SEO Audit in VS Code

Questions it answers

Find what to fix on a public website, for search engines and for AI answers.

- audit_site: SEO audit of up to 25 pages, failed rules by priority with the fix and source, a row per page with broken internal links and images without alt, plus Bing AI-search steps. - audit_page: the same page rules for one address. - local_ai_readiness: whether a local business site is ready to be named in AI search. - score_page_citation: a 0 to 100 citation score for one page, with fixes by biggest gain. - compare_citations: what the pages an AI answer cited share that your page lacks. - find_ai_questions: long searches to answer, from a pasted Search Console export. - check_ai_instructions: checks a site's llms.txt, llms-full.txt and AI page. - generate_ai_instructions: drafts an llms.txt for the owner to review. - audit_ai_search: grades a site on 12 on-site checks AI answer engines rely on, pass, partial or fail, with the 3 biggest fixes.

It reads only the public sites you name, respects robots.txt and refuses private addresses. It does not run JavaScript, query Google or AI engines, fetch directories, or measure rankings, traffic or backlinks. Scores and checks do not promise a citation, and the tools do not promise an effect on AI Overviews.

Nothing about you or the site is stored. Answers are cached for ten minutes. The optional judgement step, off by default, sends page text to a language model service. It and local checks are limited per day.

What it can do

• Audits up to 25 pages of a site from its homepage and sitemap, with robots.txt respected
• Returns failed rules grouped by priority with the affected pages, the fix and the documentation link
• Checks titles, descriptions, headings, canonicals, noindex, structured data, share tags and image alt text
• Finds broken links, redirect chains, sitemap problems, thin and duplicate content
• Checks local business markup: name, address, phone, hours, map, and that the details match the page
• Compares pages: near-identical templated pages, little own text, hreflang returns, sitemap pages nothing links to
• Adds Bing AI-search steps to a site audit: AI Performance, IndexNow, content, with sources
• Audits one page with the full page rule set
• Optional judgement of page purpose and competing pages, limited per day
• Checks local AI-search readiness: services, locations, prices, FAQ, schema, NAP, proof
• Checks llms.txt, llms-full.txt, an AI page, home links, and name, product and fact consistency
• Drafts an llms.txt from the site's own public pages for the owner to review
• Grades 12 AI-search checks from a site's own pages, each pass, partial, fail or unknown, with the top 3 fixes
• Scores one page from 0 to 100 for citation chance, with fixes by biggest gain
• Compares pages an AI answer cited with yours: opening answer, headings, table, list, date, links out, schema
• Ranks 7+ word searches in a pasted Search Console export, with page, question heading and answer skeleton

