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MCP server for AI-SEO auditing: schema, robots.txt, llms.txt, citation scoring, and AEO/GEO rewrites
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MCP server for AI-SEO auditing: schema, robots.txt, llms.txt, citation scoring, and AEO/GEO rewrites
Security Report
10.0
Low Risk10.0Low RiskValid MCP server (2 strong, 4 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
5 files analyzed · 1 issue found
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How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-automatelab-tech-ai-seo": {
"args": [
"-y",
"@automatelab/ai-seo-mcp"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
@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Audit why AI systems do or do not cite your pages.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
sameAscoverage that help AI systems identify the subject - Citation formatting - canonical URL hygiene,
og:url,hreflang, noindex traps - Sitemap freshness -
lastmodsignals 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/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
| 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_caseto 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
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