Specularis AI Visibility Audit

io.github.AdevAarons由 GitHub 账号 AdevAarons 发布v1.0.1更新于 Oct 24, 2026

Free AI visibility (GEO/AEO) audit: can ChatGPT, Claude and Perplexity find and cite your website?

发布方自己的服务：audit.domains - Domain Valuation

已验证Streamable HTTP可网页运行网页搜索与抓取商业与电商

服务详情

标识符: io.github.AdevAarons/specularis-mcp-audit
传输: Streamable HTTP
运行时: Web executable
信任: Verified
发布于: Sep 16, 2026

安全提示

MCP 服务会接收对话中的工具调用请求，并可能执行外部操作。启用前请先了解该服务能做什么，并只授予它所需的权限。

注册中心已验证此条目所属的 GitHub 账号 AdevAarons。这并不表明该账号就是产品方。

SourceWeft 的检查只涵盖条目是否完整、端点是否响应；不审查服务器的代码，也不审查其工具的行为。

在 README 中添加徽章

向读者展示该 MCP 服务已收录于 SourceWeft，徽章链接到本页。

[![Listed on SourceWeft](https://sourceweft.com/badge/mcp/io.github.AdevAarons%2Fspecularis-mcp-audit.svg)](https://sourceweft.com/mcp/io.github.AdevAarons%2Fspecularis-mcp-audit?utm_source=readme&utm_medium=badge)

概览

AI 生成的概览

远程 MCP 服务器，用于审计网站的 AI 可见性（GEO/AEO），并查看 ChatGPT、Claude、Perplexity 和 Gemini 引用哪些来源。

可以做什么: 对网站运行免费的 AI 可见性审计，在对话中返回 AI 爬虫访问、结构化数据和 llms.txt 的快照
提供邮箱地址后可获取按六大维度打分的完整 PDF 报告（0–100 分）
针对某个买家查询找出 AI 引擎实际引用的来源，并判断指定域名是否出现在其中
返回与厂商预约策略咨询的链接
适用场景: 适合想快速确认 AI 助手能否抓取、读取并引用某个网站，或想了解引擎针对某类买家问题引用哪些第三方来源的场景。主要面向关注 AI 搜索可见性的站点所有者、营销人员和代理机构。
运行要求: 无需安装：它是托管在远程的端点，作为自定义连接器或 HTTP MCP 服务器添加即可。托管服务未声明需要认证、密钥或请求头。自托管为可选项，需要 Node.js，且引用查找功能需要各引擎的 API 密钥。

安装前请注意

向审计工具提供邮箱地址会发送报告并将该人员登记为 Specularis 的潜在客户，若只要快照请勿填写。该服务由一家商业 GEO/AEO 代理机构运营，响应中包含其自身的预约链接。审计会以真实机器人 user agent 抓取目标网站，引用查找会把查询发送给第三方 AI 引擎。

安装

在 SourceWeft 中

打开控制台中的 Specularis AI Visibility Audit，将其添加到工作区。
为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.AdevAarons%2Fspecularis-mcp-audit/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "specularis-ai-visibility-audit": {
      "type": "http",
      "url": "https://specularis-mcp-audit-production.up.railway.app/mcp"
    }
  }
}

README

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):

No 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:

bash

claude mcp add --transport http specularis-audit https://specularis-mcp-audit-production.up.railway.app/mcp

claude mcp add --transport http specularis-audit https://specularis-mcp-audit-production.up.railway.app/mcp

Then 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:

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).

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

来源：README.md，提交 aa0b00d

工具

run_ai_visibility_audit

Run AI Visibility Audit

Run a free AI visibility (GEO/AEO) audit on a website — checks whether ChatGPT, Claude, Perplexity, and Gemini can find and cite it. Returns an instant snapshot of crawler access, structured data, and llms.txt. If an email is provided, a full scored report (0–100 across 5 pillars, with copy-paste fixes) is emailed as a PDF. Use this whenever a user asks to audit/check a site's AI visibility, GEO, AEO, or whether AI can find them.

输入参数nameroleemailwebsite_url必填

book_strategy_call

Book a Strategy Call

Get the link to book a free Specularis strategy call about AI visibility / GEO / AEO.

无需输入参数。

find_ai_citations

Find AI Citation Sources

Given a buyer query (e.g. 'best real estate agent in Tampa') and a website domain, find the exact sources ChatGPT, Perplexity, Claude, and Gemini cite when answering that query — and whether the domain appears in any of them. Returns the ranked source list (with which engine cites each) and an 'appears in X of N' gap. Use this whenever a user wants to know where AI gets its answers about their industry, which pages AI trusts for a query, or whether their business shows up in AI recommendations.

输入参数query必填domain必填