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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds valuable behavioral context beyond that: it parses robots.txt and makes live GET requests using each bot's User-Agent, and it surfaces both robots.txt blocks and UA-based gating. It does not mention timeouts, rate limits, or network cost, but the added detail is substantial.

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 tightly packed sentences with no filler. The core action is front-loaded, and the long crawler list is justified because those names are operationally relevant to the tool.

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

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

An output schema exists, so the description need not explain return values. Annotations cover the safety profile and the description covers the core behavior, making it nearly complete. It could still be clearer about when this tool is preferable to sibling audit tools, but no critical invocation detail 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 schema already documents url, bots, and fetch_with_ua. The description names the default bot list, which lightly reinforces the bots parameter, but adds no syntax or behavioral meaning beyond what the schema already provides.

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

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

States a specific verb and resource: verify AI crawler access to a URL. It names the crawler set and explains that it parses robots.txt and performs live GETs, clearly distinguishing it from broader audit siblings like audit_sitemap or audit_llms_txt.

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 makes the use case inferable — diagnosing whether AI crawlers can fetch a page — but does not explicitly say when to choose this over sibling audit tools, nor does it state exclusions or prerequisites. Usage is implied rather than spelled out.

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). The description adds useful procedural context: sitemap parsing, nested index following, and the slower/richer tradeoff of fetch_titles. However it doesn't state limits, pagination, or failure modes beyond what the schema already says.

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-loading the artifact and spec, then the pipeline, then the key parameter tradeoff. No 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?

Annotations carry safety, an output schema exists, and schema descriptions are complete. The description fills the remaining gaps (pipeline behavior, fetch_titles tradeoff). Minor gap: no mention of rate limits or failure behavior when the sitemap is unreachable.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the fetch_titles tradeoff and the output structure (H1+description+sectioned links), which helps the agent reason about parameter selection beyond the schema's per-parameter notes.

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 (generate) and artifact (llms.txt file) and names the spec URL. Distinct from every sibling (audit_*, citations_*, competitors_*, etc.), none of which produce an llms.txt.

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

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

The description explains what it does and hints at the fetch_titles tradeoff, but provides no explicit when-to-use vs when-not-to-use guidance or alternative selection against 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 readOnly, idempotent and non-destructive behavior, so safety is covered. The description adds real value beyond that: it discloses the parsing method (every JSON-LD block and microdata node), the validation rules applied per @type, and that malformed JSON-LD is flagged. It does not discuss cost, rate limits, or failure modes.

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 dense sentences, front-loaded with the verb and resource, and the inline @type examples earn their place by showing the depth of validation. Slightly run-on, but there is no 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 and the description still summarizes the return shape (issues list plus valid/invalid verdict), so return values are covered. Annotations cover the safety profile. The main omission is routing logic versus the very similar audit_structured_data sibling.

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 parameter and schema description coverage is 100%, so the schema already defines the url fully. The description adds only that both JSON-LD and microdata at that URL are validated, which is scope rather than parameter syntax. 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 specific verb (validate) and resource (schema.org structured data) and enumerates exactly what is parsed and checked (JSON-LD blocks, microdata nodes, required fields per @type). However, it never distinguishes itself from the near-identical sibling audit_structured_data, so an agent cannot tell the two apart from the description 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?

Gives a concrete trigger: use it to diagnose structured-data bugs that predict_citation flags but cannot explain. That is a clear when-to-use context. It does not state when to prefer audit_structured_data instead, nor any preconditions (e.g., page must be reachable).

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 safety (readOnly, idempotent, non-destructive, open-world), so the bar is lower, and the description adds genuinely useful behavior: results sorted worst-score-first and 'zero engine keys needed'. That keyless-run detail is valuable operational context not in the annotations. It stops short of describing pagination/truncation behavior, which the limit param implies.

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?

Four short sentences, front-loaded with the action and ending with a differentiating benefit. No filler; each sentence adds either mechanism, output shape, value, or constraint.

