pulsemcp/ai-design-blueprint-doctrine · v1.26.0
Also listed as mcp-registry/com.aidesignblueprint/blueprint · primary listing: mcp-registry/com.aidesignblueprint/blueprint
As of 2026-10-24 07:13 UTC, AI Design Blueprint Doctrine is up with a Glimind reliability score of 97/100 (grade A). Over the last 30 days it answered 100% of Glimind's liveness checks. It had 14 outages in 90 days, with a median recovery of 3 minutes.
Agentic AI Blueprint doctrine and 13 public tools for learning and prototyping agent patterns.
Claude Code
claude mcp add --transport http ai-design-blueprint-doctrine https://aidesignblueprint.com/mcp
Claude Desktop · Windsurf · Cline (mcpServers JSON)
{
"mcpServers": {
"ai-design-blueprint-doctrine": {
"type": "http",
"url": "https://aidesignblueprint.com/mcp"
}
}
}
Third-party server, listed as discovered. Review its source before installing.
1 tool definition(s) exceed ~1,200 tokens each (largest: architect.validate, ~1,256).
Move long reference material out of descriptions (link a doc or expose a resource), drop repeated examples, and collapse large enums. Trimming these to ~600 tokens saves ~656 tokens every session. How to fix →
29 tool name(s) contain "." or exceed 64 characters, which OpenAI-compatible function calling rejects (^[a-zA-Z0-9_-]{1,64}$).
Use "_" or "-" instead of "." and keep names to 64 characters, so clients built on OpenAI-style tool calling don't have to rename (or drop) them. How to fix →
In the last 30 days: 0 tool(s) removed and 15 schema change(s).
Add new tools or optional parameters instead of renaming or removing; deprecate first (say so in the description) and remove later. Agents cache tool contracts. How to fix →
Paste-ready definitions with the problems above fixed. Changes marked verify need a human look. All of them as JSON: /v1/readiness/pulsemcp%2Fai-design-blueprint-doctrine/fixes
{
"name": "architect_certify",
"description": "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 notifications/progress event at t=0s (BEFORE the LLM call begins). Capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently within a 6-minute budget. This is the canonical ",
"inputSchema": {
"type": "object",
"title": "architect_certify_runArguments",
"required": [
"run_id"
],
"properties": {
"code": {
"type": "string",
"title": "Code",
"default": "",
"description": "The same code that was sent to architect.validate to produce this run_id. Sent verbatim: the cert reviewer needs the actual code to surface production_blockers the first pass missed. May be omitted (empty string) when the prior validate stored the code under the 24h cert-retry hold; in that case the server reuses the stored code automatically. Handled as the tool description states: sent to OpenAI (US) for the check (no-training) and persisted at OpenAI, JSON-escaped and delimited as inert data."
},
"run_id": {
"type": "string",
"title": "Run Id",
"description": "The run_id from a prior architect.validate call. Returned in the validate response when persistence_status='saved'. Must be owned by the caller (per-user authorisation, same gate as me.validation_history)."
}
}
},
"annotations": {
"readOnlyHint": false,
"openWorldHint": true,
"idempotentHint": false,
"destructiveHint": false
},
"title": "Certify Production-Ready Architecture"
}{
"name": "architect_validate",
"description": "Pro/Teams: first-pass doctrine review of agentic code/workflow against the 10-principle AI Design Blueprint doctrine. ON CLIENT TIMEOUT: DO NOT RETRY THIS TOOL. Long-running LLM call (60-180s typical); MCP clients commonly close the call before the server returns. Retrying re-runs the 60-180s LLM call from scratch and burns compute. RECOVERY: the run_id is emitted in the FIRST notifications/progress event at t=0s (before the LLM call begins), capture it. On timeout, call `me.validation_history(r",
"inputSchema": {
"type": "object",
"title": "validate_agent_architectureArguments",
"required": [
"implementation_context"
],
"properties": {
"task": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Task",
"default": null,
"description": "What the agent or workflow is trying to accomplish. Adds evaluation context."
},
"files": {
"anyOf": [
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"title": "Files",
"default": null,
"description": "List of file paths relevant to the implementation context."
},
"goals": {
"anyOf": [
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"title": "Goals",
"default": null,
"description": "Specific safety or quality goals to evaluate against (e.g. 'prevent irreversible actions', 'explicit approvals')."