Tools

• audit_site, SEO audit (site). Use this to audit a whole site or several pages for SEO; it finds the pages itself. Never guess page addresses for requests such as "audit example.com", "which pages are worst?" or "my site has a page per city, are they too similar?". Pass the domain and optionally max_pages. Reads the homepage, sitemap and linked pages of that site, respecting robots.txt. Returns failed rules by priority, each with its pages, fix and docs link, plus a row per page with its status, broken internal links and images without alt text. It also compares pages: near-identical templated pages, pages with little own text, hreflang without return links or x-default, and sitemap pages nothing links to. Leave judge off unless the user asks what pages are for or which compete. It reads a sample, runs no JavaScript and measures no rankings, traffic or backlinks. For one named page use audit_page. Not for private or local addresses or to collect a site's content.
• score_page_citation, Citation score (page). Cite-chance score for one page, 0 to 100. Use this when the user asks whether ChatGPT, Perplexity or Google AI Overviews are likely to cite a page, or how to fix that. Pass exactly one of url, html, text, or sample. Returns the score and a fix list ordered by the biggest point gain. The checks cover an answer in the opening, question headings, an FAQ block, structured data, lists and tables, heading order, short sentences, a number with a source, a recent date, and an author line. Pasted html and text are scored as given and are not fetched. A url is one public page, with robots.txt respected. It does not call an AI engine and it does not promise a citation. Use compare_citations when the user has the pages an AI answer cited. Do not use it for private or local addresses, rankings, or traffic.
• find_ai_questions, Questions for AI search. Long-search finder for a pasted Search Console export. Use this when the user pastes Search Console Performance rows or a CSV and asks which questions to answer on their pages, or how to get named in ChatGPT or Google AI answers. Pass the text as csv, or JSON rows as rows, with query and impressions, and page if there is one. Up to 5000 rows. Returns the searches of 7 or more words ranked by impressions, marked when they look like questions. Each has the page Google shows, the question as a heading, and a 2-sentence answer skeleton with a placeholder for one real number or fact that only the owner knows. Also groups by page. It reads only what is pasted: no Google login, no Search Console call, no AI engine. It does not write the answer or invent the number. Not for a user with no export: ask them to paste one.
• compare_citations, Compare cited pages. Citation-pattern check for one query. Use this when the user has the pages an AI answer or Google AI Overview cited for a search and asks what they share, or why their own page is not cited. Pass query, cited_urls (2 to 6 public addresses the user copied from the answer) and exactly one of url, html or text for the user's page. Reads the public pages with robots.txt respected and returns the gaps first: each signal most cited pages share and the user's page lacks (opening answer, query words in a heading, question heading, table, list, date in the last year, links out, schema types, length), with counts such as 4 of 5 and a fix. Every signal is also listed per page. It reads no Google or AI engine, so the user must supply the cited addresses; never guess them. It stores nothing; change over days needs a new call later. A shared signal is a pattern in a small sample, not a proven cause, and no citation is promised. Do not use it for private addresses.
• audit_page, SEO audit (page). Use this only for a single page address the user gave. It fits requests such as "what SEO problems does https://example.com/docs have?" or "is the title and description of this page OK?". Pass the page address. For several pages or a whole site call audit_site once instead of this tool many times. Returns the page rules that failed grouped by priority, each with the fix and the documentation link, plus the title, description, headings and other facts that were read. robots.txt is respected. Not for a whole site or a request to find pages: use audit_site, which also runs the rules that compare pages or read the sitemap. Do not use it for private or local addresses, for rankings or traffic, or to read a page's text.
• local_ai_readiness, Local AI-search readiness. Local-business readiness for AI search answers. Use this when the user asks if a local business site is ready to be named by AI search, ChatGPT, Gemini or similar, or what to fix so assistants can cite it. Pass the business's own site. Crawls only that site's public pages (robots.txt respected, up to 25, default 15) and returns pass, warn or fail for a page per service, location or service-area pages, visible prices, FAQ, LocalBusiness schema (name, address, phone, hours, geo, sameAs), NAP consistency, on-site proof, a one-line description and recent dated content. Each check has the evidence URL and a fix. Also returns what Gemini, OpenAI and Anthropic leaned on in a Yext study and profile advice, which is not fetched. It does not query AI engines, directories or review sites, or measure rankings. Not for a site with no physical location or service area: use audit_site. Do not use it for private addresses or to collect contact details.
• check_ai_instructions, Check AI instructions. Checks the site's AI instructions files and page. Use this when the user asks whether a site publishes llms.txt, llms-full.txt or an AI instructions page, whether the home page links to them, and whether the file matches the site's own name, products and facts. Pass the domain. Returns pass, warn or fail for each part, with the evidence URL and a fix, plus the date the pages were read. A public post is cited as the reason the check exists. It does not promise an effect on AI Overviews and does not query AI engines. For a local business site, use local_ai_readiness; to draft the file, use generate_ai_instructions. Do not use it for private addresses or to collect contact details.
• generate_ai_instructions, Draft an llms.txt. Drafts an llms.txt from the site's own public pages. Use this when the owner wants a draft AI instructions file to review. Pass the domain. Returns markdown for the owner to edit and publish themselves. Nothing is written to the site. A public post is cited as the reason the draft exists. It does not promise an effect on AI Overviews and does not query AI engines. Do not use it for private addresses or to collect contact details.
• audit_ai_search, AI search audit (12 checks). Grades a site on 12 on-site checks for AI search. Use it when the user asks whether ChatGPT, Claude, Perplexity or Google AI would recommend or cite their site, or wants a GEO audit. Pass the domain. Reads up to 10 public pages without JavaScript (robots.txt respected). Each check is pass, partial, fail or unknown with evidence and a fix: no-JS text, crawler access, llms.txt, what-is sentence, prices page, comparison page, 10 questions, Organization and Product markup, one name, named quotes, dates, an original number. Returns a score out of 12 and the 3 biggest fixes. unknown means the fetch could not prove the row and earns no point. It does not query AI engines or promise a citation. Not for private addresses.