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 needn't explain return values and doesn't try. It supplies the ordering and keyless-run context an agent needs, though it omits guidance on concurrency/rate-limit tradeoffs and the sibling overlap already noted.

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 limit, concurrency and sitemap_url are fully documented by the schema; baseline 3 applies. The description adds no parameter-specific meaning, and its claim of running on 'every URL' sits slightly in tension with the limit/concurrency slicing controls.

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 chain: fetch a sitemap, run predict_citation on every URL, and it names the output ordering. This clearly separates it from generic audit siblings like audit_schema or audit_llms_txt. However, it does not differentiate itself from the close sibling audit_sitemap_map, which an agent would need help distinguishing.

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 rather than explicit – 'Surfaces systemic issues across a whole site in one pass' conveys the scenario (whole-site diagnostics) but nowhere states when to prefer this over audit_sitemap_map or predict_citation directly. No exclusions or prerequisites are given.

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

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

Annotations already declare read-only, idempotent, non-destructive behavior, so the safety profile is covered. The description adds the important operational dependency (prime the cache first) and the per-URL reporting content (whether/ how many queries and engines cited it), which the annotations do not 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 tight sentences, front-loaded with the core operation, followed by the inverse framing and the prerequisite. No filler; every sentence carries unique 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 explained, and the description covers the operation, the differentiation from siblings, and the cache-priming prerequisite. Nothing an agent needs to call 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?

Schema coverage is 100%, so limit, since, domain and sitemap_url are all documented in the schema. The description adds no syntax or format detail beyond what the schema provides, so the baseline 3 applies.

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

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

States a specific verb (cross-reference) and resources (sitemap vs. citation cache), then explicitly frames itself as the inverse of audit_sitemap. An agent can distinguish it from the sibling without opening the 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?

Names the sibling it contrasts with (audit_sitemap) and states the precondition to use it at all: the cache must be primed via check_citations or run_panel first. This is explicit alternative and prerequisite 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 cover readOnly, idempotent, openWorld and non-destructive. The description adds genuinely useful behavior beyond that: it fetches the page, detects existing types, returns ready-to-paste templates, pre-fills from metadata, and marks incomplete fields with FILL:. That return-shape and convention context is valuable.

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 a one-line purpose, followed by useful specifics. The parenthetical signal-to-type mapping is dense but each example earns its place by clarifying detection behavior.

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 annotations carrying the safety profile, the description need only cover purpose and behavior, which it does. The only residual gap is the unstated relationship to the sibling audit_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 coverage is 100% and there is a single url parameter already documented in the schema, so the description adds little parameter-level meaning beyond noting the page is fetched. 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 specific verb (suggest) and resource (missing JSON-LD additions) with clear scope, and enumerates exactly which types it can propose from which signals. It does not explicitly distinguish itself from the sibling audit_schema, which likely inspects existing schema markup, so an agent must infer the boundary.

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

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

The description implies when the tool is useful (page is missing structured data but content signals suggest types), but gives no explicit when-to-use/when-not or naming of alternatives like audit_schema. Usage 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 cover the safety profile (openWorldHint, idempotentHint, destructiveHint=false), so the bar is lower. The description still adds genuine operational context beyond the structured fields: an engine API key is required and the tool auto-picks the first available engine, which affects setup and failure modes.

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 with zero padding; the output is described first and the usage condition and prerequisite follow. Every clause earns its place.

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

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

With an output schema present and full parameter coverage, the description need not explain return values. It covers purpose, usage trigger, and the API-key prerequisite, leaving only sibling disambiguation as 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% and the enum, defaults, max_results bounds, and perplexity_model override are all fully documented in the schema. The description adds no parameter-level detail beyond naming the engines, so the baseline 3 applies.

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

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

States a specific verb and resource: 'Return URLs cited by an AI engine ... for a query', and enumerates the engines covered. It does not distinguish itself from the many citations_* siblings (evidence, provenance, freshness, predict), so an agent cannot tell from the description alone which citation 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?

