AI Design Blueprint is a remote MCP server that gives an AI agent 29 tools, among them architect.certify, architect.validate, architect.validate_consensus. It answers at https://aidesignblueprint.com/mcp over streamable HTTP, and it listed its tools to an anonymous client, so no sign-in was needed for that. Our probes reached it 10 of 10 times since 4 September 2026; the last check, on 17 October 2026, succeeded.
Enterprise-safe agentic AI design doctrine. Read-only MCP, UK/EU residency, zero-training policy.
Description as the publisher wrote it.
| Tool | What it does |
|---|---|
architect.certify | Pro/Teams — second-pass adversarial certification of an architect.validate run that scored production_ready (A or B first-pass tier). ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. **RECOVERY FIRST**: the run_id is emitted in the FIRST notific Inputs: run_id, code |
architect.validate | Pro/Teams — first-pass doctrine review of agentic code/workflow against the 10-principle Agentic AI Blueprint. ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running LLM call (60-180s typical); MCP clients commonly close the call before t Inputs: implementation_context, focus_area, task, language, repository, files |
architect.validate_consensus | Pro/Teams — N-shot CONSENSUS doctrine review of agentic code. ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running (~80-120s for N=3 parallel LLM calls); MCP clients often close the call before the server returns. Retrying re-runs N × 6 Inputs: implementation_context, focus_area, task, language, repository, files |
assets.list | Public — list downloadable doctrine and agent asset artifacts (skill packs, rule packs, MCP setup snippets) the user can drop into their AI coding tool to import the Blueprint as native skill/rule files. Returns a list of assets with name, |
clusters.get | Get one principle cluster by stable slug. Returns the cluster definition, shared rationale, and the full set of member principles (slug + title) so the caller can pivot into principles.get without a second list call. WHEN TO CALL: the user Inputs: slug |
clusters.list | List all principle clusters with their stable slugs and linked principle titles. Use this to discover which clusters exist before drilling in with clusters.get or filtering principles.list by cluster. Prefer clusters.get when you already kn |
design.validate | Pro/Teams — first-pass surface-craft review of a FRONTEND artefact (component, screen, or flow) against the 8 laws of the Experience Design Blueprint. The surface-craft companion to architect.validate: where architect.validate scores agenti Inputs: implementation_context, task, goals, files, repository, private_session |
examples.get | Get one curated example by stable slug. Returns title, summary, source-code links, principle coverage (the principle slugs the example demonstrates), difficulty, library/framework, and implementation notes. Use this when you already have th Inputs: slug |
examples.search | Search curated examples by free-text query, ranked by relevance, with optional filters: principle_ids (only examples covering those principles), difficulty (beginner/intermediate/advanced), library (e.g. 'langgraph', 'openai'). Returns each Inputs: query, principle_ids, difficulty, library, pattern_family, limit |
guides.get | Get a full application guide by its stable slug (e.g. 'security-application', 'observable-evaluation'). Returns sections, action items, and linked principles. Use this when you already have the guide slug from guides.list or guides.search. Inputs: slug |
guides.list | List application guides that show how Blueprint principles apply to engineering challenges (security, evaluation, observability, etc.). Use this to discover which guides exist before drilling in. Prefer guides.search when the user describes |
guides.search | Search application guides by free-text query, matched against section answers and action items. Use this when the user describes an engineering challenge (security review, evaluation harness, observability) and wants matching guides. Prefer Inputs: query, limit |
handoffs.agency | Authenticated — submit an agency engagement enquiry on behalf of the caller for a founder-led discovery call. Persists an AgencyHandoff row routed to the agency inbox; the user is contacted by the team for a scoped proposal. Engagement scop Inputs: reason, company, role, workflow_stage, support_type, website |
handoffs.operator | Authenticated — creates a support handoff record when an agent needs human review, account-specific escalation, or operator follow-up that cannot be resolved with the read-only doctrine tools. Persists a SupportHandoff row (reason, topic, p Inputs: reason, topic, page_url, agent_name, agent_platform, trace_summary |
handoffs.partnership | Authenticated — creates a partnerships handoff record for design-partner, ecosystem, training, or advisory conversations needing human review. Persists a PartnershipHandoff row routed to the partnerships inbox; the user is contacted by the Inputs: reason, topic, organization, role, website, agent_name |
me.add_evidence | Authenticated — append a free-text evidence note to a specific stage in the caller's active course. Notes record concrete implementation observations, decisions, or artefacts that demonstrate progress through a Blueprint principle (e.g. how Inputs: course_slug, stage_id, note |
me.await_steer | Pro/Teams. BLOCK until the session owner posts the next `steer` event to a Governed Session from the AIDB Studio cockpit, then return it. DELIVERY GUARANTEE: the durable cursor read against the session log is authoritative (at-least-once: a Inputs: session_id, after_event_id, timeout_s |
me.coaching_context | Authenticated — returns stages in the caller's active course where recorded evidence is thin relative to the stage's principle requirements. Each thin stage carries the missing principle slugs + a short diagnostic so the caller can suggest |
