AIGROW

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds real behavioral context beyond that: it discloses the return payload (businesses, share of answers, edition, Index link) and the domain boundaries (44 categories, 88 cities, 20 countries, eleven languages). It does not say what happens on an unknown category or place, which keeps it from 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 and return contents are front-loaded in the first sentence, followed by coverage scope and language behavior. Three dense sentences with no filler. Slightly long, but every clause carries information an agent would otherwise have to infer.

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, and the description compensates by enumerating the returned fields and the link to the Index page. Input constraints (language handling, coverage breadth) are also given. It is nearly complete for a two-required-parameter read tool; only edge-case behavior for unmatched categories or places 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 coverage is 100%, so baseline is 3. The description goes beyond the schema by stating that category and place may be written in any of the eleven supported languages, which explains the cross-language matching behavior that the schema examples only hint at. It does not add syntax guidance for the language parameter itself, but that is already fully documented in the schema.

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

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

The description states a specific verb (reads) and a named resource (the AI Visibility Index by AIGROW) and spells out what the answer contains: which businesses ChatGPT, Claude, Gemini and Grok name, their share of answers, edition, and Index link. It is clear without opening the schema. It does not, however, differentiate itself from the sibling check_ai_visibility, so an agent must guess which of the two 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?

Usage is only implied: the tool takes a category and a place, so an agent can infer it is the lookup-by-category-and-place route. There is no explicit when-to-use statement, no exclusion, and no mention of the sibling check_ai_visibility as an alternative. Adequate but with a clear routing gap.

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 mark it read-only and open-world, and the description goes well beyond them: ~3 minute runtime, progress reported as answers stream in, free-tier rate limits (two new websites per hour per network address, shared by hosted clients, plus a daily cap), and the caching nuance that a city or category makes an already-scanned website a new scan. These are exactly the operational facts an agent needs before committing to a three-minute 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?

Front-loaded with the core verb and scope in the first clause, then layered with output, duration, and limits in a logical order. It is a single dense run-on paragraph, though, so the rate-limit and re-scan details take a careful read to parse.

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

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

With five params, no output schema, and open-world behavior, the description covers everything material: what gets returned (naming frequency, score out of 100, competitors, blockers), how long it takes, how progress surfaces, and the quota constraints. Nothing an agent needs before invoking 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: it explains that website is the object being read and that city/category act as scan-identity keys ('a city or category makes it a new scan, even of a website scanned before'). Only language goes unaddressed in prose.

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 and scope: runs a free AI visibility scan of one business, detailing exactly what the scan does (reads site, writes buyer questions, queries ChatGPT/Claude/Gemini/Grok, reports naming frequency, a score out of 100, competitors and blockers). This is far more specific than 'check visibility' and lets an agent distinguish a full multi-model scan from the lighter siblings ai_index_lookup and get_score.

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 clear this is the full free scan (implying the siblings are for narrower lookups) and notes that adding a city or category forces a new scan, which is a useful selection cue. However, it never explicitly names when to use this versus get_score or ai_index_lookup, so routing is left to inference.

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

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

Annotations already declare read-only, idempotent, closed-world, and the description adds the behavior that matters beyond that: no live assistant is queried, a missing business yields 0 rather than an error, and a name matching several businesses returns them instead of a score. These are non-obvious return-shape behaviors an agent could not derive from the 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?

Three sentences, front-loaded with the core operation and then the metric breakdown, the read-only constraint, and edge cases. Dense but each clause carries information; only the parenthetical assistant enumeration is slightly heavy.

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 must describe the return values, and it does: score out of 100, 95% interval, prior edition, per-assistant score, and most-named names. Combined with the two alternative return shapes (0, or a list of matches), an agent knows what to expect from every 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 coverage is 100%, so baseline is 3, but the description adds a real matching rule: 'Category and place are matched as ai_index_lookup matches them', which constrains how loose the input strings may be. The language parameter is left to the schema, which is acceptable given full 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 and resource ('Reads the AIGROW Score of one business on an AI Visibility Index page') and immediately defines what the score measures: the share of assistant answers naming the business, out of 100. The clause 'no website is read and no assistant is asked' implicitly separates it from live-check siblings like check_ai_visibility.

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?

Edge cases are spelled out: an unlisted business reads 0 and an ambiguous name returns the candidate businesses instead of a score. It also says the tool only reads published counts, which tells an agent this is the cheap, non-live path. It never names check_ai_visibility as the alternative for live measurement, so the when-to-use routing is inferred rather than stated.

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