aidesignblueprint/integrations

Official integrations and installable doctrine for AI Design Blueprint across MCP, IDE rules, prompt files, and agent runtimes.
What is in this repo
- shared/: cross-tool doctrine files
- mcp/: public MCP configuration and usage notes
- docs/setup/: copy-first setup guides by tool
- cursor/, windsurf/, github-copilot/, gemini/: provider-specific instruction files
- open-weights/: static prompt packs for open-weight and local model workflows
- exports/: structured doctrine export
Public contract
Canonical public endpoints:
- Site: https://aidesignblueprint.com
- MCP: https://aidesignblueprint.com/mcp
- Developer docs: https://aidesignblueprint.com/en/for-agents
Quick start
1. Pick a setup guide in docs/setup/.
2. Add the relevant file or MCP config to your own repository or client.
3. If using MCP, initialize against https://aidesignblueprint.com/mcp.
4. Run the first proof call:
- clusters.list()
5. Then run a second proof call:
- examples.search(query="orchestration visibility steering", limit=3)
Public MCP tools
Public retrieval tools (anonymous-allowed, read-only)
- principles.list(cluster?)
- clusters.list()
- principles.get(slug)
- clusters.get(slug)
- examples.get(slug)
- principles.search(query, limit?)
- examples.search(query, principle_ids?, difficulty?, library?, limit?)
- assets.list()
- guides.list()
- guides.get(slug)
- guides.search(query, limit?)
Public signal tools (anonymous-allowed, opt-in write)
- signals.report(event_type, surface_used?, brief_context?, perceived_value?, workflow_stage?, would_recommend?, team_size?) — records a value moment; only offer after the user clearly expresses something was useful; never call automatically or silently
- signals.feedback(task_type?, surface?, rating_clarity?, rating_usefulness?, what_helped?, what_missing?, would_use_again?, contact_email?, permission_to_follow_up?) — explicit qualitative feedback; only call when the user explicitly asks to leave feedback
Signal tools write only the structured fields you pass. No prompts, no code, no file contents are stored. See the privacy policy for full data-handling details.
Protected tools (authenticated, not part of anonymous setup path)
- me.learning_path()
- me.coaching_context()
- architect.validate(implementation_context, ..., private_session?) — Pro/Teams; scores agentic code against the 10 principles; set private_session=true to skip the stored run for that call
- design.validate(implementation_context, ..., private_session?) — Pro/Teams; the surface mirror: scores a rendered frontend artefact against the 8 experience-design laws (own weekly bucket)
- spec.validate(implementation_context, ..., private_session?) — Pro/Teams; the what-to-build lens: scores a written specification against the 8 spec-quality laws (own weekly bucket)
- team.summarize(days_back?, private_session?) — Pro/Teams; usage reflection and recommended next assets across all three validator lenses