AI Design Blueprint MCP server
Commit Oct 30, 2026Provides design principles, architectural validation, and structured doctrine for AI agents and development workflows.
Also known as aidesignblueprint/integrations
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- 3
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- Latest version
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- Last commit
- Oct 30, 2026
- License
- MIT
What is AI Design Blueprint MCP?
AI Design Blueprint provides design principles, architectural validation, and structured doctrine for AI agents and development workflows.
Summary generated from the project's README
About AI Design Blueprint MCP server
Providing architectural validation and design principles for AI agents is what this server enables. Developers and teams use it to integrate structured doctrine into workflows across IDEs like Cursor, Windsurf, and GitHub Copilot, or through local model setups using open-weight prompt packs. The server offers a range of retrieval tools for accessing principles, clusters, examples, and guides via public endpoints.
Users can access read-only tools like principles.search and examples.search without authentication. For more advanced needs, Pro and Teams users can utilize protected tools such as architect.validate to score agentic code against ten specific principles, or design.validate to check frontend artefacts against experience-design laws. The server also includes spec.validate for checking written specifications and team.summarize for usage reflection. While most retrieval is anonymous, certain validation and coaching tools require authenticated Pro or Teams accounts.
What you can do with it
- 1Search for design examplesFind specific orchestration or visibility examples to guide your development.
Written from the project's README. Every use case is checked against a quote from it.
README
View on GitHubAI 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; scores agentic code against the 10 principles; setprivate_session=trueto skip the stored run for that calldesign.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 lensesme.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.