Add SEO Audit in VS Code

VS Code reads MCP servers from mcp.json and offers their tools to Copilot Chat in Agent mode. A workspace file at .vscode/mcp.json applies to that project. For every workspace, run MCP: Open User Configuration from the Command Palette and add the same entry there. These steps were checked against the VS Code MCP servers guide on 2026-10-09.

Create or edit the file so it contains this server. VS Code uses a top-level servers object, not mcpServers. Merge it with servers you already have.

{
  "servers": {
    "seo-audit": {
      "type": "http",
      "url": "https://seoaudit.openkrill.app/mcp"
    }
  }
}

No command, no headers and no input variables for a key: SEO Audit answers without a login. Save the file and select Start above the entry. VS Code asks once whether you trust the server: confirm it. Run MCP: List Servers to check that SEO Audit is running. You should see audit_site, score_page_citation, find_ai_questions, compare_citations, audit_page, local_ai_readiness, check_ai_instructions, generate_ai_instructions, audit_ai_search.

Open the Chat view, choose Agent mode and ask: Audit example.com for SEO problems The Configure Tools button in the chat input lists the tools of each server, and VS Code may ask you to confirm the first call. The server is public and read-only. It sees only the arguments of each call, so do not paste secrets into the question.

Command line users can add it to the user profile in one step with code --add-mcp and the same name, type and url as JSON. The portable .mcp.json at the project root also works in VS Code, with mcpServers as its top-level key. An organization can turn MCP off through Copilot policy: if the MCP commands are missing, ask the admin.

If it does not work

A call checked on 2026-10-17

example.com is one public page. Judgement stays off, so no page text is sent to a language model. The request below was posted to https://seoaudit.openkrill.app/mcp as tools/call. HTTP 200. Source: the SEO Audit server, read 2026-10-17.

{
  "method": "tools/call",
  "params": {
    "name": "audit_page",
    "arguments": {
      "url": "https://example.com",
      "judge": false
    }
  }
}

• status. 200
• notice. This is what the site served to a request from Agent Tools servers right now, read with robots.txt respected and without running JavaScript. The rules follow Google Search Central documentation and common conventions; ru
• url. https://example.com/
• finalUrl. https://example.com/

Repeat the call yourself if you need a newer reading. Cached answers expire. A rate limit is not a result: wait and try again. Nothing in the call is a ranking, a filing, or advice.

Using it in production

Nothing to install and nothing to run: no package, no Python or Node.js version and no environment variable. SEO Audit runs on our servers, and your client only needs the URL https://seoaudit.openkrill.app/mcp.

Use it in other apps

• SEO Audit in ChatGPT
• SEO Audit in Claude
• SEO Audit in Cursor
• SEO Audit in Pi

Roles that use SEO Audit

• SEO specialist guide

Frequently asked questions

Words on this page

• MCP (Model Context Protocol) definition
• Streamable HTTP transport definition
• Tool primitive definition
• Rate limit policy definition

Source: plugin listing for seo-audit in this repository, and a live tools/call on 2026-10-17. As of 2026-10-17.