AEO Visibility

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

Annotations already declare read-only, idempotent, open-world, and non-destructive behavior. The description adds substantive context beyond the annotations: it explains the processing flow (crawl, parse HTML/structured data) and enumerates what the result covers (AEO summary, AI-bot access, crawl-hint files).

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 tightly written sentence with the processing steps and return contents front-loaded, and no wasted wording.

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 no output schema, the description appropriately summarizes the return surface (AEO summary, AI-bot access, crawl-hint files). For a one-parameter, read-only analysis tool this is nearly complete, though it could mention depth or limits.

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

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

Schema description coverage is 0%, so the description must carry parameter meaning. It indicates the single input is a URL to crawl, which is useful, but adds no format, validation, or scope constraints beyond what the parameter name already implies.

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 specific verbs (crawl, parse, score) and the resource (a URL's HTML and structured data), and names the domain concept (answer-engine optimization). This clearly distinguishes it from the sibling visibility/reporting tools.

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

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

The purpose implies when to use it (single-site AEO analysis), but there is no explicit guidance about when to prefer it over check_ai_visibility or get_visibility_report, and no stated exclusions or prerequisites.

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 and open-world, so the safety profile is covered. The description adds that results come from an external LLM provider (Perplexity Sonar) via citation inspection, but omits cost, latency, non-determinism of model output, and API-key failure 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?

A single sentence that front-loads the mechanism and ends with the outcome. No filler, nothing redundant.

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 no output schema, the description does the necessary work by stating the return value is citation presence plus rank. Minor gaps remain around error cases and whether rank is across all citations or only the target URL's position.

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 all three parameters (url, prompt, perplexityApiKey) are already documented, including the BYOK note. The description restates prompt and URL without adding format, constraints, or matching semantics (e.g., exact vs. subdomain URL matching).

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 verb chain (query Sonar, inspect citations) and a concrete outcome (whether the URL is cited, and at what rank). An agent can tell this is a single-prompt citation check rather than a site-wide audit, though it never names the sibling tools it differs from.

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 only implied: the singular 'a prompt' and 'the given URL' suggest one-off testing versus the aggregate siblings (get_visibility_report, compare_competitor_visibility). There is no explicit when-to-use, when-not-to-use, or named alternative.

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

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so safety is covered. The description usefully discloses that exactly one external Sonar query is issued (a cost/latency-relevant trait) and that results are ranked by citation presence and position, but it says nothing about failure behavior or partial coverage.

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

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

A single dense sentence with no filler; the action, the input shape, and the ranking criterion all land up front.

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 no output schema, the description partially carries the burden by sketching the return ('rank ... by whether and where each is cited'), which is enough for an agent to interpret the result. It does not explain the result shape (e.g., per-URL position/absence) or what happens when zero URLs are cited.

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 only loosely echoes what the schema says ('competitor URLs', 'for the prompt'), adding no format or syntax detail. The BYOK key parameter is documented only in the schema, which is acceptable given moderate coverage.

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

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

States a specific verb ('rank'), a specific resource ('several competitor URLs'), and the mechanism ('one Perplexity Sonar query') plus the criterion ('whether and where each is cited'). This is clearly distinct from analyze_website_aeo or get_visibility_report, though it never names those siblings 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?

Usage is implied: you call this when you want a head-to-head citation ranking for a prompt across two or more URLs. There is no explicit 'use this instead of X when...' guidance and no mention of prerequisites, so the agent must infer the boundary from sibling names.

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), so the description's real contribution is disclosing the external dependency on Perplexity Sonar and the optional live-test phase that reaches out to a third-party model. That is meaningful context an agent would not infer from annotations, though it omits cost/latency implications of BYOK calls and does not flag that live-test results vary (mild tension with 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?

Two sentences, front-loaded with the core generation action, and the optional test capability is scoped to a trailing clause. No filler; the only minor weakness is that the trailing clause is vague about what 'a few' means relative to testCount.

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

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

There is no output schema, so the description carries the burden of explaining what comes back — presumably a list of prompts plus some test results — and it never does, leaving the shape of the return value and the number of prompts generated unspecified. Four parameters, three required, with an external API call; that is enough complexity that the omission is a real gap, even with annotations covering safety.

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 75%, and the schema already documents topic, testCount (default 3, max 10), and perplexityApiKey. The description adds only indirect meaning for `url` by framing generation around 'a site' — enough to infer it is the site whose prompts are being generated, but not enough to lift this above the baseline.

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 artifact: generate realistic user prompts a site should be cited for, plus an optional live-test step. An agent can tell this is prompt discovery rather than a visibility report, but the description never names the siblings (check_ai_visibility, analyze_website_aeo) it overlaps with on the 'live test' clause.

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 only implied: the phrase 'Optionally tests a few of them for live visibility' suggests when to raise testCount, and the framing (a site wanting to be cited) hints at an early-stage discovery use case. There is no explicit when-to-use/when-not, no routing to check_ai_visibility for pure visibility measurement, and no mention of setup prerequisites beyond the API key.

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=true, so the safety profile is covered structurally. The description adds genuine context by disclosing the internal scope (one audit plus N prompt checks against a live external service) and that the result includes an aggregate citation rate. It omits operational traits that matter for an open-world, per-prompt fan-out call: latency, cost, and failure behavior for an invalid API key.

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

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

A single sentence with no filler, front-loading the composite behavior and the headline output metric. Nothing is repeated from the title or schema, and nothing could be removed 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 three-required-parameter read-only tool with no output schema, the description supplies the essential shape of the result (a combined report with an overall citation rate) and annotations cover safety and idempotency. The remaining gap is behavior under partial failure or invalid credentials when running many per-prompt checks, which is a modest omission rather than a blocker.

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%: prompts and perplexityApiKey are documented in the schema (BYOK, never stored), while url has only a format constraint and no prose. The description adds the notion that url is the audit target and prompts are tested per-item, but contributes no syntax or format detail beyond the schema. Baseline 3 is appropriate given partial schema 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 names a specific action and output: composing an AEO audit plus per-prompt visibility checks into one report with a citation rate. This implicitly reveals that the tool is a composite of the sibling tools analyze_website_aeo and check_ai_visibility, which helps an agent place it. It stops short of explicitly naming those siblings, so the differentiation is inferred rather than stated.

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 only implied: an agent can deduce that this is the tool to call when it wants both an audit and prompt-level visibility in one shot. There is no explicit guidance such as 'use this instead of calling analyze_website_aeo and check_ai_visibility separately,' no exclusion criteria, and no note about when the single-purpose siblings would be preferable.

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