Aiseo Audit MCP server

aiseo-audit

A deterministic, Lighthouse-style audit for AI search readiness. Test how easily generative engines can fetch, extract, understand, and reuse a web page.

AI SEO measures content readiness, not traditional search rank. The score is a research-informed heuristic audit, not a citation probability or a promise that an engine will cite the page.

Website & guides · Quick start · CI/CD · AI assistants · Library API · Scoring methodology

Quick Start

Requires Node.js 20.19 or later.

npx aiseo-audit https://yoursite.com

No install or API key is required. Analysis runs locally with no AI API calls or telemetry. Network access is limited to the page, the sitemap, and domain-signal files such as robots.txt and llms.txt.

Typical output begins with the overall score, pipeline stages, and highest-impact fixes:

Overall Score: 68/100  Grade: D+

Pipeline Stages:
  Technical Eligibility    PASS 96%
  Retrieval Alignment           58%
  Citation Fitness              63%
  Provenance                    42%

Top fixes:
  1. Topic Consistency  (Entity Clarity)
  2. Citation Patterns  (Grounding Signals)
  3. Lead Summary       (Answerability)

For a project-local CLI installation:

npm install --save-dev aiseo-audit

Install it as a regular dependency when using the programmatic API, or globally with npm install -g aiseo-audit.

Why aiseo-audit?

• Content analysis, not tag detection. Reads the page the way an engine would: does it name its subject consistently, answer questions directly, back claims with sources, and keep boilerplate out of the way? 50+ factors.
• Evidence-tiered scoring. Every factor is labeled supported, conditional, heuristic, diagnostic, or experimental and mapped to the reviewed literature in docs/EVIDENCE.md.
• Pipeline-stage reporting. See whether the main constraint is technical eligibility, retrieval alignment, citation fitness, or provenance.
• Deterministic and inspectable. The same tool version, configuration, and fetched HTML produce the same score. Weights and thresholds are documented rather than hidden behind an external service.
• Multiple ways to run. Use the CLI, GitHub Action, MCP server, or typed TypeScript API; render terminal, JSON, Markdown, or standalone HTML reports.

Common Workflows

Audit against target queries

Query-aware audits measure three things: query terms in structural fields such as the title and headings, query terms in the body text, and a section of the page addressing each aspect of the query. Supply about five queries for a stable measurement; the maximum is ten.

npx aiseo-audit https://example.com/guide \
  --query "how to set up sso" \
  --query "saml vs oidc" \
  --query "sso troubleshooting checklist"

Use --domain product for product-specific price, specification, and comparison checks. --engine gemini|gpt|perplexity applies experimental presets that reweight existing categories toward each engine; they add no new checks.

Shortened. The full README is on GitHub.