AI agents invoke wait_for_training_tool to trigger actions in Qiskit MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
wait_for_training_tool triggers real processes with real consequences. An agent gone sideways doesn't fire it once — it starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.
Documented attack patterns abuse exactly the kind of access wait_for_training_tool gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Qiskit MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for wait_for_training_tool:
{
"version": "1",
"default": "deny",
"tools": {
"wait_for_training_tool": {
"limits": [
{
"counter": "wait_for_training_tool_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}wait_for_training_tool stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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wait_for_training_tool. It is categorised as a Execute tool in the Qiskit MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Qiskit MCP Server MCP server in PolicyLayer and add a rule for wait_for_training_tool: 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 Qiskit MCP Server. Nothing to install.
wait_for_training_tool 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 wait_for_training_tool 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 wait_for_training_tool. 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.
wait_for_training_tool is provided by the Qiskit MCP Server MCP server (qiskit-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 71 Qiskit MCP Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
71 Qiskit MCP Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.