Gives a clear use condition: 'Use this when an agent or user wants to see what sources an AI search engine grounds answers on.' There is no when-not guidance and no explicit routing to alternative sibling tools such as citations_evidence or citations_provenance.

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 safety (readOnly, idempotent, openWorld). The description adds valuable behavioral detail beyond annotations: the internal call to check_citations, the window/quote extraction logic, and the engine-specific 'not found' behavior for Bing/Brave. Doesn't mention rate limits or performance, but adds meaningful context.

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

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

Three dense sentences covering purpose, mechanism, and limitations. Front-loaded with the core action. Slightly packing mechanism + edge case into one sentence, but no wasted words.

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 output schema exists (so return structure needn't be described), the description covers purpose, internal dependency, extraction logic, and key limitation (engine coverage). Complete for an agent to invoke correctly.

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

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

Schema coverage is 100% and each parameter has a clear description. The description reinforces the engine limitation ('engines without raw_answer') which aligns with the schema's enum description, adding no new semantic detail beyond what the schema provides. 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?

Specific verb+resource (extract cited snippet from raw answer for each citation). Explicitly distinguishes from the sibling citations_check by stating it 'Calls check_citations, then...' and clarifies the why-vs-that distinction.

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

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

Gives a clear usage framing ('Use to see why an engine cited a URL, not just that it did') and notes an important limitation (returns 'not found' for Bing/Brave). However it doesn't explicitly contrast against the other citations_* siblings like citations_provenance.

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, openWorld, non-destructive), so the bar is lower, yet the description adds real value: it discloses the multi-step behavior (calls check_citations, then collects dateModified per URL), the scoring model (0-100, halflife=365d), and the bucket taxonomy (fresh/current/stale/ancient/unknown). It does not mention latency or cost of querying an AI engine, which is the main 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?

Three compact sentences, front-loaded with the action and scoring outcome, followed by the workflow and the motivation for using it. No filler and no repetition of schema fields.

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 enumeration is not required, and the description still summarizes the headline outputs (score plus per-URL buckets). Combined with the annotations covering side-effect semantics and 100% schema coverage for inputs, an agent has everything needed to invoke this correctly.

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

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

Schema description coverage is 100% and the engine enum plus max_results bounds are fully documented in the schema, so the baseline is 3. The description adds no parameter-level detail beyond what the schema already carries (e.g., no guidance on picking an engine or tuning max_results).

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?

Names a specific verb (Score) and resource (recency of pages cited for a query), and distinguishes itself from neighbors: it delegates citation fetching to check_citations and its output is a freshness score, not a citation list. An agent can separate it from citations_check, citations_trend, and citations_provenance 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?

Gives a clear context for use ('Surfaces queries where AI cites old content - opportunity to ship fresher'), which tells the agent when this tool is the right pick. It does not name alternative tools or state exclusions, so it stops short of explicit when-not guidance.

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

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

Annotations already cover safety (readOnly, idempotent, non-destructive, openWorld), but the description adds genuinely new behavioral context: the entirely heuristic, no-LLM execution model and the shape of what comes back (0-100 score, grade, signal breakdown, ranked fixes). It still does not state latency 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?

Three compact sentences, no filler, with the core purpose front-loaded and the heuristic/return details following. Every clause conveys distinct 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 single-parameter, fully documented tool with an output schema, the description supplies the method and return shape needed to call it correctly; return-value detail is not required since an output schema exists. The only real gap is sibling routing, which is a usage concern rather than missing operational 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?

Only one parameter with 100% schema description coverage, so the schema already carries the semantics (absolute http(s) URL). The description adds no constraint or format detail beyond that, making the baseline 3 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 specific verb (score) and resource (citation likelihood for a URL) and enumerates the public signals inspected, so the agent knows exactly what capability this is. It does not, however, distinguish itself from close siblings like citations_check or citations_evidence, so the boundary must be inferred.

