Specularis AI Visibility Audit

io.github.AdevAarons由 GitHub 帳號 AdevAarons 發布v1.0.1更新於 Oct 24, 2026

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

發布方自己的服務：audit.domains - Domain Valuation

已驗證Streamable HTTP可網頁執行網頁搜尋與抓取商業與電商

服務詳細資訊

識別碼: io.github.AdevAarons/specularis-mcp-audit
傳輸: Streamable HTTP
執行環境: Web executable
信任: Verified
發布於: Sep 16, 2026

安全提示

MCP 服務會接收對話中的工具呼叫請求，並可能執行外部操作。啟用前請先了解該服務能做什麼，並只授予它所需的權限。

註冊中心已驗證此條目所屬的 GitHub 帳號 AdevAarons。這並不表明該帳號就是產品方。

SourceWeft 的檢查只涵蓋條目是否完整、端點是否回應；不審查伺服器的程式碼，也不審查其工具的行為。

在 README 中新增徽章

向讀者展示該 MCP 服務已收錄於 SourceWeft，徽章連結到本頁。

[![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)

概覽

AI 產生的概覽

遠端 MCP 伺服器，用來稽核網站的 AI 可見性（GEO/AEO），並查看 ChatGPT、Claude、Perplexity 與 Gemini 引用哪些來源。

可以做什麼: 對網站執行免費的 AI 可見性稽核，在對話中回傳 AI 爬蟲存取、結構化資料與 llms.txt 的快照
提供電子郵件地址後可取得依六大面向評分的完整 PDF 報告（0–100 分）
針對某個買家查詢找出 AI 引擎實際引用的來源，並判斷指定網域是否出現在其中
回傳向廠商預約策略諮詢的連結
適用情境: 適合想快速確認 AI 助理能否抓取、讀取並引用某個網站，或想了解引擎針對某類買家問題引用哪些第三方來源的情況。主要面向關注 AI 搜尋可見性的網站擁有者、行銷人員與代理商。
執行需求: 無需安裝：它是託管的遠端端點，以自訂連接器或 HTTP MCP 伺服器加入即可。託管服務未宣告需要驗證、金鑰或標頭。自架為選項，需要 Node.js，且引用查找功能需要各引擎的 API 金鑰。

安裝前請注意

向稽核工具提供電子郵件地址會寄送報告，並將該人員登記為 Specularis 的潛在客戶；若只要快照請勿填寫。此服務由一家商業 GEO/AEO 代理商營運，回覆中包含其自身的預約連結。稽核會以真實機器人 user agent 抓取目標網站，引用查找會將查詢送往第三方 AI 引擎。

安裝

在 SourceWeft 中

開啟儀表板中的 Specularis AI Visibility Audit，將其新增到工作區。
為需要使用其工具的對話啟用該服務。

Web executable，透過 Streamable HTTP。遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式，或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南，先向你展示發布者、端點和登入方式，經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.AdevAarons%2Fspecularis-mcp-audit/install.md，並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前，先告訴我它的發布者、端點和登入方式，等我確認。不要把 API 金鑰寫進命令或我會提交的檔案；問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "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

來源：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.

輸入參數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.

無需輸入參數。

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.

輸入參數query必填domain必填