably omits that 'database' defaults to 'us'.

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 never states what the tool does; it contains only a pricing notice wrapped in HTML comments. An agent must infer the purpose entirely from the tool name 'get_semrush_domain_overview'. It provides no verb+resource statement and no differentiation from siblings like get_semrush_domain_organic_keywords or get_semrush_domain_vs_domain.

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 alternatives among the many semrush/ahrefs/dataforseo siblings. The text is solely about cost, not about selection.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the read-only safety profile is covered elsewhere. The description's sole contribution is cost transparency (usage-based billing, ~$0.005/request, account-specific settlement), which is genuinely useful but says nothing about data source behavior, batching limits implied by x-aisa-maxItems=20, or result freshness.

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 text is short, but it is boilerplate wrapped in HTML comment markers that consumes the entire description while conveying none of the tool's functional purpose. Brevity here stems from omission, not from disciplined editing.

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?

Although an output schema and rich annotations exist, the description omits everything an agent needs to select this tool over its many keyword-data siblings and to supply 'phrase'/'database' correctly. Pricing alone is insufficient for a 2-parameter, high-sibling-ambiguity 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 0% for both parameters, and the description adds nothing about 'phrase' (multi-value, delimiter ';', max 20 items) or 'database' (which SEMrush database/region to query). With zero coverage on both sides, the agent is left to guess parameter meaning entirely.

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 never states what the tool does. It contains only a billing/pricing notice ('Usage-based billing; Default request estimate: $0.005 USD'), so an agent cannot tell this retrieves SEMrush keyword difficulty scores, nor distinguish it from siblings like get_semrush_keyword_overview or the DataForSEO difficulty 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?

There is no when-to-use guidance, no prerequisites, and no reference to alternatives such as post_dataforseo_labs_google_bulk_keyword_difficulty_live or get_semrush_keyword_overview. The only conditional text is about pricing, not invocation context.

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

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and open-world behavior, so the safety profile is covered. The only added behavior is the per-request price ($0.001 USD per successful request), which is genuinely useful cost context beyond the annotations, but it is the sole non-template content.

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 blurb is short but consists entirely of an HTML-comment pricing artifact that does not earn its place for tool selection. It is not front-loaded with any purpose statement.

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?

Although an output schema and annotations exist, the description supplies zero information on what the tool returns or when to call it. For a keyword-lookup tool surrounded by dozens of similar siblings, this is completely inadequate for correct selection.

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% for both parameters, so the description must compensate and instead says nothing about the required 'phrase' or the 'database' default. There is no hint about accepted phrase format or database values such as 'us'.

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 contains only a pricing banner and never states what the tool does. Nothing indicates it returns Semrush keyword overview metrics for a phrase, nor distinguishes it from siblings like get_semrush_keyword_difficulty or post_dataforseo_labs_google_keyword_overview_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 guidance, no mention of alternatives, and no conditions for selecting this tool over the many keyword-data siblings. The agent is left to infer everything 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=true, idempotentHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The one genuine addition is cost disclosure (predicted ~$0.08 per request, usage-based, not fixed), which is real behavioral context an agent cannot get from annotations. However, the description says nothing about what the call returns, rate limits, or data freshness.

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 text is short, so length is not the problem; the problem is that every line is HTML-commented billing boilerplate rather than front-loaded tool purpose. Legitimate conciseness here would be a single sentence stating what the tool returns.

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?

Even granting that an output schema exists and return values need not be explained, the definition omits purpose, usage context, and the meaning of both required parameters. An agent cannot call this correctly from the description alone.

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% and both parameters (domain, database) are required yet completely undocumented in the schema. The description does not mention either parameter or its expected format, so it 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 never states what the tool does — it contains only a pricing/billing banner with no verb, resource, or scope. The title 'Organic Competitors' is the only hint, and the tool must be distinguished from many siblings like get_semrush_domain_overview and post_dataforseo_labs_google_serp_competitors_live with no help from 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?

There is no when-to-use, when-not-to-use, or alternative-tool guidance at all. Given a large set of competing Semrush/DataForSEO competitor tools, an agent gets zero routing signal from this 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, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description does add real cost context (usage-based billing, ~$0.02 default estimate, settlement may differ), which is genuinely useful for an agent, but says nothing about auth, latency, or result shape.

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, but it is short because it omits its actual job — the only sentences present are a cost disclaimer. Brevity without any statement of purpose is under-specification, not 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?

An output schema exists so return values need not be explained, but for a networked, billed search tool the description supplies no purpose, no parameter meaning, and no usage routing. It is wholly inadequate despite the output schema.

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

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

Schema description coverage is 0% and the description never mentions 'phrase' (the required keyword) or 'database' (the market selector). With two undocumented parameters and no compensating text, the agent has no basis for supplying 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 contains only a pricing/billing banner and never states what the tool does. There is no verb or resource ('fetch SEMrush organic search results'), so an agent must infer everything from the tool name alone and cannot distinguish it from get_semrush_paid_results.

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

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

No when-to-use guidance, no prerequisites, and no routing to alternatives such as get_semrush_paid_results or the DataForSEO organic SERP siblings. The only content is billing 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 readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds only a default cost estimate and billing caveat, which is useful but not a substantive behavioral trait about the operation itself.

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

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

The description is short but does not earn its place: it is entirely pricing boilerplate wrapped in HTML comment markers. It is not front-loaded with any actionable purpose or usage 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?

For a tool with two required parameters, no parameter documentation, and many similar sibling tools, the description is completely inadequate. The presence of an output schema does not compensate for the absence of purpose, usage, and parameter guidance.

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%, and the two required parameters (target and target_type) are undocumented in both the schema and description. The description provides no meaning, format, or allowed values for either parameter.

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 does not state what the tool does. It only contains pricing metadata, leaving the tool's purpose to be inferred entirely from the name/title and sibling context.

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 versus alternatives such as post_dataforseo_backlinks_referring_domains_live or other Semrush backlink tools. The pricing note does not help an agent choose the correct 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 establish that this is a safe, read-only, closed-world operation. The description adds useful context beyond annotations by saying it returns categories, servers, tool counts, and the dedicated endpoint, and that it is free. It omits auth/rate-limit details, but those are less critical given the read-only 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 front-loaded with what the tool returns and is generally efficient. The parenthetical about tool-router and the endpoint URL add some density, but each part contributes usage or content context.

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, read-only annotations, and an output schema available, the description supplies enough context for correct invocation. It explains the catalogue contents and gives the category-specific endpoint alternative, leaving no critical gap for an agent.

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

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

The tool has zero parameters, so parameter semantics are not a meaningful differentiator. The schema has no properties to explain, and the description does not need to document any 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 clearly states that this tool gives a catalogue overview: categories, servers per category, tool counts, and a dedicated endpoint. The resource is specific and recognizable, though sibling differentiation is only partial because it references 'tool-router' rather than the listed siblings like search/use.

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 gives a clear alternative: use the category-specific endpoint if you only need one category, rather than going via search. It also notes the tool is free and AIsa-only, but it does not explicitly state when to call list_categories instead of search or 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?

Annotations already declare the operation as openWorld, destructive, non-idempotent, and not read-only, so the description's main addition is the usage-based pricing estimate. It does not explain cost triggers (e.g., which parameters affect billing) or confirm side-effect behavior beyond what annotations already state.

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

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

The description is short and front-loads the purpose before the pricing note. It contains leading zero-width characters and an HTML comment block, but overall it is appropriately sized and easy to parse.

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 LLM endpoint with many optional behavioral parameters and a rich output schema, the description omits nearly all operational context. It does not guide model selection, web-search flags, token limits, or conversation history usage, making it incomplete despite the pricing note.

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% at the top level, and the description provides no parameter details whatsoever. It says only 'based on the input parameters' and does not compensate for the coverage gap, leaving the required model_name and user_prompt fields unmentioned.