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 explicit when-to-use or when-not-to-use guidance, and the many citations_* siblings are never referenced. Usage is only implied by the phrase 'No LLM fired - all heuristic', which suggests a fast, cheap predictive pre-check rather than a full audit.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds real context beyond that: it names the engines queried by default, the return shape, and how to interpret engine_count. It does not discuss latency, rate limits, or cost of fanning out across engines, which would fully round it out.

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 the core action and output shape, with the engine_count interpretation placed last as a useful reading aid. No filler sentences, though the trailing interpretation sentence slightly overlaps with output-schema territory.

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, idempotent fan-out tool with annotations covering safety, 100% schema coverage, and an output schema already present, the description is essentially complete. It even voluntarily explains return values despite the output schema existing, which covers the remaining 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?

Schema description coverage is 100%, so all three parameters (query, engines, max_results) are already documented in the schema with strong detail (enum list, maxItems, defaults, defaults-on-omission). The description adds nothing parameter-specific, so the baseline 3 for high coverage is correct.

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 action (fan a query out across multiple AI engines) and resource (per-URL cross-engine citation consensus), and goes further to name the exact outputs (per-URL engine lists, consensus_urls). This is specific enough to distinguish it from sibling tools like citations_evidence or citations_check that lack the multi-engine consensus angle.

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 rather than stated: the description explains engine_count semantics (1 = engine-specific, high = strong signal), which hints at when the tool is valuable, but it never names an alternative or states an explicit when/when-not condition against siblings like citations_evidence. No exclusions or 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?

Goes well beyond the annotations by disclosing that it is cache-only, makes no AI-engine API calls, consumes no API quota, requires no auth, and has no rate limits. It also clarifies the snapshot file location and the empty-series behavior when no snapshots exist, all of which materially affect calling decisions.

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 purpose in the first clause, then layers behavioral and return details. Slight redundancy between 'cache-only... costs no API quota' and 'No auth required. No rate limits,' and the return-value enumeration partly overlaps the existing output schema, but overall it is dense and earns its length.

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?

Complete for a read-only reporting tool: purpose, prerequisites, cache semantics, return shape, and edge case (empty series) are all covered. With an output schema present, the added interpretation of the deltas is a bonus rather than a required element.

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 both parameters are already documented. The description adds only marginal meaning ('use since to restrict the time window'), restating what the schema says, so the baseline of 3 is appropriate.

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

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

States a specific verb (report) and resource (citation rate over time), scoped clearly to 'a panel from stored snapshots'. This distinguishes it from siblings like citations_check (point-in-time) and citations_freshness, letting an agent differentiate 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?

Explicitly names the prerequisite sibling ('Use panel_run to accumulate snapshots first') and explains the role of the since parameter for narrowing the window. It gives clear usage context but does not state when-not to use it versus alternatives like citations_check or panel_track.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond that: results are aggregated by registered domain, ranked by cross-engine consensus, and include per-engine breakdown plus top URLs. It does not mention latency or cost of fanning out across up to seven engines.

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 tight sentences, front-loaded with the core action and its output shape, then the intended use case. No redundant restatement of the name or title and no 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-value explanation is not required, and the description still summarizes the return shape (consensus ranking, per-engine breakdown, top URLs). Parameter docs are fully covered by the schema. The only gap is practical guidance around multi-engine fan-out cost or latency for a tool with a 7-engine open-world dependency.

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

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

Schema description coverage is 100%, so all five parameters (query, top_n, engines, max_results, exclude_domains) are already documented in the schema itself. The description reinforces the domain-level grouping concept but adds no syntax or default detail beyond the schema, so the baseline 3 applies.

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

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

The description opens with a specific verb+resource: 'Fan a query across engines and aggregate citations by registered domain (not URL).' The parenthetical explicitly disambiguates the aggregation granularity, and the response shape (ranked domains with per-engine breakdown) is stated so an agent can distinguish this from siblings like competitors_compare and competitors_compete.

