AI Design Blueprint Doctrine
The industry-standard doctrine for safe, observable, and steerable AI agent UX — browse 10 principles, curated examples, and application guides via MCP.
AI Design Blueprint 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 filesmcp/: public MCP configuration and usage notesdocs/setup/: copy-first setup guides by toolcursor/,windsurf/,github-copilot/,gemini/: provider-specific instruction filesopen-weights/: static prompt packs for open-weight and local model workflowsexports/: 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
- Pick a setup guide in
docs/setup/. - Add the relevant file or MCP config to your own repository or client.
- If using MCP, initialize against
https://aidesignblueprint.com/mcp. - Run the first proof call:
clusters.list()
- 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 silentlysignals.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; setprivate_session=trueto skip all server-side logging for that callteam.summarize(days_back?, private_session?)— Pro/Teams; usage reflection and recommended next assetsme.add_evidence(course_slug, stage_id, note)
Feedback and value signal rules
- Only call
signals.reportafter the user has clearly expressed that something was useful. Never call automatically or silently. Offer at most once per session after a clear success signal. - Only call
signals.feedbackwhen the user explicitly asks to leave feedback. Never prompt for it proactively. - Never include proprietary code, file contents, or secrets in
brief_context.
Governance badges
Show that your agent or repo follows the Blueprint doctrine.
Free badge — paste into your README.md (no account required):
[](https://aidesignblueprint.com)
Pro badge — run architect.validate() via the MCP. The response includes run_id, badge_url, and review_url:
[](https://aidesignblueprint.com/en/readiness-review/<run_id>)
The Pro badge displays your tier (Governed · X/Y or Reviewed · X/Y) and links to a public readiness review page. Requires a Pro or Beta account.
What is intentionally not here yet
- no public OpenAPI schema
- no public HTTP API contract beyond MCP and static assets
- no CLI installer
- no speculative partner-specific distributions
Source of truth
This repo is intended to mirror the canonical public contract already shipped on aidesignblueprint.com.
Before publishing changes here, verify:
/mcp/llms.txt/agent-assets/[slug]/en/for-agents
remain consistent with the files committed in this repo.
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