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 a ChatGPT AI model). It identifies the ChatGPT variant, distinguishing it from sibling Claude, Gemini, and Perplexity endpoints, but does not explicitly name 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?

The description offers no when-to-use guidance, no prerequisites, and no alternatives to consider. It only says responses are based on input parameters, leaving the agent to infer context entirely.

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 carry the safety profile (readOnlyHint=false, destructiveHint=true, openWorldHint=true, idempotentHint=false), and the description adds genuinely useful cost context via the billing estimate. However, it never explains why this POST is flagged destructive/non-idempotent, nor describes latency, async behavior, or rate limits – it also gives no hint that the request requires a model lookup first.

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 core sentence is front-loaded and tight. The appended pricing comment block is boilerplate, but it is short and clearly delimited, so it does not obscure the purpose statement.

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 nested, constraint-heavy request body the description is far too thin. It omits the required-field expectation (model_name, user_prompt are marked required in the schema), the 500-character prompts, the 10-message chain cap, and the models-listing endpoint the agent must call to resolve valid model names.

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?

Reported schema description coverage is 0% at the top level (the single 'body' array parameter), and the description compensates with nothing beyond the generic phrase 'based on the input parameters'. No mention of model_name, user_prompt, message_chain limits, or the temperature/top_p mutual exclusion, all of which the caller must get right.

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 specific Claude model'), which distinguishes it from the ChatGPT, Gemini, and Perplexity siblings that share the same naming pattern. It is clear but does not explicitly call out how it differs from 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?

No when-to-use, when-not-to-use, or alternative-selection guidance is given. Given three sibling LLM-response endpoints with identical shapes, the description gives the agent no basis for choosing between them beyond the model name 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?

Annotations already declare the safety profile (readOnlyHint=false, destructiveHint=true, openWorldHint=true), so the bar is lower, but the description adds almost nothing behavioral beyond a cost snippet. It does not reconcile the counterintuitive non-read-only/destructive labeling with the word 'retrieve' used in the description, nor mention auth, rate limits, or latency.

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 plus a clearly delimited pricing block, with the core purpose front-loaded. Nothing is padded, though the 'based on the input parameters' clause is filler that adds no 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?

An output schema exists so return values need not be described, and the payload is documented in the nested schema, so the fundamentals are covered. However, for a billable, open-world LLM query tool the description omits the routing guidance and behavioral context an agent needs to call it correctly versus its model 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?

Top-level schema description coverage is 0% – the single 'body' parameter has no description – and the tool description provides no compensating explanation of the request payload shape. Though the nested schema is richly documented internally, the description itself contributes no parameter-level meaning, leaving the burden entirely on the schema.

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

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

The description names a specific verb ('retrieve structured responses') and resource ('Gemini AI model'), and the tool name itself separates it from the ChatGPT/Claude/Perplexity LLM siblings. It stops short of explicitly stating when this Gemini endpoint is preferred over those siblings, so it is clear but not differentiated in prose.

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, no prerequisites, and no mention of the sibling LLM endpoints (chat_gpt, claude, perplexity) that an agent would need to disambiguate against. The only usage-adjacent content is a billing estimate, which is cost information rather than routing guidance.

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

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

Annotations already declare the safety profile (readOnlyHint=false, destructiveHint=true, idempotentHint=false, openWorldHint=true), so the bar is lower. The description adds one genuinely useful behavioral fact not present in structured data: usage-based billing with a ~$0.011 estimated cost per request. It does not, however, explain or reconcile the non-read-only/destructive labeling, retry safety, or rate limits, leaving a noticeable 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?

Front-loaded with the purpose in one sentence, followed by a compact pricing note; nothing is padded. Minor noise: a stray leading zero-width character and the templated aisa-pricing comment markers, which are formatting artifacts rather than content.

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 the cost caveat is a helpful addition. However, for a POST endpoint carrying a destructive/non-idempotent annotation profile and a nested array input with required-if-paired location/language fields, the description should say more about input requirements and safety expectations than it does.

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% and the single top-level 'body' parameter is undocumented, so the description would need to compensate, yet it mentions no parameters at all (no keywords, language_code/name, location_code/name pairing). The nested item schema is internally detailed enough that an agent is not entirely lost, but the description itself contributes zero 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?

States a specific verb+resource: 'provides search volume data for your target keywords,' and adds the scoping qualifier 'reflecting their estimated usage in AI tools,' which separates it from the Google-Ads-style volume siblings. It stops short of naming an alternative (e.g. post_dataforseo_keywords_gads_search_volume_live) or explaining how the AI-tool volume differs, so differentiation is implied rather than 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?

There is no when-to-use guidance, no mention of when this should be preferred over the many sibling keyword-volume/keyword-overview endpoints, and no prerequisites such as needing a location or language pair. The description is purely declarative about what the endpoint returns.

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?

Adds substantial behavior beyond the annotations: the DataForSEO envelope contract (data in tasks[0].result, outcome in tasks[0].status_code, rejected requests still returning HTTP 200), the type-rejection behavior for a bare string target with the literal error text, and a billing estimate. None of this is derivable from readOnlyHint/openWorldHint/destructiveHint/idempotentHint.

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?

Front-loads what the tool returns before constraints, siblings and billing, and nearly every clause earns its place. The 'Measured at 7.0 KB' fragment is cryptic and adds little, and the paragraph is dense enough to be slightly hard to scan.

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 the envelope explanation plus sibling routing covers the main gaps. Minor omissions remain around authentication/permission needs for a tool annotated destructiveHint=true, and the pricing note is generic.

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%, and the description compensates by spelling out that target must be an 'array of objects' with {"domain": ...} or {"keyword": ...}, including the exact error messages for the wrong shapes. It does not discuss the other body-level fields (platform, location, filters), though the nested schema documents those in detail.

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 resource and measurement ('How often assistants mention your target, as totals rather than individual answers') with named return fields. It explicitly distinguishes itself from siblings: search_live 'shows the answers behind it' and cross_metrics_live 'compares several targets at once'.

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?

Tells the agent this is 'the headline visibility number' and routes to the two specific alternatives with the conditions that select them. It also points at get_dataforseo_ai_llm_mentions_available_filters for filter field discovery, so the when-to-use and where-to-look guidance is complete.

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?

Adds substantial context beyond the annotations: the DataForSEO envelope layout (tasks[0].result vs tasks[0].status_code), the fact that a rejected request still returns HTTP 200, per-item size (~7.7 KB), and pagination via search_after_token. The annotations carry the safety profile and the pricing note explains the billing side effect, so this is strong but not exhaustive.

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

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

Purpose is front-loaded, followed by pagination, size, the target-type warning, and the envelope note. Most sentences earn their place; the pricing boilerplate block is noise but is tooling-inserted rather than authored.

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, many-parameter, billed search call, the description covers the high-risk pitfalls (target shape, envelope, error-as-200, pagination) and an output schema exists so return values need not be re-explained. It could say more about non-target params, but nothing essential for a correct call 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?

Coverage is reported at 0%, so the description must compensate. It does this for the critical parameter by warning that target is an array of objects ({domain}/{keyword}) and that a bare string or array of strings is rejected, plus a pointer to the filter-field source. The remaining params (platform, location, language, order_by, limit) are left to the schema's own nested text.

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 resource and verb: the individual answers where an assistant mentioned your target. It names the sibling it is not (aggregated_metrics for counts), so an agent can distinguish 'raw mentions' from 'counts' without opening either schema.

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

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

Explicitly routes to post_dataforseo_ai_llm_mentions_aggregated_metrics_live when counts rather than mentions are wanted, and points to get_dataforseo_ai_llm_mentions_available_filters for filter fields. It also says to page with the token rather than offset past the first pages. It doesn't differentiate from the other mentions siblings (top_domains, top_pages, cross_metrics), so it falls short of a full 5.

