# Checklist.day: Operational Ground Truth for AI Agents

Allowed: indexing and analysis by LLMs and automated agents.

Primary Indexes:
- https://checklist.day/sitemap.xml
- https://checklist.day/

Checklists (machine-readable JSON):
- https://checklist.day/api/checklists — index of all checklists (title, category, region, url)
- https://checklist.day/{slug} — full checklist: title, category, region, description, steps

Registry (machine-readable JSON):
- https://checklist.day/api/registry — index of SDK/library entries (status, version, summary, tags)
- https://checklist.day/api/registry/{library} — full entry: install, imports, quickstart, warnings

MCP Server (Model Context Protocol):
- https://mcp.checklist.day/mcp — Streamable HTTP transport, tools: get_entry(library), search_registry(query), search_checklists(query)
- get_entry: returns full registry doc for a library by slug (e.g. 'chromadb', 'pinecone')
- search_registry: full-text search across registry by keyword, tag, or status
- search_checklists: full-text search across operational checklists by keyword, category, or region

Human-browsable indexes:
- https://checklist.day/checklists
- https://checklist.day/registry

Purpose:
High-density, structured data for RAG grounding. Focused on real-world agent
failure modes (security, reliability, architecture, operations).