Pro/Teams — return the authenticated user's architect.validate run history with the Blueprint Readiness Score (0-100), letter grade (A-F), and tier (draft, emerging, production_ready). Three lookup modes: (1) run_id=<id> returns a SINGLE run with the full persisted result_json — use this to RECOV...
Part of the AI Design Blueprint server.
Free to start. No card required.
AI agents invoke me.validation_history to trigger processes or run actions in AI Design Blueprint. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
me.validation_history can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"me.validation_history": {
"limits": [
{
"counter": "me.validation_history_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full AI Design Blueprint policy for all 24 tools.
These attack patterns abuse exactly the kind of access me.validation_history gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Pro/Teams — return the authenticated user's architect.validate run history with the Blueprint Readiness Score (0-100), letter grade (A-F), and tier (draft, emerging, production_ready). Three lookup modes: (1) run_id=<id> returns a SINGLE run with the full persisted result_json — use this to RECOVER a result when your MCP client tool-call timed out before architect.validate returned. The run completes server-side and persists; the run_id is surfaced in the first progress notification of every architect.validate call so you have the recovery handle even when your client gives up early. (2) repository=<name> returns the full per-run trend for that repository plus a regression diff between the latest two runs. (3) No arguments returns one summary per repository the user has validated, sorted by most recent. Use modes (2) or (3) BEFORE calling architect.validate again on the same repository — they tell you which principles regressed since the last run, so you can focus the new review on what is actually changing. Auth: Bearer <token>. Pro or Teams plan required.. It is categorised as a Execute tool in the AI Design Blueprint MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the AI Design Blueprint MCP server in PolicyLayer and add a rule for me.validation_history: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches AI Design Blueprint. Nothing to install.
me.validation_history is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the me.validation_history rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for me.validation_history. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
me.validation_history is provided by the AI Design Blueprint MCP server (https://aidesignblueprint.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 24 AI Design Blueprint tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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