Scan Results: https://Grok.ai

Vanity URL: scan.glintbase.xyz/grok-scan

LeaderboardNew Scan
50/100
Agent Readiness Score

AI-Friendly Docs

Parseable by AI models but has high token usage or lacks standard machine-readable hooks.

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Category Breakdown

Context Optimization20 / 30
Code Block Execution10 / 30
Machine Readability10 / 20
Agent Tooling & MCP10 / 20

Detailed Findings

⚡

Context Optimization

20/30

llms.txt Auto-Discovery

llms-full.txt Consolidated Docs

Markdown Content Negotiation (Accept: text/markdown)

↳ AI Agent Fix Prompt
My documentation is returning excessive HTML to AI agents.
Implement server-side content negotiation in my app.

When a request includes the header 'Accept: text/markdown',
return ONLY the technical content as clean Markdown.

Strip completely: navigation menus, footers, cookie banners,
sidebar links, breadcrumbs, social sharing buttons,
interactive widgets, and any HTML not containing technical content.

The response should contain only:
- Page title as H1
- Section headings as H2/H3
- Body text
- Code blocks with language tags
- Parameter tables
- No HTML tags, no CSS, no scripts

DOM Cleanliness Ratio (3267% markdown/HTML)

🔧

Code Block Execution

10/30

This category was analyzed using structural heuristics. Results reflect your documentation's observable patterns.

Terminal Prompt Pollution (No "$" prefixes in copyable code)

Dependency & Import Completeness

↳ AI Agent Fix Prompt
No code blocks found to analyze.

Dynamic Variable Placeholders

↳ AI Agent Fix Prompt
No code blocks found to analyze.
🔍

Machine Readability

10/20

OpenAPI/Swagger Spec Auto-Discovery

JSON-LD Structured Schema (TechArticle / APIReference / Guide)

↳ AI Agent Fix Prompt
Embed a <script type="application/ld+json"> block classifying your docs as a TechArticle, Guide, or APIReference, containing semantically enriched metadata.
🤖

Agent Tooling & MCP

10/20

Model Context Protocol (MCP) Server Integration

Error Code Diagnostics & Cross-Referencing Index

↳ AI Agent Fix Prompt
Provide a comprehensive table or section listing error codes, their causes, and solutions. This lets agents cross-reference error stacks and resolve integration errors automatically.