# 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/

API Docs:
- https://checklist.day/docs — full API reference

v1 API (versioned, structured):
- https://checklist.day/v1/registry — paginated registry index (library, type, category, description, language, status, version, install_tag)
- https://checklist.day/v1/registry/{library} — full registry doc: install, imports, compatibility summary
- https://checklist.day/v1/registry/{library}/install — install commands with coupled imports
- https://checklist.day/v1/registry/{library}/compatibility — full install compatibility matrix across Python versions and OS/libc
- https://checklist.day/v1/mcp — paginated MCP server index (slug, path, name, description, category, official, stars, transport, tool_count, status)
- https://checklist.day/v1/mcp/{path} — full MCP server doc: tools, install, env_vars, auth_type, github, homepage

MCP Server (Model Context Protocol):
- https://mcp.checklist.day/mcp — Streamable HTTP transport, tools: get_entry(library), search_registry(query), search_checklists(query)

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

Purpose:
High-density, structured data for RAG grounding and agent pipelines. Registry = dependency ground truth (imports, install, compatibility). Checklists = executable task primitives.