},
"language": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Language",
"default": null,
"description": "Programming language of the code being evaluated (e.g. 'python', 'typescript')."
},
"focus_area": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Focus Area",
"default": null,
"description": "Narrow the evaluation to a specific principle cluster or slug (e.g. 'delegation', 'visibility', 'establish-trust-through-inspectability')."
},
"repository": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Repository",
"default": null,
"description": "Iteration key. SAME value across calls auto-resolves the most recent prior run as `prior_run_baseline` for iteration-aware grading (per-principle severity deltas, regressions/improvements). CHANGING the value (even subtly with an `iter-2` suffix) silently severs the chain and yields a fresh blind first-shot. Round numbering belongs in `task`, not here. Empirical evidence of why anchoring matters: PR #157 iter1 33/F vs iter2 100/A on byte-identical baseline-race primitives (+67 spread); invoice-payment-manager #158 38/F vs #159 74/C (+36 spread), same code, score variance from non-deterministic LLM at reasoning_effort=high; the baseline anchor collapses this onto a stable arc."
},
"session_id": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"title": "Session Id",
"default": null,
"description": "Optional Governed Session to attach this run to (GEP-M2). Must reference a session YOU own (list via me.sessions; sessions are created in the web app at /app/sessions): foreign ids are refused before any model call. The run then appears on the session's timeline alongside the other lenses. With private_session=true no run is stored so nothing attaches, but the ownership check still runs FIRST: a session id you don't own fails the call either way."
},
"example_limit": {
"type": "integer",
"title": "Example Limit",
"default": 3,
"minimum": 1,
"description": "Maximum number of curated examples to include in recommendations."
},
"private_session": {
"type": "boolean",
"title": "Private Session",
"default": false,
"description": "Set to true to skip the stored run AND prior-run anchoring AND run_id recovery for this call. Operational security and cost logs are still kept, per the Privacy Policy. The request is also not persisted in the model provider's response store; the provider's abuse-monitoring retention still applies. Use for private one-shots that don't participate in the iteration arc. Default false."
},
"implementation_context": {
"type": "string",
"title": "Implementation Context",
"description": "The artifact under review. SEND FULL FILE CONTENTS VERBATIM: the architect cites per-line evidence (identifiers, branch ordering, structural choices); any compression destroys evidence and produces hallucinated findings on code that isn't there. CONCRETE DON'TS: do NOT replace docstrings/comments with `...`; do NOT condense multi-line statements; do NOT replace dict/set comprehensions with `{...}`; do NOT remove explanatory comments to save tokens. If the file is large, split into MULTIPLE architect.validate calls scoped by file/module: never truncate one call. Architecture summaries (high-level prose) accepted ONLY for greenfield (no code yet); never as a substitute for code that already exists."
}
}
},
"annotations": {
"readOnlyHint": false,
"openWorldHint": true,
"idempotentHint": false,
"destructiveHint": false
},
"title": "Validate Agent Architecture"
}{
"name": "architect_validate_consensus",
"description": "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 × 60-180s LLM calls from scratch and burns N× compute. RECOVERY: same heartbeat pattern as architect.validate, the run_id is emitted in the FIRST progress event at t=0s (before LLM children fire); on timeout, call `me.validation_history(run_id='<that-id>')` to fetc",
"inputSchema": {
"type": "object",
"title": "validate_consensus_agent_architectureArguments",
"required": [
"implementation_context"
],
"properties": {
"n": {
"type": "integer",
"title": "N",
"default": 3,
"maximum": 10,
"minimum": 2,
"description": "Number of parallel child runs. Default 3 (the variance signal is visible at N=3; cost = 3× LLM bill). Capped server-side by Settings.consensus_n_max (default 5)."
},
"task": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Task",
"default": null,
"description": "What the agent or workflow is trying to accomplish."
},
"files": {
"anyOf": [
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"title": "Files",
"default": null,
"description": "List of file paths relevant to the implementation."
},
"goals": {
"anyOf": [
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"title": "Goals",
"default": null,
"description": "Specific safety or quality goals to evaluate against."
},
"language": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Language",
"default": null,
"description": "Programming language of the code (e.g. 'python')."
},
"focus_area": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Focus Area",
"default": null,
"description": "Optional: narrow the review to a principle cluster or slug."
