SeekinWeb — AI visibility

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 genuinely non-obvious behavior beyond them: first-call latency (~1 minute background run), the need to re-poll when the test is still running, and the no-account/no-cost condition. It does not discuss rate limits or failure modes, so it falls short of a 5.

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

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

The purpose is front-loaded in the first clause and every subsequent sentence carries distinct information (return contents, first-use latency, retry, cost). It is slightly dense but not padded; no sentence is 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 carries the return-value burden and does so: it enumerates how many answers named the business, which brands were named instead, which sources the assistant relied on, and what the assistant says about the business. Combined with the polling and cost notes, an agent has everything needed to invoke and interpret the call.

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 params (url, lang) are already fully documented in the schema, including the authorization caveat and the enum default. The description adds no parameter-level meaning beyond that, so the baseline 3 is appropriate.

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

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

States a precise verb and resource: it checks whether a live-web-search AI assistant recommends a business when prompted without naming it. The scope (unprompted brand mentions, competitor brands, cited sources) is specific enough that an agent can distinguish it from generate_llms_txt and scan_website 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 operational guidance: it 'runs in the background on first use (about a minute)' and instructs to call again shortly if it reports the test is running, plus notes it is 'free, no account'. It does not name alternative tools or exclusion conditions, but the siblings are not substitutes for this test, so the missing routing is minor.

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 genuinely useful context beyond that: it states the output is the file text itself, that content is derived from the page's real content rather than invented, and that the convention's impact is unproven — an honest caveat that helps the agent frame the result.

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 action and artifact, followed by the output and the caveat. No filler; every clause carries 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 two-parameter read-only generator with no output schema, the description covers the action, the artifact, the return value and the expectation-setting caveat. The authorization constraint lives in the schema, so nothing essential is missing, though a word on whether this scans the whole site or a single page would round it out.

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 url and lang are already documented, and the baseline is 3. The description contributes only the implicit scoping "for the root of a domain," which mildly clarifies the url parameter but adds no format or language detail beyond the schema.

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

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

The description gives a specific verb and artifact ("Drafts an llms.txt file") and explains what that artifact is — a structured summary of a site for language models, derived from real page content. The purpose is unambiguous and distinct from scan_website or check_ai_recommendations, though it never names those siblings to draw the contrast 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?

There is no statement of when to choose this tool over its siblings, nor any precondition beyond the schema's authorization note. The only adjacent guidance is the caveat that llms.txt is an emerging convention with unproven impact, which sets expectations but does not route the agent between 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 safety (readOnly, idempotent, non-destructive, openWorld), so the description earns credit for going further: it enumerates exactly what the eight weighted signals evaluate and promises a concrete fix per failure. It omits operational behavior such as latency, rate limits, or failure handling for unreachable domains.

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 the verb and the payoff score, then parenthetically enumerates the signals; the 'Free, no account' fragment is a deliberate call-to-action. The signal list is long but each item is a distinct evaluation dimension rather than 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 correctly compensates by describing the return (0-100 score, eight signals, per-failure fixes). Parameters and safety are covered elsewhere, leaving only error-path and timing behavior unspecified, which is a minor gap for a simple read-only scanner.

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 schema itself documents both params including accepted URL forms and the en/es enum, so the description adds nothing beyond it. Baseline 3 is appropriate when the schema carries the full parameter burden.

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 ('Scans a website') plus the concrete artifact produced ('AI Visibility Score (0-100)'), which no sibling produces. An agent can distinguish this scan-and-score tool from check_ai_recommendations and generate_llms_txt 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?

Usage is implied by the purpose (scan a URL to get its AI visibility score) and the description usefully discloses a prerequisite condition ('Free, no account'), but it never states when to prefer this over the sibling tools or what to do with the result. No exclusions or routing guidance are given.

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