Run a queueing simulation against an arbitrary 24-hour staffing schedule. Take this when the user describes a custom day shape that doesn't match a preset (e.g., 'my coffee shop is open 6am–10pm with 4 baristas off-peak, 7 at the 8am rush, 5 at the 4pm rush'). Inputs: arrivalRates (24-element arr...
Part of the QueueSim server.
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AI agents use simulate_schedule to create or modify resources in QueueSim. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call simulate_schedule repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach QueueSim.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"simulate_schedule": {
"limits": [
{
"counter": "simulate_schedule_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full QueueSim policy for all 11 tools.
These attack patterns abuse exactly the kind of access simulate_schedule gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Run a queueing simulation against an arbitrary 24-hour staffing schedule. Take this when the user describes a custom day shape that doesn't match a preset (e.g., 'my coffee shop is open 6am–10pm with 4 baristas off-peak, 7 at the 8am rush, 5 at the 4pm rush'). Inputs: arrivalRates (24-element array of customers/hr per hour-of-day) and staffing (24-element array of servers/hr); optional uniform serviceTimeMinutes. Use 0 in both arrays for closed hours (terminating system). Returns the same per-hour metrics + summary shape as simulate_mmc / simulate_scenario. Stronger fit than simulate_scenario when the user's shape doesn't match the four presets; stronger fit than simulate_mmc when they need per-hour variation. ANTI-FABRICATION: numbers come from a real DES run. Quote them VERBATIM. Do not round, estimate, or derive from training-data recall.. It is categorised as a Write tool in the QueueSim MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the QueueSim MCP server in PolicyLayer and add a rule for simulate_schedule: 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 QueueSim. Nothing to install.
simulate_schedule is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the simulate_schedule 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 simulate_schedule. 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.
simulate_schedule is provided by the QueueSim MCP server (https://queuesim.com/mcp/v1). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 11 QueueSim tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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