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.
MCP
hanselhansel/aeo-cli
Added 1 June 2026
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
How to use
Install
pip install context-linter Tools exposed
Sub-checkShare-of-Recommendation
Tested with
Claude Desktop, ChatGPT
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
Pairs with
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