List all industries covered by the AI Visibility Index with their average LLMO/GEO scores, company counts, and score ranges. Currently covers 9 Japanese EC industries with 104 companies total. | 9業界のAI可視性スコア平均・企業数・スコア範囲を一覧表示。
| Name | Required | Description | Default |
|---|---|---|---|
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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.
Add one secure layer between your agents and this server.
Each tool has a distinct purpose: checking a domain's score, retrieving methodology, and listing industries. There is no overlap or ambiguity between them.
All tools use the consistent base 'ai_visibility' with clear verb prefixes ('check_', 'get_', 'list_'), forming a predictable and uniform pattern.
With only 3 tools, the server is tightly scoped to its niche purpose—querying AI visibility data, methodology, and industry overviews—without unnecessary bloat.
The tool set covers the core operations for the domain (query by domain, methodology, industry list), but lacks a way to list individual companies or compare multiple domains, leaving minor gaps.