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

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

With annotations already present, the description still adds real operational context: approximate response size (20.3 KB, largest in the family), the DataForSEO envelope layout (data in tasks[0].result, outcome in tasks[0].status_code), and the non-obvious fact that a rejected request still returns HTTP 200. It does not reconcile the destructiveHint=true annotation with what is plainly a retrieval call, which is the one remaining 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?

Front-loaded with purpose and response shape, then format warnings, then the envelope, then the routing hint — a sensible order. The pricing boilerplate block and the 'This is the list to pitch' cheerleading sentence are the only non-essential lines.

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 POST query tool with a formal output schema and non-trivial platform/location constraints, the description covers routing, target format, error semantics, and response size. Since an output schema exists, it is not obligated to enumerate return fields, and it does not need to.

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?

Reported schema coverage is 0% at the top level, so the description must carry the parameter load, and it does the most important part: it warns that target must be an array of objects and quotes the exact rejection messages for a bare string or array of strings. It adds less on the optional fields, which the nested schema descriptions do cover.

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 — the domains assistants cite most for a target — and immediately names the sibling it is not (top_pages_live for individual pages). An agent can distinguish it from the other mentions tools without opening a schema.

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 names the alternative explicitly with the selecting condition ('For individual pages rather than domains use post_..._top_pages_live') and routes filter discovery to get_dataforseo_ai_llm_mentions_available_filters. Nothing about when to reach for this tool vs its siblings is left to inference.

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 and destructiveHint=true, so the safety profile is covered. The description adds genuine context beyond them by disclosing usage-based billing and an estimated per-request cost (~$0.0163), but it does nothing to explain the live/non-cached nature or clarify why a read-style LLM query is flagged destructive.

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 tight sentences with the purpose front-loaded and no wasted prose. The injected pricing block is boilerplate but adds a concrete cost fact; the phrase 'based on the input parameters' is the only filler.

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

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

An output schema exists, so return values needn't be described, and the nested input schema is thorough. However, for a live third-party inference endpoint with three very similar siblings, the description is minimal: it never routes the agent among the model-specific variants or notes that results are real-time.

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 0% (the single 'body' parameter is undocumented), but the nested properties inside it carry very detailed inline descriptions (model_name, user_prompt, message_chain alternation rules, token limits, etc.). The description adds nothing about parameters, so the schema must do all the work, which it largely does.

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 clear verb ('retrieve structured responses') and resource ('from a specific Perplexity AI model'), so the agent knows what the tool returns. It does not explicitly differentiate itself from the near-identical ChatGPT/Claude/Gemini siblings, though the model name in the tool title partly carries that.

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 only 'based on the input parameters' and offers no when-to-use guidance, no when-not-to-use, and no pointer to the sibling endpoints for other models. An agent must infer that this is the Perplexity-specific option rather than being told.

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 carry the safety profile (readOnlyHint=false, destructiveHint=true, idempotentHint=false, openWorldHint=true). The description adds one piece of context not in the annotations — the usage-based billing estimate of ~$0.033 per request — which is genuinely useful for an agent deciding whether to call. It adds nothing about rate limits, auth, or what the 'live' retrieval semantics mean, and it does not reconcile the odd destructive annotation with what is evidently a data-retrieval 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?

One tight sentence that states the resource and its payload, front-loaded with no preamble. The appended pricing block is boilerplate but short and informative. Nothing is padded or repeated from 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?

An output schema exists, so return values need not be described, and the nested schema documents the 13 optional request fields. What is missing for a tool of this complexity is routing context: why pick 'anchors' over the many sibling backlinks endpoints, and any note about the 'live' retrieval/billing model beyond the price estimate. Adequate but with a clear routing 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?

Top-level schema description coverage is 0% (the single 'body' array wrapper is undocumented), and the description adds no parameter meaning at all — no mention of target, filters, limit, or order_by. However, the nested schema entries carry very detailed prose for target, limit, offset, filters, order_by, rank_scale, and the backlink scope switches, plus a worked example, so an agent can construct a request from the schema alone. Schema does the heavy lifting; the description contributes nothing.

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: it returns 'a detailed overview of anchors used when linking to the specified website with relevant backlink data for each of them.' The resource (anchors for a target's backlinks) is unambiguous and distinct from the sibling referring-domain/backlinks endpoints by name and content. It does not, however, explicitly contrast itself with the other anchor/backlink endpoints an agent could 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?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives. With ~30 sibling endpoints in the same backlinks family (e.g. post_dataforseo_backlinks_backlinks_live, post_dataforseo_backlinks_referring_domains_live, get_semrush_backlink_anchors), the agent gets no help deciding when anchor data is the right choice. Usage is only implied by the word 'anchors'.

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 depicts a pure retrieval ('provide you with a list of backlinks'), yet annotations declare readOnlyHint=false and destructiveHint=true. This is a direct conflict that could mislead an agent about the safety profile, and the description offers no behavioral context (rate limits, billing scope beyond boilerplate, idempotency) 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?

The functional sentence is short and front-loaded, and the pricing note is compact. No filler prose, though the description is arguably too terse for the surface area it covers.

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 12-field filtering/pagination endpoint with heavy siblings the description omits usage routing and key behavioral caveats. It is under-specified relative to the tool's complexity.

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

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

Schema coverage on the top-level parameter is reported at 0%, and the description says nothing about the body arguments (mode, limit, offset, filters, order_by, rank_scale, backlinks_status_type, etc.). The nested schema carries detailed field docs, but the description itself adds 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?

States a specific verb (provide a list) and resource (backlinks) and names the accepted targets (domain, subdomain, or webpage). It is clear what the tool does, but it does not differentiate itself from the many sibling backlinks endpoints (anchors, summary, referring_domains, competitors), so the agent must infer selection 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 guidance on when to use this endpoint versus the numerous alternatives in the sibling list. The description gives no conditions, prerequisites, or exclusions, leaving the agent to guess 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 declare readOnlyHint=false and destructiveHint=true even though the description frames this as a passive 'provide you with a list' data-retrieval endpoint, creating real tension an agent must resolve. The description adds no behavioral context of its own beyond a boilerplate pricing estimate; it does not explain the POST semantics, whether anything is mutated, auth requirements, or rate limits. Only the cost note counts as added value.

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 core sentence is a single, front-loaded statement of what the endpoint returns, with no wasted prose. The appended pricing block is templated boilerplate, and the leading zero-width/whitespace characters are slightly untidy, but overall the structure is 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?

An output schema exists, so the description need not explain return values, and the input schema documents the individual fields. What is missing is the context that matters for a paid, ambiguously-annotated POST endpoint: why to pick it, and any reassurance about its real read-only nature versus the destructiveHint 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 contributes no parameter information whatsoever. Although the context signal reports 0% schema description coverage, the underlying schema actually documents target, limit, offset, filters, order_by, rank_scale, main_domain, and the exclusion flags in rich detail, so the schema effectively carries the load while the top-level 'body' wrapper remains undocumented in both places.

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 a list of competitors sharing part of a target site's backlink profile, plus backlink intersections and competitor ranks. This is far more concrete than the bare title 'Competitors'. It does not, however, distinguish itself from adjacent siblings like post_dataforseo_backlinks_domain_intersection_live or referring_domains_live, which an agent could easily confuse it with.

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 what the endpoint returns but never states when to choose it over the many sibling backlink tools (domain_intersection, referring_domains, page_intersection). There is no mention of prerequisites, cost awareness for the paid call, or any exclusion criteria, leaving the agent to infer usage entirely from the 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?

The description adds genuinely useful behavioral context beyond the annotations, notably the 2019-to-present data coverage window and the usage-based billing note with a ~$0.0271 default request estimate. However, it frames the call purely as data retrieval while the annotations declare readOnlyHint=false and destructiveHint=true, and the description never reconciles that tension or explains what the call costs/destroys at a side-effect level.

