Get the AI Visibility Index scoring methodology: how LLMO/GEO scores are calculated, which AI engines are tested (ChatGPT, Claude, Gemini, Perplexity), query types, scoring formula, and data freshness. | スコアリング方法論（計算式・対象エンジン・クエリ種別・データ更新頻度）。
| 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 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.
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.
