AI Visibility Index

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

Annotations already declare the tool as read-only and idempotent. The description adds behavioral context by stating the data source (monthly scan of 104 Japanese EC companies) and the return fields, enhancing transparency 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?

The description is concise, with two informative sentences in English followed by a Japanese translation. Every sentence adds value, though the translation repeats content. 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?

For a simple read-only tool with one parameter and no output schema, the description provides adequate context: what it does, what it returns, data source, and usage. The lack of explicit output format is mitigated by listing return fields.

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 single parameter 'domain' is fully described in the schema (100% coverage). The description adds example domains and mentions partial matches, which adds marginal value but does not significantly extend schema information.

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 clearly states the verb 'Check' and the resource 'AI visibility score for a specific domain'. It enumerates the returned data (overall score, engine scores, citation rate, industry ranking) and distinguishes from sibling tools (methodology, list industries).

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 mentions usefulness for LLMO and GEO analysis, providing a clear usage context. It does not explicitly state when not to use or alternatives, but the sibling tools imply complementary purposes.

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 mark it as read-only and idempotent. The description adds meaningful context about what information is returned (engines, formula, queries, freshness), which goes beyond 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?

The description is very concise: two clear sentences (one English, one Japanese). It is front-loaded with the main purpose, though the bilingual repetition is slight but acceptable for international tools.

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 no output schema, the description covers the key return elements (engines, query types, formula, freshness). It provides sufficient completeness for a methodology info 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 has zero parameters, so the input schema coverage is 100%. The description does not need to explain parameters; the baseline of 4 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 clearly states it retrieves the AI Visibility Index scoring methodology and lists specific content (engines, query types, formula, freshness). It distinguishes from siblings like check_ai_visibility and list_ai_visibility_industries by being a static methodology endpoint.

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 vs. alternatives, but the tool is a simple read-only info tool with no parameters, making its usage self-evident. The context is clear, though exclusions are not stated.

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 provide readOnlyHint=true and idempotentHint=true, so the description's addition of scope details (covers 9 Japanese EC industries, 104 companies) adds value beyond annotations. No contradiction or missing major behavioral traits.

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 description is two sentences, each earning its place: the first states the purpose and outputs; the second provides Japanese translation. No fluff, front-loaded with key 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?

Given no parameters, no output schema, and a simple listing task, the description fully explains what the tool returns and its scope. No missing information needed for a low-complexity 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?

There are zero parameters, so schema coverage is 100%. The description adds meaning by detailing what the output includes (average scores, company counts, score ranges), which is beyond the schema. Baseline is elevated to 4.

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 explicitly states the tool lists industries with scores, company counts, and ranges, and distinguishes itself from siblings like 'check_ai_visibility' (checks a specific company) and 'get_ai_visibility_methodology' (explains methodology). The verb 'list' and resource 'AI Visibility' industries are clear.

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 does not explicitly state when to use this tool versus alternatives, but sibling names imply usage: use this for an overview list, others for specific checks or methodology. Lack of explicit when/not or exclusions keeps score at 3.

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