io.github.atomno-labs/mcp-seo-audit

Technical SEO site audit MCP: health score, issues, robots, sitemap, JSON-LD.

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Marketing

Step 1

Install in your client

Config is the same across clients — only the file and path differ.

Supported in Claude Desktopstdio · Node 18+

Paste into ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "io-github-atomno-labs-mcp-seo-audit": {
      "command": "<see-readme>",
      "args": []
    }
  }
}

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What io.github.atomno-labs/mcp-seo-audit does

Technical SEO site audit MCP: health score, issues, robots, sitemap, JSON-LD.

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No automated test available for this server. Check the GitHub README for setup instructions.

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Frequently Asked Questions

Is io.github.atomno-labs/mcp-seo-audit safe to use?: io.github.atomno-labs/mcp-seo-audit has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.
How do I install io.github.atomno-labs/mcp-seo-audit?: io.github.atomno-labs/mcp-seo-audit can be installed by cloning its GitHub repository and following the setup instructions in the README.
What AI clients work with io.github.atomno-labs/mcp-seo-audit?: io.github.atomno-labs/mcp-seo-audit is compatible with claude-desktop, cursor, claude-code. It uses stdio transport.

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Discussion

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Has anyone used this with Cursor?How do you handle auth?Any alternatives?

atomno-mcp-seo-audit

MCP (Model Context Protocol) server for technical SEO & GEO audits, powered by the detail.web engine — real measurements, not LLM guesses. Run it from Cursor, Claude Desktop or any MCP client. 8 tools: audit_site (deterministic 0–100 health score + letter grade, 78 checks across 8 categories, plus a GEO sub-score — visibility in ChatGPT / Perplexity / Google AI Overviews), audit_diff (compare vs the previous snapshot), robots.txt & sitemap.xml validators, JSON-LD & meta/OpenGraph builders, and per-check fix explainers. Probes TLS, redirects, TTFB, AI-crawler access (GPTBot) and llms.txt. Free tier + PRO (deep-crawl, GEO, 40+ deeper checks).

Why pair it with an LLM?

A language model on its own infers a site from training data and, at best, one rendered page — it can't directly read your TLS certificate's expiry, measure response time, parse sitemap.xml, or check whether GPTBot is blocked in robots.txt. This server runs those checks for real: actual HTTP requests, security headers, redirect chains, structured-data validation — and returns a deterministic score (same site → same number), reproducible enough to put in a client report. Think of it as the instrument and the LLM as the analyst that interprets the readout — the two work best together.

What you get

audit_site(url, depth=1, lang="ru") — one call returns:

health score 0–100 (higher is better) and a letter grade A–F;
issues grouped by category (security, SEO & indexing, performance, GEO, …), each with status pass / warn / fail;
a short human-readable summary.

audit_diff(url, lang="ru") — re-audits a site and compares it to the previous run: health/score delta and which checks got worse or better. The first call stores a baseline. This is something a one-off LLM question can't do — track a site over time. Stateful PRO feature (needs an API key).

list_checks(lang="ru") — the full catalogue of engine checks grouped by category, with a free / PRO badge on each — so you (and the agent) can see exactly what the free tier covers and what PRO unlocks.

explain_issue(check_id, lang="ru") — a deep-dive on a single check: why it matters and how to fix it. Pass a check_id from audit_site or list_checks. Title and category are localized; detailed advice is currently in Russian (the advice_lang field reports this).

validate_robots(content, lang="ru") — paste a robots.txt and get back syntax issues, whether a Sitemap: directive is present, whether CSS/JS is blocked from render bots, and which AI crawlers (GPTBot, ClaudeBot, …) are explicitly blocked. No fetch — validates the text you provide.

check_sitemap(url, lang="ru") — fetches a sitemap by URL and reports its format (urlset / sitemapindex), URL count and common problems (404, non-XML content type, http:// links, missing <lastmod>, the 50k-per-file limit). The fetch is SSRF-guarded on the server.

build_jsonld(type, fields, lang="ru") — generates a ready-to-paste schema.org JSON-LD <script> (Organization, LocalBusiness, Article, Product, FAQPage, BreadcrumbList, WebSite) and tells you which required/recommended fields are missing. It never invents data — only what you pass in.

build_meta(fields, lang="ru") — generates <head> meta tags (title, description, canonical, Open Graph, Twitter Card) and validates the title (50–60 chars) and de

Frequently Asked Questions

Is io.github.atomno-labs/mcp-seo-audit safe to use?

io.github.atomno-labs/mcp-seo-audit has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.

How do I install io.github.atomno-labs/mcp-seo-audit?

io.github.atomno-labs/mcp-seo-audit can be installed by cloning its GitHub repository and following the setup instructions in the README.

What AI clients work with io.github.atomno-labs/mcp-seo-audit?

io.github.atomno-labs/mcp-seo-audit is compatible with claude-desktop, cursor, claude-code. It uses stdio transport.

atomno-mcp-seo-audit

Why pair it with an LLM?

What you get

audit_site(url, depth=1, lang="ru") — one call returns:

health score 0–100 (higher is better) and a letter grade A–F;
issues grouped by category (security, SEO & indexing, performance, GEO, …), each with status pass / warn / fail;
a short human-readable summary.

build_meta(fields, lang="ru") — generates <head> meta tags (title, description, canonical, Open Graph, Twitter Card) and validates the title (50–60 chars) and de