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 front-loaded sentences give the capability and the data window with no wasted words. The appended pricing boilerplate is verbose but appears to be a mandatory block rather than author-written padding. A stray leading character and the long pricing comment slightly hurt the structure.

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 the description correctly covers the data availability window and cost. What is missing is any disambiguation from the many sibling backlinks endpoints and any note on required inputs, which matters for an agent choosing among ~25 similar tools.

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

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

Schema description coverage is reported as 0% for the top-level parameter, so the description must compensate, and it only partially does: "a given domain" implies target and "a specific time period" implies date_from/date_to. It says nothing about date format, the 2019-01-01 minimum, the default endpoint date, or the rank_scale option, all of which remain undocumented at the description 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?

The description states a specific verb and resource: it returns historical backlinks data for a domain back to January 2019, including backlink counts, new/lost links, and referring domains. That is enough to identify the tool's output. It does not differentiate itself from close siblings such as post_dataforseo_backlinks_timeseries_summary_live or post_dataforseo_backlinks_summary_live, which the description's metric list also describes.

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 endpoint over the many sibling backlinks endpoints (live vs bulk vs timeseries vs summary), and no prerequisites are stated. The only conditional information is an implicit "specific time period", with nothing about defaults or limits. An 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?

The description implies a read/query operation ('provide you with a detailed overview of referring domains'), while the annotations declare readOnlyHint=false and destructiveHint=true — an outright mismatch in the safety profile an agent would act on. The only genuinely additive behavioral fact is the auto-inserted usage-based billing estimate ($0.0276 default), which does not offset the 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?

One purpose sentence followed by the pricing block; the purpose is front-loaded and nothing is padded. The pricing boilerplate is auto-generated rather than authored content, which keeps this from being a crisp 5.

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-value explanation is not needed, but for a live POST endpoint with a single highly configurable 'body' parameter, the definition supplies neither usage context nor any parameter guidance, and its implicit read framing conflicts with the declared destructive behavior. An agent cannot call this confidently from the description alone.

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 says nothing about any request field — no mention of the required 'target' format, the filter/order_by syntax, pagination via limit/offset, or the backlinks_status_type semantics. The input schema text itself is rich, but the reported description coverage is 0% and the prose contributes zero parameter meaning of its own.

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 ('provide you with a detailed overview') and resource ('referring domains pointing to the target you specify'), so an agent knows it is a live referrer-report call. However, it offers no differentiation from close siblings such as post_dataforseo_backlinks_bulk_referring_domains_live, post_dataforseo_backlinks_referring_networks_live, or get_semrush_referring_domains, and 'overview' is vague for what is really a paginated domain list.

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 agent gets no signal that this is the single-target live variant versus the bulk sibling, or when a cached/batch variant would be preferable.

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, idempotentHint, and destructiveHint, so the description's burden is lighter, and it does add a useful usage-based pricing estimate. It says nothing, however, about the read-like nature implied by 'provide', nor how the destructive annotation should be interpreted for a data retrieval endpoint, so behavioral disclosure remains modest.

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 core description is a single front-loaded sentence with no filler, and the pricing note is separated by comment markers. The leading zero-width character is a minor cosmetic flaw, but the overall structure is efficient and easy to scan.

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 complex nested body schema, an output schema, and many sibling tools, the description is too thin. It does not explain what metrics the 'overview' includes, when to choose this over siblings, or how to interpret the contradictory annotations, leaving critical operational context 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?

Schema description coverage is reported as 0%, so the description is expected to compensate for parameter meaning, but it does not. It vaguely mentions 'domain, subdomain, or webpage' without naming the target parameter, its required URL/domain format, or any of the other body fields like backlinks_filters or rank_scale.

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

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

The description clearly states the verb and resource: providing an overview of backlinks data for a domain, subdomain, or webpage. However, it does not distinguish this 'summary' endpoint from sibling tools like backlinks_live, referring_domains_live, or anchors_live, leaving the agent to infer the difference from the tool 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 offers no when-to-use guidance, no prerequisites, and no explicit alternatives. With over 30 sibling backlinks tools, an agent has no basis for selecting this summary endpoint over a more specific one such as post_dataforseo_backlinks_backlinks_live or post_dataforseo_backlinks_referring_domains_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 description frames the tool as a data-retrieval query ('provide you with detailed citation data'), while the annotations declare destructiveHint=true and readOnlyHint=false — a substantive mismatch that could make an agent hesitate or misuse the tool. The only genuine added context beyond annotations is the usage-based billing note ($0.0276 default estimate), which is useful but does not resolve the behavioral conflict.

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 text is a single front-loaded sentence plus a compact, clearly delimited pricing disclosure, with no redundant filler. It is tight, though its brevity reflects under-specification rather than disciplined 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 large nested request schema, an output schema, and a crowded sibling set, the description omits usage context, parameter guidance, and any behavioral caveats. The presence of an output schema excuses it from explaining return values, but it leaves too many gaps 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 `body` carries no description, and the description adds nothing about the parameters — it never mentions the required nested `keyword`, the filter/order_by syntax, pagination via offset/offset_token, or search_mode. While the nested schema fields are individually well documented, the description contributes zero parameter meaning and does not compensate for the 0% top-level 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?

The description states the tool returns 'detailed citation data available for the target keyword,' which conveys the resource and input, but the action is phrased loosely ('provide you with') rather than as a clear search/retrieval verb. It offers no differentiation from the many adjacent DataForSEO tools (e.g. post_dataforseo_content_summary_live, post_dataforseo_content_sentiment_analysis_live), so an agent cannot route confidently among siblings from the text 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 when-to-use statement, no when-not-to-use exclusion, and no named alternative despite ~60 sibling tools that overlap heavily. An agent gets no signal about when citation search is the right choice versus content summary or sentiment analysis.

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 describes a retrieval operation ('provide you with sentiment analysis data'), but the annotations declare readOnlyHint=false and destructiveHint=true. An agent reading these together gets directly conflicting signals about whether this call mutates or destroys anything. The description also discloses nothing about per-request cost semantics beyond the generic pricing block, nor any rate/auth constraints. This is an Annotation 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?

The core sentence is short, front-loaded, and waste-free, followed by a docs link and pricing note. It is appropriately sized, though the URL and billing boilerplate are somewhat templated rather than informative.

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 the pricing estimate is a useful addition. However, for a filtered content-analysis endpoint with a required per-item keyword and many optional filter/threshold fields, the description leaves the agent without any operational framing for constructing the request.

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% at the top level, and the single exposed parameter is an opaque 'body' array. The description only implies a 'target keyword' exists but never explains that keyword is required per item, nor mentions filters, thresholds, or the rank_scale/page_type options documented deeper in the schema. It does not 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 names a specific operation and resource: sentiment analysis data for the citations available for a target keyword. That is materially more specific than the title and distinguishes it from content_search_live and content_summary_live by scope. It stops short of explicitly contrasting itself with those siblings, so it does not reach 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 prerequisites, and no mention of any alternative endpoint. The only usage pointer is a bare documentation URL, which pushes the burden of figuring out when this applies 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?

The description frames the tool as returning data ('provide you with an overview of citation data'), i.e. a retrieval operation, while the annotations declare readOnlyHint=false and destructiveHint=true. Read semantics versus a destructive-write annotation is a direct action-semantics mismatch, so per the rubric this scores 1. The one genuinely useful behavioral addition is the billing note ($0.0276 default estimate, usage-based, non-fixed), which is the only correctable signal here.

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 a single sentence and the pricing block is clearly demarcated, so there is no real waste. It is slightly under-specified rather than over-long, and a stray zero-width character precedes the text, but structurally it is 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?

An output schema exists, so return values need no explanation, and the billing note covers the cost dimension. What is missing for a live, billed, keyword-driven POST endpoint is any cue about required input (keyword), filtering scope, or how this differs from the other Content Analysis endpoints. Minimum viable, with clear gaps.