me.learning_path | Authenticated — returns the caller's Blueprint learning-path state: current course slug, stage progress, certification status (Foundation, Practitioner, Capstone), Capstone track eligibility flags, and the next recommended stage. WHEN TO CA |
me.session_event | Pro/Teams — append a TYPED TEAM EVENT to a Governed Session's timeline (GEP-M6). This is how the user's own harness makes trio work inspectable: handoffs between role lenses, pushbacks, plan previews, gates, and acks land as structured even Inputs: session_id, event_type, actor, summary |
me.sessions | Pro/Teams — list or inspect the authenticated user's Governed Sessions (GEP-M2): durable, owner-scoped containers that group validation runs across lenses (architect.validate → 'architecture', design.validate → 'surface', spec.validate → 's Inputs: session_id |
me.validation_history | Pro/Teams — return the authenticated user's validation run history for all three lenses (architect.validate → validator='architecture', design.validate → validator='surface', spec.validate → validator='spec') with the Blueprint Readiness Sc Inputs: repository, run_id, limit |
principles.get | Get one doctrine entry by stable slug. The lens selects the doctrine: 'architecture' = one of the 10 agentic principles (default); 'surface' = one of the 8 experience-design laws; 'spec' = one of the 8 spec-quality laws. Returns id, title, Inputs: slug, lens |
principles.list | List Blueprint doctrine with stable slugs, titles, and clusters. The lens selects which of the three public doctrines: 'architecture' = the 10 agentic principles (default, the architect.validate rubric); 'surface' = the 8 experience-design Inputs: cluster, lens |
principles.search | Search Blueprint principles by free-text query and return the closest matches ranked by relevance. Use this to find principles related to a specific design challenge, failure mode, or keyword (e.g. 'reversibility', 'approval flow', 'delegat Inputs: query, limit |
signals.feedback | Public — records explicit free-text user feedback about the Blueprint, this tool surface, or a specific principle/example. Captures category (bug, doctrine_critique, missing_example, ergonomics, other), free-text body, and optional contact_ Inputs: task_type, surface, rating_clarity, rating_usefulness, what_helped, what_missing |
signals.report | Pro/Teams — records a value moment (e.g. review_confidence, runtime_risk_found, workflow_clarity) after a successful validate run on any lens — architect.validate, design.validate, or spec.validate — or a doctrine session. Each event captur Inputs: event_type, surface_used, brief_context, perceived_value, workflow_stage, would_recommend |
spec.validate | Pro/Teams — first-pass specification-quality review of a WRITTEN SPEC (proposal, design doc, task breakdown, or an OpenSpec-style change bundle) against the 8 laws of the Spec Quality Blueprint. The what-to-build lens of the doctrine trio, Inputs: implementation_context, task, goals, files, repository, private_session |
team.summarize | Pro/Teams — summarises the caller's tool-usage patterns and value signals over a configurable window (default 30 days). Returns tool_call_counts, top principles cited in validate runs, value_event_counts by event_type, and an aggregate read Inputs: days_back, private_session |
Every block below points at https://aidesignblueprint.com/mcp. Pick your client and copy.
claude mcp add --transport http ai-design-blueprint https://aidesignblueprint.com/mcp
{
"mcpServers": {
"ai-design-blueprint": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://aidesignblueprint.com/mcp"]
}
}
}{
"mcpServers": {
"ai-design-blueprint": {
"url": "https://aidesignblueprint.com/mcp"
}
}
}{
"servers": {
"ai-design-blueprint": {
"type": "http",
"url": "https://aidesignblueprint.com/mcp"
}
}
}{
"context_servers": {
"ai-design-blueprint": {
"url": "https://aidesignblueprint.com/mcp"
}
}
}{
"mcpServers": {
"ai-design-blueprint": {
"serverUrl": "https://aidesignblueprint.com/mcp"
}
}
}{
"mcpServers": {
"ai-design-blueprint": {
"url": "https://aidesignblueprint.com/mcp"
}
}
}name: AI Design Blueprint
version: 0.0.1
schema: v1
mcpServers:
- name: AI Design Blueprint
type: streamable-http
url: https://aidesignblueprint.com/mcp{
"mcpServers": {
"ai-design-blueprint": {
"httpUrl": "https://aidesignblueprint.com/mcp"
}
}
}{
"mcpServers": {
"ai-design-blueprint": {
"type": "http",
"url": "https://aidesignblueprint.com/mcp"
}
}
}{
"mcpServers": {
"ai-design-blueprint": {
"url": "https://aidesignblueprint.com/mcp"
}
}
}goose://extension?url=https%3A%2F%2Faidesignblueprint.com%2Fmcp&type=streamable_http&id=ai-design-blueprint&name=AI%20Design%20Blueprint
It answered our last check on 17 October 2026 in 158 ms. Over 10 checks since 4 September 2026 it answered 10. Status here comes from Neuronto's own probes, not from the publisher.
Yes. It is published by aidesignblueprint.com and served from aidesignblueprint.com, the vendor's own domain: https://aidesignblueprint.com/mcp.
29, as returned by the server's own tools/list on 9 September 2026: architect.certify, architect.validate, architect.validate_consensus, assets.list, clusters.get, clusters.list, design.validate, examples.get, and 21 more.
It answered our last check on 17 October 2026 in 158 ms. Over 10 checks since 4 September 2026 it answered 10.
Put the server under mcpServers in ~/.cursor/mcp.json with the URL https://aidesignblueprint.com/mcp. The exact JSON is above, with the same setup for Claude Code, Claude Desktop, VS Code, Windsurf, Zed, Gemini CLI and the rest.
It listed its tools to an anonymous client, so reading the tool list needs no key. Individual tools can still ask for credentials in their own inputs.
Publisher: com.aidesignblueprint. Endpoint host: aidesignblueprint.com. ARD identifier: urn:air:com.aidesignblueprint:mcp:blueprint.
First indexed 31 August 2026. The tools are exactly what the server returned; nothing on this page
is inferred from its name. Ranking in Neuronto search is never for sale.