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ai-visibility-mcp

ai-visibility-mcp

by draltaway

ai-visibility-mcp

AIアシスタントに、あるウェブサイトがAI検索エンジンやAIアシスタント(ChatGPT、Claude、Perplexity、Google AI Overviews)にどれだけ発見されやすいかを伝えるMCPサーバーです。

URLを渡すと、それらのシステムがサイトを発見し、読み、引用できるかを左右するシグナルを報告し、ひと目でわかる0〜100のスコアにまとめます。

チェックする内容

シグナル

重要な理由

robots.txt 内でのAIクローラーのアクセス設定

GPTBot、ClaudeBot、PerplexityBot などがブロックされている場合、そのサイトはそれらのエンジンから見えなくなります

/llms.txt の有無

サイトの重要な事実をLLM向けに平文でまとめたマップ

構造化データ(JSON-LD)

回答エンジンが読み取って引用できる機械可読な意味情報

sitemap.xml の有無

クローラーがすべてのページを見つけるのに役立ちます

タイトル、メタディスクリプション、H1

エンジンが最初に読む、ページ内の基本的なシグナル

Related MCP server: maxaeo-ai-visibility-mcp

インストール

git clone https://github.com/draltaway/ai-visibility-mcp
cd ai-visibility-mcp
python -m pip install -e .

HTTP関連は標準ライブラリのみ。唯一の依存関係は FastMCP です。

Claude Code から使う

claude mcp add ai-visibility -- ai-visibility-mcp

Claude Desktop から使う

claude_desktop_config.json に追加:

{
  "mcpServers": {
    "ai-visibility": {
      "command": "ai-visibility-mcp"
    }
  }
}

次に、"stripe.comのAI可視性を確認してください。" と尋ねます。

出力例

{
  "url": "https://stripe.com",
  "score": 90,
  "summary": "Strong: AI engines can find, read, and cite this site.",
  "checks": {
    "ai_crawlers_allowed": true,
    "ai_crawlers_blocked": [],
    "llms_txt": true,
    "sitemap": false,
    "homepage_reachable": true,
    "structured_data": true,
    "title": true,
    "meta_description": true,
    "h1": true
  }
}

ツール

check_ai_visibility(url: str) -> dict は { url, score, summary, checks } を返します。ホスト名のみ(example.com)または完全なURLを受け付けし、デフォルトでは https を使います。

ライセンス

MIT。作者は Dallin Rowley です。

Available Tools

1 tool
check_ai_visibilityA

Check how discoverable a website is to AI assistants and AI search engines.

NameRequiredDescriptionDefault
urlYesThe site to check, e.g. "example.com" or "https://example.com".

Output Schema

NameRequiredDescription

No output parameters

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure, and it only states the tool's purpose. It does not reveal whether the check involves a live network fetch, what signals are analyzed (e.g., robots.txt, llms.txt, meta tags), or whether results are instantaneous or sampled. Nothing is disclosed that contradicts annotations because no annotations exist.

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 a single, front-loaded sentence with zero wasted words, stating the action before the object. It is efficient, though slightly terse — a phrase clarifying what 'discoverable' means could be added without hurting conciseness.

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?

This is a low-complexity tool with one required parameter and an output schema present, so the description does not need to describe return values and the input is fully documented in the schema. The main context gap is the absence of any explanation of what 'AI discoverability' is based on, but for such a simple tool the definition is nearly complete.

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 description coverage is 100% — the url parameter already documents its type and accepted formats ('example.com' or 'https://example.com'). The tool description adds no additional meaning about the parameter, so the 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 states a clear action ('Check') and a specific resource/scope ('how discoverable a website is to AI assistants and AI search engines'), so an agent can tell what the tool does at a glance. There are no sibling tools to distinguish from, and the term 'discoverability' is left slightly undefined, so it falls just short of a 5.

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?

Because there are no sibling tools, there are no alternatives to exclude, but the description still provides only implied use context: if an agent wants to know a site's AI visibility, this is the tool. There is no explicit when-to-use guidance, exclusions, or prerequisites, making the usage guidance minimally adequate.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.1.0
    • First observedcheck_ai_visibility

TDQS

A3.7/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap. The tool's purpose is clearly unique and distinct.

Naming Consistency5/5

A single tool with a descriptive snake_case verb_noun name. There is no inconsistency to assess, but the naming is clear and follows conventional MCP patterns.

Tool Count3/5

A single tool feels thin for a dedicated MCP server, even for a focused purpose. The borderline count leaves little room for related operations but is not excessively sparse.

Completeness4/5

The tool directly covers the core stated purpose of checking AI visibility. Minor gaps exist, such as batch checks or historical analysis, but they do not block the primary use case.

Related MCP Connectors

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