Audit web pages for AI search readiness (ChatGPT, Claude, Perplexity, Gemini).
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io.github.agencyenterprise/aiseo-audit is an MCP server that audit web pages for AI search readiness (ChatGPT, Claude, Perplexity, Gemini). Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 81/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"aiseo-audit": {
"args": [
"-y",
"aiseo-audit-mcp"
],
"command": "npx"
}
}
}Are you the author?
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Deterministic CLI that audits web pages for AI search readiness. Think Lighthouse, but for how well AI engines can fetch, extract, understand, and cite your content.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y 'aiseo-audit' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
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Deterministic CLI that audits web pages for AI search readiness. Think Lighthouse, but for how well AI engines can fetch, extract, understand, and cite your content.
AI SEO measures how reusable your content is for generative engines, not traditional search rankings.
Who is this for? Content teams running pre-publish checks, developers gating deployments in CI/CD, and marketers auditing their own or competitor pages. If your content needs to be cited (not just ranked), this tool tells you where you stand.
Traditional SEO optimizes for ranking in a list of links. AI SEO optimizes for being cited in generated answers. Different goal, different signals.
When someone asks ChatGPT, Claude, Perplexity, or Gemini a question, those engines fetch web content, extract the useful parts, and decide what to cite. AI SEO (also called Generative Engine Optimization or GEO) is the practice of structuring your content so that process works in your favor. The foundational research behind this field comes from Princeton's GEO paper, which identified the specific content traits that increase generative engine citations.
aiseo-audit measures those signals: can the content be extracted? Is it structured for reuse? Does it contain the patterns AI engines actually quote? It runs entirely locally with no AI API calls and no external services.
Most "AI readiness" audits check whether certain files and tags exist. Does the site have llms.txt? Is there a sitemap? Is JSON-LD present? Those are binary checks that tell you very little about whether AI engines will actually use your content.
aiseo-audit goes deeper: