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hanselhansel/aeo-cli

by Various

LLM readiness linter for websites. Audits robots.txt, llms.txt, Schema.org, and content density on a 0-100 scale. Includes MCP server. Published on PyPI: pip install context-cli.

H

MCP

hanselhansel/aeo-cli

Added 1 June 2026

#agent-readiness #ai #cli #linter #llm #llms-txt #mcp #python

Overview

A CLI tool that audits websites for LLM readiness by checking robots.txt, llms.txt, Schema.org, and content density. This tool scores each audit from 0 to 100 and includes an MCP server for extensibility. It is installable via pip as context-cli.

Best for

Best for
Developers who need a quick, actionable assessment of a website's optimization for LLM crawlers

Use cases

  • Auditing website compatibility with LLM crawlers
  • Verifying Schema.org markup for structured data
  • Checking robots.txt and llms.txt for proper directives

Notes

A CLI tool that audits websites for LLM readiness by checking robots.txt, llms.txt, Schema.org, and content density. This tool scores each audit from 0 to 100 and includes an MCP server for extensibility. It is installable via pip as context-cli.

3 stars on GitHub. Last updated 2026-03-17. Licensed MIT.

Use cases

  • Auditing website compatibility with LLM crawlers
  • Verifying Schema.org markup for structured data
  • Checking robots.txt and llms.txt for proper directives

Pros

  • Provides a specific, quantifiable score for LLM readiness
  • Includes an MCP server for integration into larger workflows
  • Simple pip install and command-line usage

Cons

  • Low community adoption (3 GitHub stars) may indicate limited support
  • Only audits a narrow set of factors for LLM readiness
  • Requires manual setup and understanding of each checked component

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Provides a specific, quantifiable score for LLM readiness
  • Includes an MCP server for integration into larger workflows
  • Simple pip install and command-line usage

Cons

  • Low community adoption (3 GitHub stars) may indicate limited support
  • Only audits a narrow set of factors for LLM readiness
  • Requires manual setup and understanding of each checked component