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

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

'Use to identify the canonical competitor set for a query - the domains every engine treats as authoritative' gives clear context for when to reach for this tool. However, it never names or excludes the closely related competitors_compare/competitors_compete siblings, leaving the agent to infer the boundary 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 readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is fully covered by structured data. The description adds that the tool batches predict_citation across URLs and emits a divergence list, which is useful framing, but it discloses no auth, rate-limit, or cost characteristics of the batch call.

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

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

Two sentences, zero filler, with the core operation front-loaded and the usage rationale following. Every clause earns its place.

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

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

An output schema exists, so return values need not be detailed, and the annotations carry the safety profile. The description covers purpose, usage, and output shape, which is sufficient for a single required parameter with full schema coverage, though sibling disambiguation is absent.

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

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

Schema description coverage is 100% and the single parameter is fully documented in the schema, including the 2-10 bound and the 'one is typically yours, rest are competitors' convention. The description merely restates the 2-10 range, adding no syntax or format detail beyond the schema. Baseline 3 applies.

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

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

States a specific verb and resource: runs predict_citation over 2-10 URLs and returns a side-by-side signal table plus divergent signals. This is clear and distinguishable from the single-URL citations_predict. It does not, however, explicitly differentiate itself from the sibling competitors_compete or competitors_canonical_set, 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?

The second sentence gives concrete when-to-use guidance: comparing your URL against top-cited competitors for the same query. It conveys the intended scenario well but names no alternative tool or exclusion condition, so there is no routing help beyond 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, destructiveHint=false and openWorldHint, so the safety profile is covered. The description adds genuine behavioral value by disclosing that this triggers two sequential sub-calls and summarizing the returned aggregate (your score, competitor average, gap), which implies latency/cost characteristics.

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 tight sentences: the deliverable first, the call sequence second, the return shape third. No filler and nothing buried.

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 a full parameter schema, rich annotations, and an output schema, the description only needs to explain the composite behavior and it does. Minor gap: it doesn't hint at engine/failure behavior when a sub-call like check_citations has no key available, but that is largely covered by the 'auto' schema 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 100% and every parameter (query, engine, your_url, max_competitors) is documented in the schema, including the enum and the max-9 cap nuance. The description adds no parameter-level detail beyond what the schema already provides, so the baseline 3 applies.

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

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

States a specific deliverable ('end-to-end competitive snapshot for a single query') and names the exact sub-tools it orchestrates (check_citations, compare_domains), which distinguishes it from the plain single-purpose siblings like competitors_compare and citations_check.

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 pipeline description makes the usage context clear: use it when you want a one-shot competitive snapshot rather than stitching the two underlying calls yourself. It doesn't state an explicit when-not or name the sibling to use instead, so it falls short of a 5.

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

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

Annotations already declare openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds the return shape (per-query presence, rank, citation-rate summary), which is useful, but says nothing about cost, engine selection behavior, or why readOnlyHint is false for a 'check' 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?

Three sentences with zero filler, front-loaded with the core action and return format. Efficient, though it spends a sentence on return values that the output schema already 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?

With an output schema present, return-value disclosure isn't strictly needed, and annotations carry the safety profile. The remaining gap is sibling differentiation among citations_check, domain_cited_for, and citations_* tools, which the description does not resolve.

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

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

Schema description coverage is 100%, so the schema fully documents domain, engine, and queries, including the cost tradeoff of pinning an engine. The description adds no parameter detail beyond what the schema provides, so the baseline 3 applies.

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

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

States a specific verb ('check') and resource ('whether a domain is cited by an AI engine across a cluster of queries'), which distinguishes it from audit_* tools. However, it doesn't differentiate from close siblings like citations_check or domain_cited_for, so the agent can't tell them apart from the description 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 final sentence gives a use case ('measure visibility for a brand, product, or content site'), which implies when to reach for it. But there are no explicit when-not conditions and no named alternatives despite several look-alike siblings, leaving routing 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 cover readOnly, idempotent, openWorld=false, and non-destructive. The description adds key behavioral context beyond annotations: it is served from local cache and does not spend API budget. This is valuable context not in annotations.