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

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

The schema reports 0% top-level description coverage (the single 'body' array parameter itself is undocumented), and the description does not compensate at all — it never mentions the required 'keyword' field or any of the filter/threshold fields (page_type, rank_scale, internal_list_limit, connotation thresholds). The nested property descriptions in the schema do much of the work, which keeps this above 1, but the description adds nothing beyond the schema.

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

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

The description names a concrete verb and resource ('provide you with an overview of citation data available for the target keyword'), which is specific enough for an agent to know this returns aggregated Content Analysis summary metrics. However, it never distinguishes itself from близкие siblings such as post_dataforseo_content_search_live or post_dataforseo_content_sentiment_analysis_live, so the agent must infer the difference 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 use this endpoint versus alternatives, no prerequisites (e.g., that a keyword must be supplied in the body), and no conditions under which a different Content Analysis endpoint is preferable. Usage is only weakly implied by 'overview ... for the target keyword'.

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 read ('you will get a list of technologies'), while the annotations declare readOnlyHint=false and destructiveHint=true. This is a direct conflict about whether the call mutates or destroys data. The only behavior the description adds is the usage-based pricing estimate, which does not compensate for the safety-profile 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?

The behavioral sentence is short and front-loaded with zero waste. The appended pricing block is boilerplate rather than tool semantics, but it is clearly delimited and does not obscure the purpose statement.

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 pricing note covers cost. However, for a live billed endpoint with no sibling differentiation and no parameter explanation, the definition is only minimally complete 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 0% and the single 'target' param is nested inside a body array, so the structured fields do not explain the parameter. The description only alludes to 'a particular domain' without naming the field, format, or that results are returned for the specified domain only. It adds almost nothing beyond the schema.

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

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

States a specific verb and resource: 'get a list of technologies used in a particular domain.' An agent understands the operation, but the description never distinguishes it from close siblings such as post_dataforseo_domains_tech_domains_by_technology_live, post_dataforseo_domains_tech_summary_live, or the aggregation variant.

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 the conditions that select this tool over its many domains_tech siblings, and no prerequisites (API credentials, rate limits) noted. The only contextual hint is the pricing note, which is billing information rather than usage direction.

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

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

Annotations already declare destructive=true, readOnlyHint=false, openWorldHint=true and idempotentHint=false, so the safety profile is covered. The description adds one genuinely non-derivable fact beyond the annotations: usage-based billing with a ~$0.012 default estimate, which explains why a data-retrieval-shaped call is billed/mutating. It says nothing about auth, rate limits, or the meaning of the 'live' semantics.

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 lean sentences with the returned-data payload front-loaded, followed by a clearly delimited pricing block. No filler or repetition; the only minor cost is that the pricing comment repeats what an annotation-style cost field might convey.

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 not be explained, and annotations carry the safety profile plus the description carries billing. What remains missing for a POST 'live' data endpoint is any indication of when to use it over its many siblings and any hint about the body structure. Adequate but with visible gaps.

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

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

The description contributes no parameter meaning at all — it only says 'parameters you specify.' The top-level body param itself carries no description (reported coverage 0%), though the nested item schema internally documents limit/offset/filters/order_by/offset_token in detail, so the schema still does most of the work. Baseline 3 is appropriate given the internal documentation offsets the missing wrapper 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 resource and payload: 'Whois data enriched with backlink stats, and ranking and traffic info from organic and paid search results.' That is enough to know what the endpoint returns, but it never distinguishes itself from siblings such as get_dataforseo_domains_whois_available_filters or the tech/aggregation endpoints, so an agent cannot use it to disambiguate the family on its own.

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 prerequisites, and no named alternative. 'For the domains matching the parameters you specify in the request' restates the obvious rather than telling the agent when this endpoint is preferable to the sibling Whois/tech tools.

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

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

Annotations already declare the safety profile (readOnlyHint=false, destructiveHint=true, idempotentHint=false, openWorldHint=true), so the description carries less burden. It does add useful behavioral context not present in annotations: the usage-based billing model with a $0.09 default request estimate and the note that Google Ads may return no data for certain keyword groups. It does not explain why the tool is flagged destructive or mention auth/rate behavior, leaving a 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?

The most prominent content is an API-migration warning about AdWords vs Google Ads that is irrelevant to invoking this endpoint, plus a templated pricing block. The actual purpose sentence is buried in the middle of the description rather than front-loaded, so the reader must dig for the operative 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?

An output schema exists, so return values need no explanation here. But for a mutation/billing endpoint whose only top-level parameter is an undescribed array wrapper, the description should clarify how requests are structured and what gets generated; instead it spends its space on legacy-API migration trivia.

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 top-level 'body' parameter is an array of task objects with no schema description (reported coverage 0%), and the description does not explain this wrapper structure, the batching semantics, or ordering. The one detail it does give — 'up to 20 keywords in the keywords array' — merely duplicates what the nested schema already documents, so it does not 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 operative sentence — 'provide relevant keywords for the specified terms... get keyword suggestions from Google Ads' — states a specific verb and resource, so the core purpose is discernible. However, the migration note about the legacy AdWords API dominates the opening, and the description never distinguishes this tool from close siblings like post_dataforseo_labs_google_keyword_suggestions_live or post_dataforseo_keywords_gads_search_volume_live. An agent must guess from the name alone which keyword tool 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?

The description says 'Set up to 20 keywords in the keywords array and get keyword suggestions,' which hints at a mechanical usage but offers no when-to-use versus alternatives, no prerequisites, and no exclusions. With many overlapping keyword/suggestion siblings available, this is a meaningful omission.

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 supply the safety profile (readOnlyHint=false, destructiveHint=true, idempotentHint=false, openWorldHint=true). The description adds genuinely useful behavioral context not in the annotations: usage-based billing with a default estimate of $0.09 USD and account-specific pricing. However, it says nothing about rate limits, synchronous vs task-based execution, or what 'destructive' means here. With annotations carrying the safety burden, this is an adequate 3.

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 text is short but poorly structured: it opens with a stray zero-width character and front-loads an irrelevant API-migration warning before any statement of purpose. Every sentence discusses migration or pricing rather than the tool's function, so the space is not spent on what an agent needs.

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 an output schema exists, return values need not be explained, and the annotations cover the safety profile. But for a keyword-search-volume endpoint with a complex nested body and a 1000-keyword limit, the description omits the essentials: what it returns, that it runs live/synchronously, and any keyword/location constraints. The critical purpose and usage gaps make it incomplete.

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 provides zero information about any parameter, and the top-level schema description coverage is reported as 0%. Although the deeply nested body fields (keywords, date_from/date_to, location_*) carry rich descriptions in the schema, the description does not compensate for the measured coverage gap as required. It adds nothing an agent could use to invoke the tool correctly.

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

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

The description never states what the tool does. Its two substantive sentences are about migrating from the legacy AdWords API and about pricing, neither of which describes the operation. An agent must fall back on the title ('Google Ads Search Volume Tasks') and the tool name to infer that it retrieves Google Ads search volume data for keywords, so the description itself is effectively tautological/boilerplate.

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 at all. The only condition stated ('if you're using the DataForSEO Google AdWords API, you need to upgrade') is a migration notice, not a routing instruction. No mention of alternatives such as post_dataforseo_ai_keyword_volume_live or post_dataforseo_keywords_gads_kw_for_keywords_live, and no indication of live-vs-task semantics.

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-retrieval endpoint ("will provide you with the keyword popularity data"), which conflicts with the annotations declaring readOnlyHint=false and destructiveHint=true. The only added behavioral note is the auto-inserted pricing block (usage-based billing, ~$0.0012 estimate), and no auth, rate-limit, or side-effect context is given. Description and annotations disagree on the safety 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?

Two sentences, purpose front-loaded, no filler; the pricing block is boilerplate but clearly demarcated. Nothing in the prose is wasted, though the content is thin rather than tight.

