MCP-сервер для актора Mamba Labs Agent Accessibility Auditor на Apify.
Может ли ИИ-агент прочитать этот сайт? Дайте ему домен, и он вернёт одну плоскую строку из 42 полей, охватывающих пять семейств фактов: семейство llms.txt, политику ИИ-краулеров в robots, включая новые директивы Content Signal, наличие и состояние структурированных данных, режим рендеринга и обнаружение машиночитаемых конечных точек.
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" }
}
}
}Получите токен Apify на console.apify.com/account/integrations.
Related MCP server: maxaeo-ai-visibility-mcp
audit_agent_accessibilityДомен на входе, ответ — может ли ИИ-агент прочитать этот сайт.
Вход | Тип | Обязательно | Примечания |
| string | да | Один домен компании, например vercel.com. Протокол и путь отбрасываются. |
| array | нет | Запускать только эти проверки: |
| boolean | нет | Псевдоним для массива checks: false удаляет |
| boolean | нет | Псевдоним для массива checks: false удаляет |
| enum | нет | Оставьте |
Каждое поле — это факт, полученный из запроса. Никакая модель не вызывается, поэтому один и тот же домен возвращает одну и ту же строку сегодня и в следующем месяце, если только сайт действительно не изменился.
has_llms_txt истинно только тогда, когда /llms.txt возвращает 200 и тело является настоящим markdown, а llms_txt_reject_reason объясняет, почему 200 не было засчитано. Двенадцать запросов на домен: сначала robots.txt, затем главная страница и десять проб одновременно. Типичное время выполнения — от 2 до 4 секунд.
Создан для технического SEO-специалиста или инженера по росту, готовящего сайт к ИИ-краулерам и агентному трафику, или для агентства, продающего такие услуги и нуждающегося в аудите «до и после» по списку клиентов.
Вы платите за каждый проанализированный домен плюс небольшую плату за запуск актора. Повторный запуск в пределах 7-дневного окна кэша ничего не стоит.
Цены указаны на странице актора на Apify. Запуск этого сервера расходует кредиты Apify.
Это тонкий клиент для актора Apify. Он передаёт ваш ввод и возвращает вывод актора без изменений. Всё описанное выше поведение реализовано в акторе, а не здесь.
Ошибки отображаются, а не скрываются. Неверный ввод, недействительный токен, исчерпанный баланс, тайм-аут или запуск, возвращающий что-либо, кроме набора данных, — всё это возвращается как явная ошибка инструмента, а не как пустой результат.
Актор находится в Apify Store. Этот обёртка лицензирована по MIT.
Создано Mamba Labs
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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