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

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

Two tight sentences, front-loaded with what the tool does, followed by the prerequisite and cost-saving trait. No wasted words.

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 an output schema exists and annotations cover safety, the description provides the key missing context: the tool reads from local cached corpus and is free. It doesn't mention pagination or return format, but those are covered by the output schema and parameter descriptions.

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

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

Schema description coverage is 100%, so the schema documents all four parameters (limit, since, domain, engine). The description doesn't add parameter-level semantics beyond what the schema provides, so baseline 3 is appropriate.

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

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

States a specific verb and resource ('List queries that the given domain has been cited for') with clear scope. It also names the sibling tools (check_citations, am_i_cited) that build the corpus, distinguishing it from related 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?

Explicitly says to call check_citations or am_i_cited first to build a corpus, and notes it 'queries it without spending API budget.' Clear usage context and prerequisite, though it doesn't explicitly state when not to use this tool.

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

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds genuinely useful non-annotation context: 'Cache-only, no API spend' and the precise definition of what 'gained' and 'lost' mean relative to the windows.

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 the core operation, then output semantics, then the cost/behavior note and usage hint. Every sentence earns its place with no 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?

With an output schema present, return-value detail is not needed, and the description still supplies window semantics, cost behavior, and usage context. Nothing an agent needs to call 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?

Schema coverage is 100%, so baseline is 3. The description goes beyond the schema by explaining how baseline_until and current_since define the two windows and what membership in each means, adding real semantic value over the field descriptions.

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

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

States a specific verb (diff), resource (cited_for citations), and scope (two time windows for a domain), and names the gained/lost output semantics. An agent can distinguish it from the sibling domain_cited_for point-in-time lookup 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?

Gives an explicit context for use ('track citation drift over time after publishing or migrating content'), which implicitly routes the agent away from domain_cited_for. No explicit when-not condition or named alternative, so it falls short of a 5.

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

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

Well beyond the annotations, it discloses external API calls and quota cost, a concrete disk write path (<config>/snapshots/<panel>/<iso>.json), and the credential prerequisite (at least one engine API key, same as am_i_cited). The annotations already establish the write/openWorld/non-destructive profile, so this is additive rather than redundant. Minor nuance: appending a fresh timestamped file each run sits in mild tension with idempotentHint=true, but the tool's panel state is unchanged, so it is not a true 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?

Four sentences, each carrying distinct payload (action, side effects, credentials, return shape, routing), with the core purpose front-loaded before the caveats. No filler or restated title text.

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

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

With an output schema present, the description needn't detail return structure, yet it still summarizes 'per-query citation presence and a citation_rate summary.' Combined with annotations and a fully documented 3-param schema, an agent has everything needed to call this correctly and cheaply.

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 name, domain override, and the engine enum (including the serp restriction) are already documented in the schema; the description adds nothing parameter-specific. This is the expected baseline when the schema does the heavy lifting.

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

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

The opening sentence gives a specific verb and resource ('Run a saved panel through am_i_cited and append a timestamped snapshot'), immediately separating it from audit_* tools and from panel_track, which only creates panels. An agent can identify the operation and its scope without opening the 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 explicitly routes the agent: 'Use panel_track to create a panel first; use citations_trend to read the accumulated trend after multiple runs,' naming both prerequisites and the downstream alternative. The engine note also states when serp engines are appropriate and that am_i_cited will refuse 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 carry a rich profile (readOnlyHint=false, idempotentHint=true, destructiveHint=false, openWorldHint=false), lowering the burden. The description adds the persistence path (<config>/panels/<name>.json), but never states whether action=save overwrites an existing panel — a meaningful omission for a write operation despite idempotentHint=true.

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?

Four tightly packed sentences: purpose, concept definition, per-action usage, and storage location. The scope is front-loaded and nothing is 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 detail is unnecessary, and the description covers purpose, actions, and storage. The only real gap is the relationship to panel_run, which is absent but not strictly required to call the tool correctly.

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

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

Schema description coverage is 100%, so the schema already documents name, action, domain, and queries, including the enum values. The description's action guidance is reinforcing rather than additive, 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?