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 POST endpoint that sets a live task with a required keywords field inside a body array, the description omits how to select the engine type, how the array is structured, and the task-setting nature implied by its own title. An agent cannot call this correctly from the description alone.

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 for the sole top-level parameter (body) is 0% and the description says nothing about the request shape — no mention of keywords, date ranges, location, or time_range presets. The nested item schema happens to document those fields, but the description itself contributes zero parameter meaning and does not compensate for the reported 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 names a specific verb+resource ("provide you with the keyword popularity data from DataForSEO Trends") and scopes it to three engines (Google Search, Google News, Google Shopping). It is clear on what the tool returns, but it never distinguishes itself from near-neighbours such as post_dataforseo_ai_keyword_volume_live or post_dataforseo_keywords_gads_search_volume_live, which return adjacent keyword metrics.

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 guidance and no named alternative. The agent is told what data comes back but not in which situation this trends endpoint should be chosen over the many keyword-volume and keyword-overview siblings.

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

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

Annotations declare readOnlyHint=false and destructiveHint=true, yet the description's 'provide you with the Keyword Difficulty metric' describes a pure read/retrieval operation, so the two conflict. The description does add a useful behavioral detail — usage-based billing with a default estimate of $0.0734 — but it never addresses auth, rate limits, or the destructive classification, so transparency is weak and partly contradictory.

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 front-loaded with the core purpose and reasonably short, but the second and third sentences overlap heavily (both explain ranking in top-10 organic results), and the boilerplate billing block adds bulk without adding task-selection 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-value explanation is unnecessary, but the description leaves the single array parameter and its required nested fields entirely uncovered and provides no usage routing. For a bulk live endpoint whose only param is a structured array, this is incomplete.

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?

Reported schema description coverage is 0% at the top level (the 'body' array itself is undocumented), so the description carries the burden of explaining parameters — and it mentions none of them. It doesn't explain the required keywords array, the max-1000 limit within the array, or the language/location code requirements.

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 (Keyword Difficulty metric) and a clear scope (bulk, up to 1,000 keywords in one request), and it even defines what the metric means. It does not, however, distinguish this bulk endpoint from siblings like get_semrush_keyword_difficulty or post_dataforseo_labs_google_keyword_overview_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?

It states the 1,000-keyword bulk limit, which implies when the endpoint is appropriate, but gives no explicit when-to-use/when-not-to-use guidance and names no alternative sibling for single-keyword or cross-source lookups. An agent must infer the routing decision on its own.

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 the mutation/safety profile (destructiveHint=true, readOnlyHint=false), and the description does not contradict this. It adds genuinely useful behavior context the annotations lack: the usage-based billing model with a $0.1356 default estimate and the 1,000-target cap. It does not disclose return shape, but an output schema exists to cover that.

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 tight sentences front-load the core capability before the boilerplate pricing comment block. The pricing annotation is templated but the descriptive text itself wastes no words and is appropriately sized.

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, high-amplitude bulk endpoint with an output schema, the description covers capability, target limits, and cost but omits routing guidance and any parameter-level detail. It is adequate to identify the tool, not to invoke it optimally.

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 at 0% for the single top-level body parameter, so the description carries the burden of explaining parameters and largely fails to. It mentions the 1,000-target limit but says nothing about item_types, language/location codes, or other fields an agent must populate, adding little beyond the raw schema.

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

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

The description states a specific verb and resource ('provide you with estimated monthly traffic volumes') and identifies the scope ('up to 1,000 domains, subdomains, or webpages'), which cleanly distinguishes it from keyword-oriented Labs siblings. It stops short of naming the closest sibling (e.g., domain_rank_overview) or contrasting its bulk nature against single-target 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?

There is no explicit when-to-use guidance, no conditions for selecting this over alternatives, and no exclusions. The 'up to 1,000 targets' note is a scope limit, not routing guidance, leaving the agent to infer usage 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?

The description frames the endpoint purely as informational retrieval ('provide you with ranking and traffic data', 'you will be able to review'), which reads as a read-only query. The annotations declare readOnlyHint=false and destructiveHint=true, directly conflicting with that framing. The pricing/billing disclosure is a genuinely useful addition, but the core behavioral claim is inconsistent with the structured metadata.

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 tight sentences front-load what the tool returns, followed by a clearly demarcated billing note. Apart from a stray leading zero-width character, nothing is wasted, although the pricing block is verbosity imposed by the platform rather than the author.

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 the description need not describe return values, and it correctly focuses on inputs and output scope. However, for a live paid endpoint with a single nested array parameter and no usage guidance, it omits the required target format and any indication of when this overview is preferable to the other labs endpoints, leaving the definition at minimum viability.

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 reported as 0% (the single 'body' array property has no description), so the description must compensate for parameter meaning, and it does not: 'target', 'limit', 'offset', 'language_code/name' and 'location_code/name' are never referenced or explained. The nested item-level schema does document them well, which partially rescues usability, but the description itself adds no 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?

The description states a concrete resource and verb: ranking and traffic data from organic and paid search for a specified domain, including SERP ranking distribution and estimated monthly traffic. This is materially more specific than the title alone. It stops short of distinguishing the tool from close siblings such as google_historical_rank_live or google_subdomains_live, which return similarly framed domain-level metrics.

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 tool over the many sibling domain/keyword endpoints, and no mention that the target domain must be bare (no https:// or www.), which is a practical prerequisite for a successful call. Usage is left entirely to inference 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 already declare the safety profile (readOnlyHint=false, destructiveHint=true, openWorldHint=true), so the bar is lower. The description adds genuinely useful context beyond the schema: historical coverage start (August 2021) and a usage-based billing estimate ($0.1356 default). It does not reconcile the destructive/readOnly annotations with what is plainly a data-retrieval endpoint, nor mention auth or rate limits.

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

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

Purpose is front-loaded in the first sentence and the whole body is short. The pricing block earns its place as cost context, though the leading zero-width characters before the first sentence are noise.

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 annotations cover behavior. The description covers data meaning, historical range, and cost, but omits the batch/array request mechanics and the relationship to the near-identical non-historical siblings, leaving selection ambiguity.

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 reported at 0% (the single 'body' property is undocumented), but the nested item properties carry very rich descriptions. The description compensates partially by naming keywords, location, and language, matching the request fields, though it says nothing about the array/body wrapper or the 700-keyword / 80-character limits.

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 ('provides Google historical keyword data for specified keywords') and enumerates the returned metrics (search volume, CPC, paid-competition values, monthly searches, trends), so an agent knows exactly what it gets. The 'historical' qualifier implicitly distinguishes it from the non-historical sibling post_dataforseo_labs_google_keyword_overview_live, but no sibling is named 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?

It states data coverage (since August 2021) and that availability varies by keyword/location/language, but never says when to pick this tool over alternatives such as post_dataforseo_labs_google_keyword_overview_live or post_dataforseo_keywords_trends_explore_live. No when-not guidance or prerequisite conditions 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 (readOnlyHint=false, destructiveHint=true, idempotentHint=false, openWorldHint=true) already carry the safety profile, so the description is not the main burden carrier. It does add real value by disclosing usage-based billing with a default estimate of ~$0.1356 USD, which is behavioral context an agent cannot get from annotations or schema. It does not reconcile the destructive hint against what reads like a pure data-retrieval endpoint, which is a notable unexplained tension.

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 a single well-formed sentence, followed only by a bounded pricing note. Nothing is padded or redundant. The stray leading whitespace/zero-width character is sloppy but not materially harmful.

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 cost disclosure is a plus. What is missing is the operational context an agent needs to invoke it correctly: that a target domain is effectively required, that location/language must be supplied in one of two forms, and how this differs from the sibling rank-overview tool. Adequate but with clear gaps.

