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Specularis AI Visibility Audit
概览
作者提供的说明（英文原文）
Free AI visibility (GEO/AEO) audit: can ChatGPT, Claude and Perplexity find and cite your website?
PopNx MCP 的三大支柱
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。
- 用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
- 我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
- 使用情况i非常新★ –
它能帮你做什么
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。
- 连接方式: 在云端运行
- 传输方式: Streamable HTTP
- 分类: 网页与浏览器
安装
选择你使用的 AI 应用，然后复制设置。
安装之前
- 基础检查：1 项中通过 1 项
- 在作者的服务器上运行：你向它提出的内容会经过他们的服务器。
- 注册中心没有列出需要填写的密钥。
你需要
- 在云端运行无需安装任何东西，通过互联网连接
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 …”。
适用于网页版、桌面版和手机版 Claude：
- 打开 Settings → Connectors
- 点击 Add custom connector
- 粘贴下面的地址，然后点击 Add
- 如果要求登录，请登录作者的服务
https://specularis-mcp-audit-production.up.railway.app/mcp在终端中运行这条命令
claude mcp add --transport http specularis-audit https://specularis-mcp-audit-production.up.railway.app/mcp或者自己复制设置
粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json
{
"mcpServers": {
"specularis-audit": {
"url": "https://specularis-mcp-audit-production.up.railway.app/mcp"
}
}
}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。
或者自己复制设置
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"specularis-audit": {
"type": "http",
"url": "https://specularis-mcp-audit-production.up.railway.app/mcp"
}
}
}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。
ChatGPT 只能连接互联网上的服务器：
- 打开网页版 ChatGPT → Settings → Connectors (Apps)
- 点击 Create 或 Add（部分套餐需要先在 Advanced 中开启 Developer mode）
- 粘贴下面的地址，起个名字，然后保存
- 如果要求登录，请登录作者的服务
https://specularis-mcp-audit-production.up.railway.app/mcp菜单名称可能因套餐和版本不同而略有差异。
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。
它能做什么
我们还没有读取这个服务器的工具列表；我们会轮流检查各个端点。
基础检查i
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9日
- ✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 AdevAarons 验证，该账户拥有此仓库
- … 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
- … 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
- … 尚未检查
端点有响应
尚未检查；我们会轮流检查各个端点
- … 尚未检查
使用 OAuth 登录
尚未检查；我们会轮流检查各个端点
- … 尚未检查
工具描述中没有隐藏指令
尚未检查；我们会在检查端点时读取工具列表
与“网页与浏览器”中的其他服务器比较i
- 星标数多于此分类中其他 1397 个服务器的 0%
- 发布到注册中心的时间比此分类中其他 1397 个服务器的 21% 更新
- 基础检查结果等于或优于此分类中其他 1397 个服务器的 100%
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。
常见问题
Specularis AI Visibility Audit 安全吗？
它在 1 项适用的基础检查中通过了 1 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。
它需要哪些密钥或代码？
注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。
它在我的电脑上运行还是在云端？
它在作者的云端运行。通过互联网连接，无需安装，但你发送的内容会经过作者的服务器。
它还在维护吗？
GitHub 仓库最后更新于–，最新版本（1.0.1）于3个月前进入注册中心。
哪些应用可以使用它？
支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接，所以接受远程 MCP 服务器的应用也可以使用，例如开启开发者模式的 ChatGPT。
有什么可以替代它？
同一分类中有：Browser Use, Chrome DevTools MCP, MCP Server Browser
替代选择
同一分类中其他作者的服务器，按星标数排序。
如何看卡片：↓ 每周下载量i★ 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
作者的 README
打开 README
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。
Specularis AI Visibility Audit — MCP Server
Run a free AI visibility (GEO/AEO) audit on any website from inside Claude, ChatGPT, or any MCP client — check whether ChatGPT, Claude, Perplexity and Gemini can reach, read and cite it, and find the sources they cite instead.
Hosted endpoint (Streamable HTTP):
https://specularis-mcp-audit-production.up.railway.app/mcpNo installation needed — it's a remote MCP server. Add the URL above and you're done.
Connect it
Claude (claude.ai or Claude Desktop): Settings → Connectors → Add custom connector → paste the URL above.