The description gives three specific verbs (save, load, list) applied to a clearly named resource (named query panels) and even defines what a panel is with a concrete example. It does not, however, distinguish itself from the sibling panel_run, which an agent could easily confuse for the execute-side of this feature.

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 maps each action value to its purpose ('action=save with queries[] to create, action=load to read, action=list to enumerate'), which is genuine when-to-use guidance. It stops short of naming exclusions or pointing to panel_run as the alternative for running a panel.

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?

Discloses concrete side effects beyond the annotations: one check_citations call per query, API quota cost for uncached queries with cached queries free. It also states the return shape (summary + top_domains + per_query + optional markdown), which the annotations alone don't 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?

Front-loaded with the purpose before the mechanics, and every sentence carries information (metrics list, side effects, inputs, return shape). It is dense and somewhat long but with negligible filler.

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

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

For a high-complexity aggregation tool with an output schema already present, the description covers inputs, cost semantics, and the composed-call behavior needed to invoke it correctly. Nothing essential 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 coverage is 100%, so the baseline is 3, but the description adds real meaning: queries[] and panel are mutually exclusive alternatives, brand_terms defaults to the domain's second-level label, and engine selection mirrors check_citations. This goes beyond restating 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 ('Turnkey AI visibility report for a domain across a query set') and enumerates exactly what it returns (mention frequency, share_of_voice, average rank, sentiment). It also clarifies its relationship to check_citations by describing itself as composing that tool over each query, which distinguishes it from the raw citation siblings.

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

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

The 'one call' / 'dashboard' framing implies when to reach for it over individual checks, and it specifies the panel-vs-queries requirement. However, it never explicitly states when NOT to use it or names a sibling alternative (e.g. citations_check, competitors_compare) as the correct choice in a given scenario.

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 beyond annotations by disclosing the external dependency (SerpAPI), the required credential (SERPAPI_KEY), and a quota limit (free tier 100/month) that materially affects call frequency. Annotations already cover idempotency and openness; the quota and auth disclosures are exactly the added value the description should provide.

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

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

Two sentences, front-loaded with the core purpose, followed immediately by the dependency and setup requirement. Zero 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 need not be described, and the auth/quota prerequisites are stated. The only minor gap is that location's effect on AI Overview eligibility is left to the schema rather than reinforced in the description.

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

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

Schema description coverage is 100%, so all three parameters (query, hl, location) are already documented with defaults and semantics. The description adds no parameter detail beyond the schema, so the baseline 3 applies.

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

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

States a specific verb and resource: check AI Overview presence and cited URLs for a query. This is clearly distinct from siblings like signals_answer_box or audit_schema, though it does not explicitly name which sibling to prefer. A near-complete purpose statement.

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 and no alternative named. The only operational note is the SerpAPI dependency and free-tier quota, which is useful context but not usage routing. An agent must infer when this beats signals_answer_box or citations_check.

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

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

Annotations already cover readOnly/idempotent/non-destructive, and the description adds real behavioral context: the early/middle/late binning and the fact that engines lacking raw_answer (Bing, Brave) return 'unknown'. It stops short of describing the output shape or edge cases like multiple matches, but adds meaningful value beyond 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?

Four tight sentences, front-loaded with the core purpose and followed by mechanism, value framing, and the key limitation. Every sentence earns its place with no 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 need no explanation, and the description covers purpose, mechanism, and the engine limitation thoroughly. Slightly short on routing guidance versus sibling citation tools, but otherwise complete.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds engine-specific semantics ('engines without raw_answer... return position unknown') that reinforce the engine param's enum behavior in a way the schema only partially conveys.

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 ('Locate where each cited URL appears in the AI's raw answer text') and describes the mechanism (bins char position into early/middle/late thirds). It is clearly distinguishable from siblings like citations_check and signals_ai_overview.

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

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

Provides implied context (measuring citation position) and notes which engines yield 'unknown', but never states when to choose this over citations_check or signals_ai_overview, nor any exclusions beyond the engine caveat.

