AutomateLab-si-seo MCP Server

Servidor MCP independente de fornecedor que audita, pontua e reescreve páginas da web para elegibilidade de citação por IA. Sem chaves de API. Sem registro. Funciona no Claude Desktop, Cursor, Cline, Windsurf, VS Code (Copilot / Continue) e qualquer cliente que utilize o Model Context Protocol.

GitHub

Documentação

@automatelab/ai-seo-mcp

AI Citation Toolkit for the Model Context Protocol

Audit why AI systems do or do not cite your pages.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):

{
  "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

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):

{
  "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:

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.

- uses: actions/checkout@v4
- name: AI-SEO audit
  uses: AutomateLab-tech/[email protected]
  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.

Tool	Purpose
`audit_page`	Composite AI-SEO audit with 8-dimension scoring (schema, technical, structure, robots, freshness, authority, entity density, sitemap).
`audit_schema`	Validate JSON-LD against Schema.org rules and AI-citation best practice. Flags deprecated patterns.
`audit_canonical`	Canonical link integrity, trailing-slash hygiene, `og:url` consistency.
`audit_site`	Single-call site sweep: `audit_page` + `check_robots` + `check_sitemap` + `audit_schema` with overall grade and top-5 fixes.
`audit_sitemap`	Site-wide content audit: stride-sample N URLs from the sitemap, run `audit_page` on each, return distribution + worst pages + top findings.
`check_robots`	Parse `robots.txt` and report per-crawler allow/disallow for all known AI crawlers. Surfaces the GPTBot-blocked-but-OAI-SearchBot-allowed trap.
`check_sitemap`	Validate XML sitemaps: presence, URL count, `lastmod` freshness, image/video extensions.
`check_technical`	HEAD tag audit: canonical, OpenGraph, Twitter Card, hreflang, HTTPS, noindex, title hygiene.
`score_ai_overview_eligibility`	Score a page's probability of appearing in Google AI Overviews using current correlation factors.
`score_citation_worthiness`	Score 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_browsing`	Score a page against the Lighthouse "Agentic Browsing" category (May 2026): llms.txt, WebMCP, accessibility-tree integrity, and layout stability.
`score_test_citation`	Simulate "would an AI engine cite this for this query?" via MCP sampling, with deterministic heuristic fallback.
`llms_txt_generate`	Generate `llms.txt` and optionally `llms-full.txt` from a domain's sitemap.
`llms_txt_validate`	Lint an existing `llms.txt` for spec compliance and broken links.
`rewrite_aeo`	Rewrite content for Answer Engine Optimization (BLUF structure, FAQ format, schema additions).
`rewrite_geo`	Rewrite content for Generative Engine Optimization (entity definitions, comparison tables, synthesis-ready structure).
`extract_entities`	Extract named entities, `sameAs` links, and citation-density score from a page's content and structured data.
`diff_pages`	Compare two URLs for AI citation-worthiness: side-by-side dimension scores, gap analysis, and prioritized fix recommendations for url_a.
`report_save`	Render 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