Apify 上の Mamba Labs の Agent Accessibility Auditor アクター向けの MCP サーバーです。
AI エージェントはこのサイトを読めるでしょうか。ドメインを渡すと、llms.txt ファミリー、新しい Content Signal ディレクティブを含む robots の AI クローラーポリシー、構造化データの存在と健全性、レンダーモード、機械可読なエンドポイント検出という5つの事実カテゴリを網羅する、42フィールドからなるフラットな1行を返します。
npx -y @mambalabsdev/mcp-agent-accessibility-auditor{
"mcpServers": {
"mamba-agent-accessibility-auditor": {
"command": "npx",
"args": ["-y", "@mambalabsdev/mcp-agent-accessibility-auditor"],
"env": { "APIFY_TOKEN": "your-apify-token" }
}
}
}Apify トークンは console.apify.com/account/integrations で取得できます。
Related MCP server: maxaeo-ai-visibility-mcp
audit_agent_accessibilityドメインを入力すると、AI エージェントがそのサイトを読み取れるかどうかを出力します。
入力 | 型 | 必須 | 備考 |
| string | はい | 企業ドメインを1つ指定します。例: vercel.com。プロトコルとパスは取り除かれます。 |
| array | いいえ | これらのチェックのみを実行します: |
| boolean | いいえ | checks 配列のエイリアスです。false にすると |
| boolean | いいえ | checks 配列のエイリアスです。false にすると |
| enum | いいえ |
|
すべてのフィールドは、フェッチから読み取られた事実です。どの時点でもモデルは呼び出されないため、サイトが実際に変更されない限り、同じドメインは今日も来月も同じ行を返します。
has_llms_txt は、/llms.txt が200を返し、本文が実際のマークダウンである場合にのみ true になります。llms_txt_reject_reason は、200がカウントされなかった理由を示します。
ドメインごとに12件のリクエストを送信します。最初に robots.txt、次にホームページと10件のプローブを同時に実行します。一般的な所要時間は2〜4秒です。
AI クローラーとエージェントトラフィックに備えてサイトを準備するテクニカルSEO担当者やグロースエンジニア、あるいはその作業を販売し、クライアントリスト全体で監査の前後比較を必要とするエージェンシーのために作られています。
分析したドメインごとに課金され、さらに少量のアクター起動料金がかかります。7日間のキャッシュ期間内の再実行には追加料金はかかりません。
料金はアクターの Apify ページに記載されています。このサーバーを実行すると Apify クレジットを消費します。
これは Apify アクターのシンクライアントです。入力をそのまま渡し、アクターの出力を変更せずに返します。上記で説明したすべての動作は、このサーバーではなくアクターに実装されています。
エラーは隠蔽されず、必ず表面化されます。無効な入力、無効なトークン、残高不足、タイムアウト、またはデータセット以外を返す実行はすべて、空の結果ではなく明示的なツールエラーとして返されます。
アクターはApify Storeにあります。このラッパーはMIT ライセンスです。
Mamba Labsによって構築されました。
Give it a domain and it returns whether an AI agent can read that site, and what the site's policy says, as one flat row of 42 fields across five families: the llms.txt family including llms-full.txt and ai.txt, robots.txt AI crawler policy including the newer Content Signal directives, structured data presence and health across JSON-LD, microdata, Open Graph and canonical, render mode, and machine readable endpoint discovery covering sitemap, OpenAPI, well known files and feeds. Every field is a fact read off a fetch. No model is called at any point, so the same domain returns the same row today and next month unless the site actually changed. Twelve requests per domain, typically 2 to 4 seconds. Built for a technical SEO or growth engineer preparing a site for AI crawlers, or an agency selling that work and needing a before and after audit across a client list. Requires an APIFY_TOKEN and consumes Apify credits. Read only.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | One company domain, for example vercel.com. Protocol and path are stripped. | |
| skipCache | No | Leave as false to use the 7 day cache. Set to true to re-audit the domain from scratch. Default: "false". | |
| check_endpoints | No | Probes sitemap, OpenAPI, well known files and feeds. Adds 7 concurrent requests. Default: true. | |
| check_structured_data | No | Parses JSON-LD, microdata, Open Graph and canonical off the homepage. Costs no extra requests. Default: true. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds substantial behavioral context beyond the annotations: 'No model is called at any point', 'same domain returns the same row today and next month unless the site actually changed', 'Twelve requests per domain, typically 2 to 4 seconds', and 'Read only'. It also discloses resource consumption and auth needs, aligning with the annotations without 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 relatively long but every sentence adds value: it covers output structure, behavior, performance, use case, and requirements. It is front-loaded with the core purpose and then expands into detail. Minor verbosity exists, but it is well-organized and not redundant.
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 having no output schema, the description thoroughly describes the return value as a flat row of 42 fields across five named families. It also covers deterministic behavior, request count, latency, auth, and intended audience. For a complex tool with 4 parameters and detailed output, this is highly complete.
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 100% description coverage, so the baseline is 3. The description does not add parameter-specific details beyond what the schema already provides, but it does reference the overall request count and endpoint checks, slightly reinforcing the check_endpoints/check_structured_data semantics. This is sufficient given the schema's thoroughness.
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 explicitly states the tool's function: 'Give it a domain and it returns whether an AI agent can read that site, and what the site's policy says'. It also enumerates the output families, providing a specific verb+resource+scope. Even without siblings, it is clearly differentiated from generic audit tools.
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 names the target user ('technical SEO or growth engineer', 'agency') and use case ('preparing a site for AI crawlers', 'before and after audit'). It also mentions prerequisites (APIFY_TOKEN, credits). However, it does not explicitly state when not to use the tool or mention alternatives, which is acceptable given there are no siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
With only one tool, there is no possibility of confusion or overlap. The tool's purpose is clearly defined and distinct by virtue of being the sole member of the set.
The single tool name follows a clear verb_noun pattern (audit_agent_accessibility), consistent with common MCP naming conventions. There are no other tools to conflict with this pattern.
A single tool feels minimal for a server, but the tool itself is highly specialized and performs a comprehensive audit in one action. The count is borderline, as it could benefit from additional tools like listing domains or comparing audits, but the narrow scope partially justifies the thin surface.
For the stated purpose of auditing agent accessibility, the tool covers a wide range of signals (llms.txt, robots.txt, structured data, render mode, endpoint discovery) in a single output. The only gap is the lack of supporting operations, but as a read-only audit tool, the core domain is well covered.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Checks llms.txt, AI crawler access in robots.txt, and sitemap - with a 0-100 AI readiness score.
Scan any public site for AI-agent visibility; get scored findings, a machine-readable fix pack, and
Generate 18 AI readiness files (llms.txt, ai.txt, RAG indexes, schema) for any website.
AEO audit: score any website 0-100 for AI visibility. Checks schema, meta, content, AI crawlers.
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