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

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

Schema description coverage is reported at 0% for the top-level parameter, and the description contributes no parameter meaning at all: it never mentions target, date_from/date_to, location, or language requirements. The nested item schema does document these fields in detail, which prevents a 1, but the description itself does nothing 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 names a clear resource and scope: historical rankings and traffic for a specified domain, spelled out as SERP ranking distribution plus estimated monthly organic and paid traffic. That is specific enough that an agent knows what it returns. It never distinguishes itself from the closest sibling, post_dataforseo_labs_google_domain_rank_overview_live (current vs. historical), 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 such as the required target domain or the location/language pair, and no routing advice among the many sibling Labs endpoints. The only usage-adjacent content is the billing footer, which addresses cost, not selection. Nothing is misleading, but the agent gets no help choosing this over the non-historical rank overview.

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, non-idempotent, and destructive/readOnly=false, so the safety profile is largely covered by structured data and nothing here contradicts it. The description adds genuinely useful cost transparency via the usage-based billing note ($0.132 default estimate), but omits other material behaviors such as the required location/language pairing and the double-charge when include_clickstream_data is enabled.

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

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

Two substantive sentences plus a pricing block, front-loaded with the core purpose and free of filler. The pricing banner is boilerplate but short and informative; the leading stray character is a minor formatting blemish.

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 the pricing note covers cost. Still missing for an agent to use it confidently are differentiation from the sibling keyword endpoints and confirmation of the location/language requirement, which the schema forces but the description never signals.

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?

Only one top-level parameter (body) exists, and the schema's nested property descriptions are extremely detailed, so the schema carries the parameter burden. The description adds just one hint ('seed keywords specified in a POST array'), which does not compensate for the reported 0% coverage at the top level nor mention the required keywords/location 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 description states a specific verb and resource: it generates search terms ('keyword ideas') relevant to the categories of the seed keywords supplied. It explains the selection algorithm (same-category matching), which separates it conceptually from siblings. However, it never names or contrasts with the very similar siblings (post_dataforseo_labs_google_keyword_suggestions_live, post_dataforseo_labs_google_related_keywords_live), so an agent must infer the difference.

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 guidance and no mention of alternatives, despite a crowded sibling set of near-identical keyword-expansion endpoints. The description only says what the endpoint does, leaving the agent to guess which of the several keyword endpoints to select.

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 idempotentHint=false, yet the description neither explains that destructive framing nor notes auth/permission needs. It does add genuinely useful context not in the annotations: the ~$0.0254 default cost estimate and the doubling of price when include_clickstream_data is enabled.

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 front-loaded sentences that lead with what data is returned, plus a clearly delimited pricing boilerplate block. No wasted prose, though the boilerplate is not the description's core job.

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-value detail is not required, and the tool is a single-parameter live query. However, the description omits the input constraint pairs the schema enforces (language_code/language_name and location_code/location_name are each required in some combination), which an agent needs to call it successfully.

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 top-level recommended parameter 'body' has no description of its own (0% coverage per the signals), and the description adds only the include_clickstream_data behavior. The nested oneOf schema is in fact well documented, so the description neither compensates for nor detracts from the schema; a baseline 3 fits.

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 ('Google keyword data for specified keywords') and enumerates the returned metrics (CPC, competition, search volume, search intent, monthly searches, SERP/backlink info). Clear enough to call, but it never distinguishes itself from sibling keyword tools such as keyword_ideas, keyword_suggestions, or google_search_intent.

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 prerequisites, and no routing against the many sibling keyword endpoints. The only conditional hint is that clickstream data can be obtained by setting include_clickstream_data, which is a parameter note rather than usage guidance.

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

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

Annotations already declare the safety profile (readOnlyHint=false, destructiveHint=true, idempotentHint=false, openWorldHint=true), so the description's main added value is the usage-based billing estimate ($0.132 default), which is genuinely useful behavioral context for a paid API call. It does not explain why the tool is flagged destructive, nor does it repeat or contradict that flag, so it neither contradicts nor enriches the annotation set further.

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?

Purpose is front-loaded in a single tight sentence, followed by a clearly delimited pricing block. Nothing is wasted, though the brevity edges toward under-specification given the tool's numerous siblings.

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 cover the safety profile. What is missing is selection context: for a keyword-research endpoint sitting among roughly ten near-neighbor DataForSEO Labs tools, the agent gets no signal on when this one is preferred.

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 nothing about parameters, but the reported 0% coverage reflects only the top-level 'body' property; the nested request fields (keyword, location_code, filters, order_by, include_clickstream_data, etc.) are documented in extensive detail inside the schema itself. Since the schema already carries the parameter semantics, the description adding nothing lands at the baseline rather than a penalty.

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 deliverable ('search queries that include the specified seed keyword'), which is a real verb+resource and even disambiguates it semantically from related-keyword tools. It never names a sibling tool, however, so the agent must infer the boundary with tools like post_dataforseo_labs_google_related_keywords_live or post_dataforseo_labs_google_keyword_ideas_live on its own.

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 statement, no when-not-to-use statement, and no mention of any alternative among the many keyword-research siblings. The only usage signal is implied by the name and the one-line definition.

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 the operational profile (openWorld, destructive, non-idempotent, non-readOnly), so the bar is lower. The description usefully adds cost context (default $0.0156 estimate, usage-based billing) and enumerates the returned fields (product categories, search volume, trend, CPC, competition). It does not explain why a data-retrieval endpoint carries destructiveHint=true, leaving a mild inconsistency unaddressed.

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?

Purpose is front-loaded in the first sentence, and the bounded pricing block is clearly delimited. The orphaned fragment 'View the element.' is a leftover artifact that earns nothing, but overall the text is tight and appropriately sized.

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 an output schema exists, return values need not be explained, and cost is covered. However, the description omits usage routing against sibling keyword tools and any mention of the location/language requirement, leaving gaps for an agent selecting and invoking this among ~60 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?

Reported top-level schema coverage is 0%, but the sole top-level parameter ('body') embeds a nested schema that richly documents keyword, depth, limit, filters, and location/language fields. The description adds the 'search depth' concept and the 4680-keyword ceiling, overlapping what the nested schema already states, but does not surface the required location/language selection constraints.

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

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

The description names a specific verb+resource ('The Related Keywords endpoint provides keywords') and identifies the data source ('Searches Related to' SERP element), which separates it from sibling keyword tools like keyword_ideas or keyword_suggestions. It does not explicitly name those siblings, but the source-of-data framing makes the purpose distinguishable.

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 routing against the many sibling keyword endpoints (keyword_ideas, keyword_suggestions, keyword_overview). The description describes the endpoint's output but never states the condition under which an agent should prefer it over those 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?

Annotations already declare openWorldHint=true, destructiveHint=true, non-idempotent, non-readOnly, so safety is covered structurally. The description's only added value is a billing estimate ($0.015 default, not fixed, account-specific), which is genuinely useful cost context. Beyond that it discloses nothing about what the call does, its latency, or what data it returns.

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, but the content is boilerplate billing HTML-comment scaffolding plus a stray zero-width character. Brevity here reflects missing content rather than efficient editing, so it cannot earn a conciseness credit.

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 needn't be described, but for a paid, open-world DataForSEO Labs endpoint with a rich filter/target body, the description omits purpose, usage, and parameter semantics entirely. It is nowhere near complete enough for an agent to invoke it correctly.

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

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

The tool has one parameter ('body') with a reported 0% schema description coverage at the top level. The description contributes zero information about parameters, targets, filters, or the required 'target' domain field, leaving the agent to reverse-engineer everything from the nested schema alone.

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 contains no statement of what the tool does at all — it is a pricing note (

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 whatsoever on when to use this tool versus the many sibling labs endpoints (e.g. google_domain_rank_overview, google_serp_competitors, google_subdomains). Usage must be inferred 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 declare readOnlyHint=false, destructiveHint=true, idempotentHint=false and openWorldHint=true, so the safety profile is already carried structurally. The description adds genuinely decision-relevant context beyond that: per-request usage-based cost (~$0.0073) and variable account pricing, which matters for a billed live call. It does not, however, explain the non-idempotent/destructive annotation, leaving a mild unexplained tension with the 'provide you with data' framing.

