Ai Seo

io.github.AutomateLab-tech由 GitHub 账号 AutomateLab-tech 发布v0.1.1更新于 Oct 24, 2026

MCP server for AI-SEO auditing: schema, robots.txt, llms.txt, citation scoring, and AEO/GEO rewrites

概览

AI 生成的概览

本地 MCP 服务器,用于审计网页在 AI 搜索中的可见性:结构化数据、robots.txt、llms.txt、引用评分与 AEO/GEO 改写。

可以做什么
  • 对单页和整站做 AI 引用就绪度审计,给出八维度评分、按优先级排序的问题与具体修复建议
  • 检查抓取与索引信号:AI 爬虫的 robots.txt 规则、站点地图、canonical、OpenGraph、hreflang 与 noindex
  • 校验并生成 llms.txt,检查现有文件是否符合规范、链接是否失效
  • 评估引用价值、AI Overview 出现概率、智能体浏览就绪度,并模拟搜索引擎是否会引用某页
  • 提取命名实体与 sameAs 链接,对比两个网址,并按答案引擎与生成引擎优化改写内容
  • 将审计结果保存为 Markdown 报告写入磁盘
适用场景
适合发布网页内容、想弄清 AI 助手和搜索引擎为何引用或不引用自己页面的场景,也适合在 CI 流程中做可重复的 AI-SEO 检查。上线前校验结构化数据、robots.txt 和 llms.txt 时同样有用。
运行要求
通过 npx 以 stdio 在本地运行,需要 Node 20 或更高版本,无需 API 密钥。对 JavaScript 渲染的站点做无头渲染需额外安装 playwright-core 与 Chromium。保存报告会在 MCP_WORKSPACE_ROOT 目录下写入文件。
安装前请注意
审计会实际抓取目标网址,即从本机向这些页面发起请求。改写工具生成的新内容应在发布前人工审阅。保存报告会在配置的工作区根目录写入文件。v0.4.0 起工具名称有变更,已保存的调用可能需要更新。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Ai Seo,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

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Audit why AI systems do or do not cite your pages.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

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

bash
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):

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:

bash
npm install playwright-corenpx 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/[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.


Further reading


MCP tool surface (20 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

Maintenance and verification

See the maintenance guide for interpretation limits, current tool names where applicable, and reproducible verification.

来源:README.md,提交 f42d4da

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.1.1最新Sep 16, 2026

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