visibility

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds real behavioral context: the exact fields returned, the AI-recommendation check with competing products named instead, a public page link, a graceful fallback for untracked domains, and that no token is needed.

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

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

Front-loaded with purpose and return contents in dense, waste-free prose. Slightly long, but every clause carries information an agent would otherwise have to guess.

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?

No output schema exists, so the description carries the return-value burden and does so thoroughly: named fields, score range, AI-provider coverage, link, and untracked-domain fallback. Nothing material is missing for a single-parameter read tool.

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

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

Only one parameter, and schema description coverage is 100% with the schema documenting the domain/URL format and example. The description adds no syntax or format 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 ('look up') and resource ('a product launch that FrontStat already tracks'), then enumerates the fields returned. It is clearly distinguishable from siblings like get_visibility or who_wins_instead, though it never names them 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 by the scope (checking a launch FrontStat tracks), but there is no explicit when-to-use statement or routing to an alternative tool. The only conditional guidance describes behavior for untracked domains rather than tool selection.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds genuinely useful behavioral context beyond that: a Pro-tier auth requirement with the token passed as a Bearer token, and the data-scoping rule that the caller's own domain is excluded and results are limited to the subscription's domain. It does not mention pagination or result caps.

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 what is returned before any caveats, and the auth/scope constraints are compressed into the final two sentences. Slightly promotional phrasing ('These are the pages to get mentioned on') is the only sentence that does not strictly carry invocation 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?

With no output schema, the description carries the burden of describing the return payload and does so adequately (sites plus citation counts, own domain excluded). Auth and subscription scope are also disclosed. Missing only secondary details such as how many sites are returned or ordering, which are minor for a zero-parameter read tool.

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

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

The tool takes zero parameters, so per the rubric the baseline is 4. The description correctly does not fabricate parameter semantics and instead spends its words on scope and return shape, which is the right allocation for a no-arg read.

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

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

The description names a concrete resource (sites cited by AI assistants when answering buyer questions) and specifies the payload precisely: each site plus how many answers cite it, with the caller's own domain excluded. That is enough to distinguish it from a generic visibility tool, but it never contrasts itself with siblings such as who_wins_instead or get_visibility, 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?

Usage is only implied via the framing 'These are the pages to get mentioned on', which hints at a link-building/PR use case. It states a prerequisite (needs FrontStat Pro, Bearer token) but gives no explicit when-to-use-vs-alternatives guidance and no exclusions relative to the sibling tools.

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

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's value-add is the access model: it requires a FrontStat Pro subscription, the dashboard token must be sent as a Bearer token, and the scope is limited to the domain on the subscription. That auth/prerequisite detail is genuinely beyond the structured fields; no rate limits or failure modes are mentioned.

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

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

Front-loaded with what the report contains, then two short sentences on prerequisites and scope. The first sentence is a dense enumeration but every listed field is a real return value given no output schema exists. Slightly long, but 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 no output schema, the description has to carry the return-value burden and does: it names the date, score, per-assistant recommendation, the competitor named instead, and X-of-Y coverage. Combined with the stated subscription/token prerequisite, an agent has enough to call it and interpret the result; only pagination/formatting and the trend-vs-snapshot distinction are missing.

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

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

The tool takes zero parameters, so there is no parameter semantics to document and the baseline is 4. The description's reference to the subscription domain and Bearer token correctly signals that scoping comes from credentials rather than input, avoiding any expectation that a domain argument exists.

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 the exact resource ('your latest FrontStat Pro report') and enumerates its contents concretely — date, visibility score, per-assistant recommendation and the competitor named instead, and X-of-Y coverage across 10 buyer questions. It is clearly distinguishable in substance from cited_sites, list_buyer_questions, or who_wins_instead, though it never explicitly names a sibling or contrasts 'latest' against visibility_history.

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 word 'latest' implies this is the current-snapshot tool versus visibility_history for trends, but the description never states when to prefer this over that sibling or over check_public_launch. Usage is only inferable from the words 'latest' and 'today' in the title.

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, closed-world behavior, and the description adds meaningful extras beyond them: the FrontStat Pro entitlement requirement, the Bearer token auth mechanism, and the subscription-domain scope. That is real behavioral context not available in structured fields.

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

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

Compact and front-loaded with the return semantics, though the clause chain ('...with yes or no per assistant: does the answer recommend your product...') is slightly muddy and could be tightened into a cleaner statement.

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

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

With no output schema, the description does the work of explaining the return shape (yes/no per assistant per question), plus auth and scope, for a zero-param read tool. Sufficient to call correctly; only the domain scoping mechanics are slightly implicit.

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

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

The tool takes zero parameters, so the baseline is 4; there is nothing for the description to compensate for, and it correctly avoids inventing parameter semantics.

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

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

The description states a specific resource and its return shape: each buyer question asked to AI assistants, with a yes/no per assistant on whether your product is recommended. It is distinguishable from siblings like who_wins_instead (competitor focus) and cited_sites, though 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 implied by the resource ('your buyer questions' for your subscription domain), and the Pro/Bearer-token prerequisite gives context, but there is no explicit when-to-use or when-to-prefer-a-sibling 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, non-destructive and closed-world, so the safety profile is covered. The description adds real operational context beyond that: it requires FrontStat Pro, requires a dashboard token as a Bearer token, and is scoped to the domain on the subscription.

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 ordered output-shape first, then use case, then requirements and scope. Every sentence carries distinct information with no repetition of the title or annotations.

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

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

With no output schema, the description correctly describes the returned data (daily points with score and recommendation count), plus auth, plan gating, and scope. It leaves minor gaps such as error behavior on a missing/invalid token or the exact timezone/refresh cadence of the 30-day window.

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

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

There are zero input parameters, so there is no schema meaning to add; the baseline for a parameterless tool applies. The description still clarifies that authentication (Bearer dashboard token) and scope (subscription domain) are implicit inputs, which is useful even though no schema field exists.

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 and the exact output shape: one point per day for 30 days, containing the visibility score and the count of answers recommending the product. This clearly separates it from the sibling get_visibility (snapshot) without needing 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?

"Use it to see whether your changes moved the assistants" gives a concrete when-to-use condition tied to trend analysis. It does not explicitly name or exclude the sibling get_visibility, so it falls short of full alternative routing.

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

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

Annotations already establish read-only, idempotent, non-destructive behavior, so the bar is lower. The description still adds meaningful context the annotations don't carry: the required Bearer dashboard token, the Pro subscription gate, snapshot freshness ('latest Pro report'), and the data scope ('the domain on your subscription').

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 payload (what the tool returns) before trailing the operational caveats about Pro and tokens. Efficient and waste-free, though the trailing clauses make it dense and slightly run-on.

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 right thing by describing the returned content (products, cited sites, appearance counts). Auth and plan gating are covered, so an agent has enough to attempt the call; only the distinction from cited_sites is left implicit.

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

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

There are zero parameters, so per the rubric the baseline is 4. Nothing in the schema needs compensating for, and the description correctly indicates the tool is implicitly scoped to the subscription's domain rather than requiring an argument.

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

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

The description names the specific resource it returns: the products AI assistants cite instead of yours, plus the sites they cite, each with an appearance count. This is clearer than a vague 'get competitors' label and partially differentiates it from siblings, though it never explicitly contrasts itself with cited_sites, which overlaps on the 'cited sites' half.

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

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

It gives a real precondition ('Needs FrontStat Pro', 'from your latest Pro report', Bearer token) but never says when to choose this over get_visibility or cited_sites, nor when it is not applicable. Usage is implied by the prerequisites rather than stated as a routing rule.

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