Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Audit whether an AI agent can read a site: llms.txt, robots AI rules, structured data.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
APIFY_TOKEN标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"agent-accessibility-auditor": {
"command": "npx",
"args": [
"-y",
"@mambalabsdev/mcp-agent-accessibility-auditor"
],
"env": {
"APIFY_TOKEN": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add agent-accessibility-auditor --env APIFY_TOKEN=your-value -- npx -y @mambalabsdev/mcp-agent-accessibility-auditor运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"agent-accessibility-auditor": {
"command": "npx",
"args": [
"-y",
"@mambalabsdev/mcp-agent-accessibility-auditor"
],
"env": {
"APIFY_TOKEN": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"agent-accessibility-auditor": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@mambalabsdev/mcp-agent-accessibility-auditor"
],
"env": {
"APIFY_TOKEN": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日
发布者(mambabuilt.com)不是仓库所有者(mambalabsdev);可能是其他人代为上架的
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 0 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
你需要填写:APIFY_TOKEN
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(1.3.1)于前天进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
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按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
MCP server for the Mamba Labs Agent Accessibility Auditor actor on Apify.
Can an AI agent read this site? Give it a domain and it returns one flat row of 42 fields covering five families of fact: the llms.txt family, robots AI crawler policy including the newer Content Signal directives, structured data presence and health, render mode, and machine readable endpoint discovery.
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" } } } }
Get an Apify token at console.apify.com/account/integrations.
audit_agent_accessibilityDomain in, whether an AI agent can read that site out.
| Input | Type | Required | Notes |
|---|---|---|---|
domain | string | yes | One company domain, for example vercel.com. A full URL is accepted: the protocol and path are stripped. One domain per call. |
checks | array | no | Run only these checks: llms_txt, robots_ai, sitemap, openapi, security_txt, feeds, json_ld, microdata, open_graph, canonical, render_mode. Omit for all of them. A check you did not run reports null, never false, and the score is rescaled over what you selected. |
check_endpoints | boolean | no | Alias for the checks array: false removes sitemap, openapi, security_txt and feeds. Ignored when checks is set. Default true. |
check_structured_data | boolean | no | Alias for the checks array: false removes json_ld, microdata, open_graph and canonical. Ignored when checks is set. Default true. |
skipCache | enum | no | Leave as false to use the 7 day cache. Set to true to re-audit the domain from scratch. Default false. |
max_charge_usd | number | no | Optional cost cap in USD for this run. The actor stops before an item that could take its charges past this amount. Omit to rely on the maximum cost per run set in Apify. |
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.
has_llms_txt is true only when /llms.txt returns 200 and the body is real markdown, and llms_txt_reject_reason says why a 200 was not counted. Twelve requests per domain, robots.txt first and then the homepage and ten probes concurrently. Typical wall clock is 2 to 4 seconds.
Built for a technical SEO or growth engineer preparing a site for AI crawlers and agent traffic, or an agency selling that work and needing a before and after audit across a client list.
You are charged per domain analyzed, plus a small actor start fee. A repeat run inside the 7 day cache window costs nothing new.
Price per event on the Apify FREE plan, from the live pricing record; paid Apify plans pay less per event.
| Event | Price (USD) |
|---|---|
Domain analyzed (domain-analyzed) | 0.005 |
Actor Start (apify-actor-start) | 0.00005 |
Pricing is on the actor's Apify page. Running this server consumes Apify credits.
It is a thin client for the Apify actor. It passes your input through and returns the actor's output unchanged. Every behavior described above lives in the actor, not here.
The tool starts the actor run and polls it to a finished status, so a long run is not cut off at 300 seconds. The run is allowed 1,800 seconds. If it is still going two minutes after that, the call stops waiting and returns the run ID with a link to it in the Apify Console, where the results land when it finishes. A run that does not succeed comes back as an error with its run ID and status.
Errors are surfaced, never swallowed. An invalid input, an invalid token, an exhausted balance, a timeout, or a run that returns anything other than a dataset all come back as an explicit tool error rather than as an empty result.
The actor is on the Apify Store. This wrapper is MIT licensed.
Built by Mamba Labs