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/openWorld/idempotent/non-destructive, and the description adds meaningful context beyond that: the required BING_WEBMASTER_API_KEY, where to obtain it, and a rationale linking Bing's index to Copilot/ChatGPT/Perplexity grounding. It does not discuss rate limits or result volume, and the output schema covers 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?

Three front-loaded sentences with zero filler: purpose, the insight it yields, and the prerequisite. Every sentence carries information an agent needs to select and invoke the tool.

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

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

An output schema exists so return values need not be explained, annotations cover the safety profile, and the schema fully documents all four parameters. The description supplies the remaining piece — the API-key prerequisite and its sourcing — making the definition complete for calling correctly.

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

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

Schema description coverage is 100%, so the schema already documents domain, engine, queries, and site_url (including the trailing-slash/not-sc-domain subtlety). The description adds no parameter-level syntax or constraints beyond what the schema provides, so the baseline 3 applies.

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

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

States a concrete operation (joining Bing Webmaster query stats with am_i_cited per query) and the output it surfaces (queries ranking in Bing but not cited by AI). The 'Bing rank gap is an LLM-citation gap' framing makes it differentiable from the adjacent signals_gsc_gap sibling.

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?

Gives clear context for use — finding editorial wins where Bing rank is strong but AI citation is absent — and implies this is a diagnostic gap-finding tool. It does not explicitly name alternatives like signals_gsc_gap or citations_check or state when not to use it, so it stops short of full when/when-not guidance.

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

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds a genuinely useful behavioral fact beyond that: it requires GCP service account credentials supplied via credentials_path or the GOOGLE_APPLICATION_CREDENTIALS env var, which affects whether the call can succeed.

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 tight sentences with zero padding: purpose first, then the value proposition, then the credential prerequisite. Front-loaded and each sentence earns its place.

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

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

An output schema exists so return values need not be described, and all seven parameters are covered by the schema. The description supplies purpose, value and auth prerequisite, leaving only sibling routing (vs signals_bing_gap) unaddressed.

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 every parameter including the engine enum and date formats is already documented in the schema. The description only restates the credential source and the GSC/citation join, adding little semantics beyond what the schema provides. 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 specific verb and resource: it joins GSC performance with citation data and surfaces queries where the domain ranks in Google but is not cited in AI. The phrase 'the closest editorial wins' adds a concrete framing of the output. It does not name the obvious sibling signals_bing_gap, so differentiation is left to inference.

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 value framing ('closest editorial wins') implies the scenario where this is useful, but there is no explicit when-to-use or when-not, and no reference to the near-twin signals_bing_gap. The usage context is implied rather than spelled out.

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 beyond the annotations by disclosing the exact endpoint (en.wikipedia.org/w/api.php?action=query&list=exturlusage), the absence of auth/API keys, the external rate limit (~1 request/second fair-use), and the return shape (article titles and URLs). With annotations already covering the safety profile, this adds genuinely useful operational context.

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

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

Tight and front-loaded: the action comes first, then the implementation detail, then the rationale. The LLM-training-corpus sentence is longer than strictly necessary but carries decision-relevant motivation, so it largely earns its place.

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

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

An output schema exists, so return-value explanation is not required, yet the description still summarizes it. Combined with the endpoint, rate limit, auth status, and lang guidance, nothing an agent needs to invoke this 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?

Schema coverage is 100%, so baseline is 3, but the description adds a usage hint for lang ('Use lang to query non-English Wikipedias') that is not present in the schema's own field description. It does not characterize limit, leaving that to 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 precise verb and resource ('List Wikipedia articles that reference the given domain'), which cleanly separates it from the broader signals_* and citations_* siblings. An agent can identify the tool's job without reading the 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?

Provides clear context for why and when to reach for it ('highest-lift signal for LLM training corpora') and how to extend it with lang. However, it never names a sibling it competes with or states an exclusion, so it stops short of explicit alternative routing.

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