Scan Results: https://docs.base.org
Vanity URL: scan.glintbase.xyz/base-scan
41/100
Agent Readiness Score
AI-Friendly Docs
Parseable by AI models but has high token usage or lacks standard machine-readable hooks.
Category Breakdown
Context Optimization21 / 30
Code Block Execution10 / 30
Machine Readability0 / 20
Agent Tooling & MCP10 / 20
Detailed Findings
⚡
Context Optimization
21/30
llms.txt Auto-Discovery
llms-full.txt Consolidated Docs
Markdown Content Negotiation (Accept: text/markdown)
DOM Cleanliness Ratio (1% markdown/HTML)
↳ AI Agent Fix Prompt
Reduce layout boilerplate (header, footers, scripts) or use Markdown Content Negotiation to strip DOM noise.
🔧
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
0/20
OpenAPI/Swagger Spec Auto-Discovery
↳ AI Agent Fix Prompt
Expose an OpenAPI or Swagger specification at a standard path (e.g. /openapi.json) or link to it using the "Link" HTTP response header with rel="openapi".
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.