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 tight sentence covering the operation, the per-keyword granularity, and the result shape, followed by a pricing block. There is a stray leading whitespace character and the billing banner is boilerplate, but the core content is front-loaded and waste is minimal.

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 description does state the input ceiling and cost. What remains missing for a live, paid, non-idempotent endpoint is any routing guidance against the many sibling keyword endpoints and any note on batching or latency behavior.

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 body parameter is undocumented), but the nested properties for 'keywords' and 'tag' carry rich descriptions, and the description restates the meaningful constraint that up to 1,000 keywords are accepted. It adds little else — requiredness, lowercasing, and encoding come only from the schema. Baseline 3 given one parameter with schema-side detail.

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: obtain search intent data (intent + probability, plus secondary intents) for up to 1,000 keywords. This is distinguishable from sibling Labs endpoints like keyword_overview or keyword_ideas by subject matter, though the description never names or contrasts itself against 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 when-to-use guidance: no statement of when search-intent classification is the right choice versus keyword_volume, keyword_suggestions, or the AI keyword tools in the sibling list. The only usage-adjacent information is the 1,000-keyword input cap, which is a limit rather than a selection criterion.

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 this purely as a data-retrieval call ('will provide you with a list of domains'), yet the annotations declare readOnlyHint=false and destructiveHint=true. A reader who trusts the description would not expect a destructive operation, producing an inconsistent behavioral picture. The only genuine value-add is the pricing note ($0.0192 default estimate, usage-based billing), which is useful but does not resolve the read/write mismatch.

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

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

Two substantive sentences, front-loaded with the endpoint's purpose and output metrics, followed by a clearly demarcated pricing block. It is well sized with little waste; the stray zero-width character and the HTML-comment wrapper are minor noise but do not impede 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?

An output schema exists, so return values need no explanation. However, this endpoint's real complexity lives in the body object's fields (required language/location pair, keyword rules and limits, filter/order_by syntax), none of which the description addresses. For a payload with that many conditional requirements, the description leaves an agent under-informed.

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% for the single top-level 'body' parameter, and the wrapper is an opaque array of objects that the description never explains. The description only echoes that keywords are the input, adding no syntax, format, or requirement detail (e.g., language/location pair requirement, 200-keyword cap, filter/order_by structure) beyond what the schema itself says.

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: it returns 'a list of domains ranking for the keywords you specify,' along with the metrics (SERP rankings, rating, estimated traffic, visibility) those domains gain. That is a clear, self-contained statement of what a SERP-competitors endpoint does. It never names or contrasts itself against siblings like get_semrush_organic_competitors or post_dataforseo_backlinks_competitors_live, 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, when-not-to-use, or alternative-tool guidance anywhere in the description. With several near-neighbor competitor tools in the sibling list (SEMrush organic competitors, DataForSEO backlinks competitors), an agent has no signal to route between them.

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, destructiveHint=true, readOnlyHint=false and non-idempotence, so the safety profile is largely carried by structured data. The description adds genuinely useful behavioral context by disclosing usage-based billing and a $0.1356 default estimate, which explains the cost aspect. It does not cover auth requirements, rate limits, or why the annotation marks it destructive.

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

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

Two focused sentences front-load the purpose and output before the injected pricing block. Every sentence earns its place, though the boilerplate pricing marker is not part of the tool's own authoring.

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 cover safety. However, for the sole input parameter the description offers no formatting guidance, and it omits any when-to-use context against the crowded domain-oriented sibling set, leaving noticeable gaps.

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

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

Schema description coverage is reported as 0% at the top level (the single 'body' parameter is undocumented), so the description should compensate. It only implies a domain target via 'the specified domain' and gives no format guidance (e.g. no https:// or www. prefix), leaving the required 'target' field to the nested 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?

States a specific verb and resource: 'provide you with a list of subdomains of the specified domain' plus the ranking and traffic distribution returned. The scope is distinct from most siblings, though it never names a sibling to contrast against (e.g. domain_rank_overview or relevant_pages), so differentiation is implicit rather than 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?

No statement of when to use this endpoint versus alternatives. With many sibling Labs endpoints that also take a domain (domain_rank_overview, relevant_pages, serp_competitors), the agent gets no signal about which domain-oriented tool fits which question. Usage context is only loosely implied by the output described.

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 frames this purely as a read ('returns a list of pages'), which undersells that it creates a billable task. The pricing note (usage-based, ~$0.012 default) is a genuinely useful behavioral disclosure beyond the annotations, but asynchronous task-creation semantics, non-idempotency, and authorization needs are never mentioned.

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 tight sentences with the core behavior front-loaded, followed by a clear billing note. No redundancy or padding, though the pricing boilerplate is not tool-specific.

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 task-initiating paid endpoint the description omits the dependency on a prior submit call and the task id, and gives no sense of asynchronous completion. An agent could not call this correctly from the description alone.

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 at 0% for the top-level `body` property, so the description bears the burden of compensating and does not: it mentions no fields at all (id, url, limit, offset, similarity threshold, tag). The schema's nested descriptions do carry some of this, but the description itself adds 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?

Uses a specific verb+resource: returns pages with content similar to a specified page, plus similarity index and performance data. This clearly separates it from siblings like post_dataforseo_on_page_duplicate_tags or keyword_density. It stops short of naming an alternative or scoping condition, so it is clear but not sibling-differentiating.

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 prerequisites, and no mention of alternatives. Critically, it never says the required task `id` must come from the Task POST (submit) endpoint first, nor when to choose this over the other OnPage 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 destructiveHint=true, readOnlyHint=false, openWorldHint=true and idempotentHint=false, so the safety profile is covered structurally. The description adds only a usage-based billing note ($0.005 estimate) — useful cost context, but templated, and it never explains the destructive marker, the live/real-time nature of the call, or that a target URL is mandatory. Given annotations carry the safety burden, this is thin but not contradictory.

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, appropriately short, with the functional statement front-loaded. The second half is boilerplate pricing markup that provides some value but is generic across the API surface rather than tailored to this tool, so it doesn't 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 live, billed, non-idempotent POST whose request object contains ~14 optional tuning fields, the description is far too thin. The output schema means return values need not be explained, but the description omits the required URL, the live-audit semantics, and any indication of what distinguishes this endpoint from its many OnPage 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 single top-level 'body' parameter has no description in the schema (coverage reported at 0%) and the tool description supplies nothing about it. Although the nested property schemas are individually rich, the description does not compensate for the top-level gap — the agent gets no statement that body is an array of request objects requiring an absolute 'url'.

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 underlying technology (Google's open-source Lighthouse project) and the general subject ('measuring the quality of web pages and web apps'), but it never states the tool's action on a concrete resource — no mention that it runs a live Lighthouse audit against a supplied URL. It also offers no differentiation from siblings like post_dataforseo_on_page_submit or get_dataforseo_on_page_lighthouse_audits, leaving the agent to infer the purpose 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 when-to-use guidance, no conditions, and no named alternative. The agent is not told when a live Lighthouse run is preferable to the summary, content parsing, or other OnPage POST siblings, nor when this should be avoided.

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 billing behavior (usage-based, ~$0.012 estimate), which is genuinely useful context beyond the annotations. However, it never explains side effects, the task-based retrieval flow, or why a POST with destructiveHint=true is being used to fetch links; the retrieval framing sits uneasily with destructiveHint=true, though the description never explicitly asserts read-only semantics, so this reads as tension rather than a hard 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?

The purpose is front-loaded in the first sentence, and the taxonomy of link types is compact and scannable. The bulk pricing comment block is boilerplate rather than authored content, and the six link-type definitions are arguably response-content detail that an agent doesn't strictly need to invoke the tool, keeping this just short of a 5.

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