},
"repository": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"title": "Repository",
"default": null,
"description": "Iteration key. Consensus children all run unanchored (`private_session=True`), but the consolidated row IS persisted under this key: discoverable as prior baseline for the next single-shot `architect.validate`. Same value across calls keeps the iteration arc inspectable."
},
"example_limit": {
"type": "integer",
"title": "Example Limit",
"default": 3,
"minimum": 1,
"description": "Max curated examples per child run."
},
"implementation_context": {
"type": "string",
"title": "Implementation Context",
"description": "The artifact under review. SEND FULL FILE CONTENTS VERBATIM: same constraint as architect.validate. Truncation produces hallucinated findings on code that isn't there."
}
}
},
"annotations": {
"readOnlyHint": false,
"openWorldHint": true,
"idempotentHint": false,
"destructiveHint": false
},
"title": "Validate Agent Architecture (Consensus Mode)"
}Get an email or webhook when this server's readiness drops by 10+ points, tool-poisoning text appears, or a tool breaks:
curl -X POST https://glimind.com/v1/subscriptions -H 'content-type: application/json' -d '{"tools":["pulsemcp/ai-design-blueprint-doctrine"],"email":"[email protected]"}'Agents: watch_tool({"toolId": "pulsemcp/ai-design-blueprint-doctrine"}) over MCP.
architect.validate ~1,256 tokenssignals.report ~1,049 tokenssignals.feedback ~874 tokensdesign.validate ~836 tokensspec.validate ~836 tokensStatic checks of the advertised tools/list (schema, descriptions, annotations, tool-poisoning patterns, names, size) plus observed protocol, uptime, latency and contract changes. Read-only: no tool is ever called. Weights: schema 15, docs 20, safety 15, efficiency 15, compatibility 15, protocol 5, reliability 10, stability 5. Token counts are estimates (~3.6 characters per token).
Operator? Fix these, then re-check live. Embed the readiness badge:
[](https://glimind.com/server/pulsemcp%2Fai-design-blueprint-doctrine?ref=badge)
<a href="https://glimind.com/server/pulsemcp%2Fai-design-blueprint-doctrine?ref=badge"><img src="https://glimind.com/v1/readiness/badge/pulsemcp%2Fai-design-blueprint-doctrine.svg" alt="MCP agent-readiness"></a>
.. image:: https://glimind.com/v1/readiness/badge/pulsemcp%2Fai-design-blueprint-doctrine.svg :target: https://glimind.com/server/pulsemcp%2Fai-design-blueprint-doctrine?ref=badge :alt: MCP agent-readiness
Glimind records every version of this server's tool definitions in a signed, append-only transparency log. Current manifest sha256:abe0d509e7f5ba4c…, first seen 2026-10-17, included at leaf 9380 of 57,174. Version history · verify what your client received.
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Claude Code
claude mcp add --transport http glimind https://glimind.com/mcp
Claude Desktop · Windsurf · Cline (mcpServers JSON)
{
"mcpServers": {
"glimind": {
"type": "http",
"url": "https://glimind.com/mcp"
}
}
}
As of 2026-10-24 07:13 UTC, AI Design Blueprint Doctrine is up with a Glimind reliability score of 97/100 (grade A). Over the last 30 days it answered 100% of Glimind's liveness checks. It had 14 outages in 90 days, with a median recovery of 3 minutes.
AI Design Blueprint Doctrine advertises 29 tools, including architect.certify, architect.validate, architect.validate_consensus, assets.list, clusters.get.
Point your MCP client at this streamable_http endpoint: https://aidesignblueprint.com/mcp
Glimind scanned all 29 tool definitions for tool-poisoning patterns — hidden or invisible characters, instructions that override the model, concealment from the user, requests to read credential or MCP config files, conversation exfiltration and cross-tool shadowing — and found none. This checks what the server tells the model, not what its code does; review the source before granting access to sensitive data.
Status page: 90-day uptime, every outage and how long it lasted.
Claim this listing to get the Verified operator marker, a remediation checklist and owner analytics, or embed the live badge from any of the tool pages above. Maintainers: alerts, deploy attestation and monthly reports.
Status is an automated estimate from Glimind's liveness checks (handshake and tool listing only; no tool is ever called) and may lag or differ from your network. Glimind is not affiliated with this server or its operator. Operator? Request a correction or opt out at https://glimind.com/opt-out.