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AI Seo README

The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the AI Seo listing page.

Back to AI Seo View source on GitHub

@automatelab/ai-seo-mcp

AI Citation Toolkit for the Model Context Protocol

npm version license node

Audit why AI systems do or do not cite your pages. MCP server. No API keys.

Works inside Claude, Cursor, Windsurf, Codex, and any MCP client that speaks stdio.


What it checks

  • AI crawler access - GPTBot, OAI-SearchBot, ClaudeBot, and PerplexityBot allowed or blocked in robots.txt
  • llms.txt - present, spec-compliant, links alive
  • Structured answer extraction - FAQ headings, BLUF paragraphs, answer-ready blocks
  • [[schema]] completeness - FAQPage, Article, Organization, Person; flags deprecated patterns
  • Entity clarity - named entity density and sameAs coverage that help AI systems identify the subject
  • Citation formatting - canonical URL hygiene, og:url, hreflang, noindex traps
  • Sitemap freshness - lastmod signals that tell crawlers the page is current

Run an audit. Get a list of citation-blockers, ranked.

You: Run an AI-SEO audit on https://automatelab.tech/launching-the-ai-seo-mcp/.

Result (truncated):

config.json
{
  "url": "https://automatelab.tech/launching-the-ai-seo-mcp/",
  "score": 61,
  "grade": "C",
  "dimension_scores": {
    "schema": 45, "technical": 80, "structure": 40,
    "robots": 90, "freshness": 85, "authority": 40,
    "entity_density": 21, "sitemap": 100
  },
  "findings": [
    {
      "severity": "critical",
      "category": "structure",
      "message": "No FAQ structure found (no FAQPage schema or H3 question headings).",
      "fix": "Add FAQ H3 headings ending in '?' with answer paragraphs, and a FAQPage JSON-LD block.",
      "estimated_impact": "high"
    },
    {
      "severity": "warning",
      "category": "authority",
      "message": "Low authority signals - missing Organization or author Person schema.",
      "fix": "Add Organization JSON-LD and Article.author as a Person node with sameAs links.",
      "estimated_impact": "high"
    }
  ]
}

Each finding names the exact fix. No opaque scores, no guesswork.


Install

Terminal
npx -y @automatelab/ai-seo-mcp

Requires Node 20 or later.

Claude Desktop

Add to %APPDATA%\Claude\claude_desktop_config.json (Windows) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

config.json
{
  "mcpServers": {
    "ai-seo": {
      "command": "npx",
      "args": ["-y", "@automatelab/ai-seo-mcp"]
    }
  }
}

Restart Claude Desktop. Any MCP client that supports stdio transport works - same command / args pattern.

Optional: headless rendering for SPAs

By default audit_page reads raw HTML — fast, but misses content on React/Vue/Angular SPAs. Pass render: "headless" to spin up Chromium and audit the rendered DOM (adds 3-10s per audit).

One-time install:

Terminal
npm install playwright-core
npx playwright install chromium

Then call audit_page with render: "headless". Use static for everything else — most marketing sites and docs render fine without it.


Run it in CI (GitHub Action)

This repo doubles as a GitHub Action. Drop it in a workflow to fail a PR when any page regresses below an AI-citation score - the same audit engine, gated on every change.

yaml
- uses: actions/checkout@v4
- name: AI-SEO audit
  uses: AutomateLab-tech/ai-seo-mcp@v0.5.0
  with:
    urls: "https://example.com,https://example.com/pricing"
    min-score: "70"            # fail if any URL scores below this
    respect-robots: "true"     # set false for staging / sites you own
    report-path: "ai-seo-report.md"   # optional Markdown report artifact
    fail-on-regression: "true"

The Action builds the auditor from the pinned ref, runs audit_page on each URL, writes a scorecard to the job summary, and exits non-zero if any URL falls below min-score (when fail-on-regression is true). Outputs: min_score_observed, urls_audited, report_path. Full example: examples/github-action-usage.yml.


Further reading

  • automatelab.tech - teardowns and case studies

MCP tool surface (19 tools)
ToolPurpose
audit_pageComposite AI-SEO audit with 8-dimension scoring (schema, technical, structure, robots, freshness, authority, entity density, sitemap).
audit_schemaValidate JSON-LD against Schema.org rules and AI-citation best practice. Flags deprecated patterns.
audit_canonicalCanonical link integrity, trailing-slash hygiene, og:url consistency.
audit_siteSingle-call site sweep: audit_page + check_robots + check_sitemap + audit_schema with overall grade and top-5 fixes.
audit_sitemapSite-wide content audit: stride-sample N URLs from the sitemap, run audit_page on each, return distribution + worst pages + top findings.
check_robotsParse robots.txt and report per-crawler allow/disallow for all known AI crawlers. Surfaces the GPTBot-blocked-but-OAI-SearchBot-allowed trap.
check_sitemapValidate XML sitemaps: presence, URL count, lastmod freshness, image/video extensions.
check_technicalHEAD tag audit: canonical, OpenGraph, Twitter Card, hreflang, HTTPS, noindex, title hygiene.
score_ai_overview_eligibilityScore a page's probability of appearing in Google AI Overviews using current correlation factors.
score_citation_worthinessScore how citable a page or text block is for Perplexity, ChatGPT, Google AI Overviews, and Claude. Includes per-section chunk_analysis / extractability_score: how cleanly an LLM can lift a standalone answer from each heading.
score_agentic_browsingScore a page against the Lighthouse "Agentic Browsing" category (May 2026): llms.txt, WebMCP, accessibility-tree integrity, and layout stability.
score_test_citationSimulate "would an AI engine cite this for this query?" via MCP sampling, with deterministic heuristic fallback.
llms_txt_generateGenerate llms.txt and optionally llms-full.txt from a domain's sitemap.
llms_txt_validateLint an existing llms.txt for spec compliance and broken links.
rewrite_aeoRewrite content for Answer Engine Optimization (BLUF structure, FAQ format, schema additions).
rewrite_geoRewrite content for Generative Engine Optimization (entity definitions, comparison tables, synthesis-ready structure).
extract_entitiesExtract named entities, sameAs links, and citation-density score from a page's content and structured data.
diff_pagesCompare two URLs for AI citation-worthiness: side-by-side dimension scores, gap analysis, and prioritized fix recommendations for url_a.
report_saveRender an audit_page / audit_site result as a Markdown report and write it to disk under MCP_WORKSPACE_ROOT.

v0.4.0 renamed tools from flat snake_case to dot-notation (audit_page, check_robots, …) for a navigable hierarchy. Update any saved invocations.

Environment variables: see ENV.md.


Contributing

Bug reports, feature ideas, and PRs welcome. See CONTRIBUTING.md.

Security

To report a vulnerability, see SECURITY.md.

License

MIT - see LICENSE.

Built by automatelab.tech