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AI Visibility Index

by io.github.pulse-digital-dev

check_ai_visibility

Read-onlyIdempotent

Check the AI visibility (LLMO/GEO) score for a specific domain. Returns the overall score (0-100), scores from 4 AI engines (ChatGPT, Claude, Gemini, Perplexity), citation rate, and industry ranking. Data is based on the AI Visibility Index monthly scan of 104 Japanese EC companies. Useful for LLMO (Large Language Model Optimization) and GEO (Generative Engine Optimization) analysis. | 日本EC企業104社のAI検索可視性スコアをドメイン指定で照会。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesDomain to check, e.g. "amazon.co.jp", "zozo.jp", "uniqlo.com/jp". Partial matches are supported.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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.

Conciseness4/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines3/5

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.

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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a distinct purpose: checking a domain's score, retrieving methodology, and listing industries. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tools use the consistent base 'ai_visibility' with clear verb prefixes ('check_', 'get_', 'list_'), forming a predictable and uniform pattern.

Tool Count5/5

With only 3 tools, the server is tightly scoped to its niche purpose—querying AI visibility data, methodology, and industry overviews—without unnecessary bloat.

Completeness4/5

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.

Resources