Is AI Design Blueprint Doctrine down?

pulsemcp/ai-design-blueprint-doctrine · v1.26.0

Also listed as mcp-registry/com.aidesignblueprint/blueprint · primary listing: mcp-registry/com.aidesignblueprint/blueprint

healthy
Last checked 2026-10-24 07:13 UTC · handshake 649ms

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.

uptime (30d checks)
100%
checks observed
4,525
outages (90d)
14
tools
29

Agentic AI Blueprint doctrine and 13 public tools for learning and prototyping agent patterns.

Install AI Design Blueprint Doctrine

Add to Cursor Add to VS Code

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.

Agent-readiness: B (88/100)

B (88/100): 0 error(s), 2 warning(s) across 29 tool(s); ~12,906 tokens of tool definitions.
Scores higher than 64% of 15,626 MCP servers Glimind has audited.
Its tool list costs an agent ~12,906 tokens per session — more than 85% of servers.
85 → 88 since 2026-10-17
no tool-poisoning patterns
Schemas100
Descriptions100
Safety hints100
Token cost88
Client compatibility50
Protocol100
Reliability100
Stability40

What to fix (3)

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 →

Tools: architect.validate · saves ~656 tokens/session

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 →

Tools: architect.certify, architect.validate, architect.validate_consensus, assets.list, clusters.get and 24 more

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 →

Auto-fix: 29 corrected tool definitions

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

architect.certify 1 change
{
  "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"
}
architect.validate 1 change
{
  "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"
}
architect.validate_consensus 1 change
{
  "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 alerted

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Agents: watch_tool({"toolId": "pulsemcp/ai-design-blueprint-doctrine"}) over MCP.

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Operator? Fix these, then re-check live. Embed the readiness badge:

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Tool-definition log

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.

Tools (29)

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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"
    }
  }
}

FAQ

Is AI Design Blueprint Doctrine down right now?

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.

How many tools does AI Design Blueprint Doctrine provide?

AI Design Blueprint Doctrine advertises 29 tools, including architect.certify, architect.validate, architect.validate_consensus, assets.list, clusters.get.

How do I install AI Design Blueprint Doctrine?

Point your MCP client at this streamable_http endpoint: https://aidesignblueprint.com/mcp

Is AI Design Blueprint Doctrine safe to connect to an AI agent?

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.

Operator of AI Design Blueprint Doctrine?

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.

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