Claude Code:
claude mcp add --transport http specularis-audit https://specularis-mcp-audit-production.up.railway.app/mcpThen in any chat: *"Audit example.com for AI visibility"* — the tool runs.
Tools
| Tool | What it does |
|---|---|
run_ai_visibility_audit(website_url, email?, name?, role?) | Instant in-chat snapshot: AI crawler access (GPTBot, ClaudeBot, PerplexityBot, CCBot, Google-Extended), JSON-LD structured data, and llms.txt. Include an email to also receive the full scored report — 0–100 across six pillars — as a PDF. role accepts Real Estate Agent, Attorney, Founder or Other and tilts the scoring toward local entity signals, reviews and directories. |
find_ai_citations(query, domain) | Given a buyer question (*"best personal injury lawyer in Miami"*) and a domain, returns the sources AI engines actually cite when answering it, tagged by engine, plus an appears in X of N count for the domain. The ranked source list is the off-site target list. |
book_strategy_call() | Returns a link to book a strategy call with Specularis. |
Passing email to run_ai_visibility_audit sends a report and registers the person as a Specularis lead. Omit it for the snapshot alone.
Without an MCP client
The same scan engine is reachable over plain HTTP, with no key and no connector:
curl -s "https://specularis-mcp-audit-production.up.railway.app/api/scan?d=example.com"Pass a bare hostname — no scheme, no path. It returns the full structured scan: per-bot fetch results, browser-vs-bot word counts for render-gap detection, parsed schema types, llms.txt, sitemap, and the six pillar scores. Useful for scanning domains in bulk.
If inconclusive is true, the scan could not complete — read inconclusiveReason and don't report a score.
What the full audit measures
Each bot is fetched with its real published user agent, so the result is what the engine itself would receive — not what a browser sees.
The report scores six pillars out of 100:
| Pillar | Max | What it covers |
|---|---|---|
access | 20 | Whether GPTBot, ClaudeBot and PerplexityBot can actually reach and receive the page |
entity | 15 | Organization / Person / LocalBusiness schema, sameAs, address |
content | 15 | Whether the text answers the questions buyers actually ask |
offsite | 30 | The directories, review platforms and threads AI engines cite |
freshness | 10 | Whether the content is maintained |
technical | 10 | Sitemap, llms.txt, render gaps, crawlability |
offsite is deliberately the largest pillar — more than access and entity combined. That reflects how the engines behave: they cite third-party corroboration far more often than a company's own marketing pages, so a technically flawless site can still be invisible.
A note on measuring "blocked". A real block is a 403, or a genuine interstitial with under 80 words of page text. Bot-protection layers return challenge pages intermittently, and counting those as blocks inflates the result — we published numbers once that were wrong for exactly this reason, and corrected them. Re-run before reporting a block.
Across 561 US businesses scanned in 2026, roughly half were effectively invisible to AI engines.
Use it as an agent skill
SKILL.md packages this as a portable agent skill in the open SKILL.md format — when to run an audit, how to read every field it returns, and the order to apply the fixes in. It works against either the MCP tools or the HTTP endpoint above.
Self-hosting
The server is a single-file Node app (Streamable HTTP transport).
npm installnpm start
Point any MCP client at http://localhost:3000/mcp.
| Variable | Purpose |
|---|---|
N8N_WEBHOOK_URL | Backend audit engine endpoint for the full PDF report |
CONTACT_URL | Booking link returned by book_strategy_call |
AUDIT_URL | Web-form fallback link included in responses |
PORT | Listen port (default 3000) |
PERPLEXITY_API_KEY | Primary engine for find_ai_citations |
ANTHROPIC_API_KEY | Adds Claude as a second engine |
GEMINI_API_KEY | Adds Gemini as a third engine |
OPENAI_API_KEY | Adds ChatGPT as a fourth engine |
OPENAI_MODEL | Model for the ChatGPT engine (default gpt-5-mini) |
FINDER_DAY_MAX | Global daily cap on citation-finder runs (default 250) |
find_ai_citations needs at least one engine key. The engines array in each response reports which ones actually ran, so only claim the engines it names.
Who's behind it
Built by Specularis — a GEO/AEO agency helping businesses get found and cited by AI engines. The audit is free; the report is the real deliverable.
- Free audit (web form): https://specularisinc.com/free-audit
- Contact: https://specularisinc.com/contact
License
MIT