AI Visibility Index

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

Annotations declare readOnly=true, idempotent=true, destructive=false. The description adds data source (monthly scan of 104 Japanese EC companies), partial domain matching support, and return details (4 engines, citation rate, ranking). No contradictions.

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 efficient sentences plus a Japanese translation. Front-loaded with key information. Slight redundancy (bilingual) but overall concise and well-structured.

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 enumerates return fields (overall score, 4 engine scores, citation rate, industry ranking). This is sufficient for a simple query tool with one parameter and good annotations.

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% with examples and partial match note in the parameter description. The tool description does not add additional semantic meaning beyond what schema provides. 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?

The description clearly states the tool checks AI visibility score for a specific domain and lists return fields (overall score, engine scores, citation rate, industry ranking). It distinguishes from siblings (methodology, industry list) by being the core query tool, though not 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?

The description implies usage for LLMO/GEO analysis with specific domain, but does not mention when not to use or compare to alternative tools (e.g., methodology). Context signals indicate only one parameter, so usage is straightforward.

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 the tool as read-only, idempotent, and non-destructive. The description adds context about what the methodology includes but does not disclose additional behavioral traits beyond what annotations provide. No contradiction.

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 front-loaded with the core purpose and details, and includes a concise Japanese translation. It is efficient but slightly repetitive due to bilingualism; could be more concise without the translation.

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 and good annotation coverage, the description adequately conveys the tool's purpose and content. However, it does not specify the output format (e.g., JSON, text), which would help an agent fully understand what to expect.

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 input schema has zero parameters, so the description carries the full burden of explaining what the tool provides. It adds meaning by listing the content of the methodology (scoring formula, engines, query types, data freshness), which is valuable beyond the empty 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 clearly specifies the verb 'Get' and the resource 'AI Visibility Index scoring methodology', and details what is covered (scoring formula, AI engines, query types, data freshness). It distinguishes itself from sibling tools like check_ai_visibility and list_ai_visibility_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 implies usage context by stating it returns methodology details, but does not explicitly clarify when to use this tool over its siblings or provide exclusion criteria. The purpose is clear enough that an agent can infer, but explicit guidance is missing.

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 read-only, idempotent, non-destructive behavior. Description adds only current coverage scope (9 Japanese EC industries, 104 companies) which provides context but not significant behavioral insight 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?

Two sentences (one English, one Japanese) are concise and front-loaded. However, the Japanese repetition adds minimal value for non-Japanese users. Still, no wasted content.

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?

Despite no output schema, description gives sufficient detail: industries, scores, counts, ranges. Combined with annotations, the tool's function is fully understandable. Simple list tool requires little additional context.

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?

No parameters exist, so schema coverage is trivially 100%. Description does not need to explain parameters. Baseline score of 4 is appropriate given zero parameters.

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?

Description clearly states it lists industries with specific metrics (average scores, company counts, ranges). The verb 'list' and resource 'industries' are unambiguous, and it distinguishes from sibling tools like check_ai_visibility and get_ai_visibility_methodology.

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 guidance on when to use versus siblings. While purpose is clear, no 'when to use/when not to use' information is provided. The implied usage is for obtaining an overview, but direct alternatives are not mentioned.

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