Put your virtual pet to sleep to restore energy
AI agents use put_to_bed to create or update resources in MCPet — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your MCPet environment.
This tool creates or modifies data (the pet's sleep state and energy attribute) in a reversible manner. It is not destructive (the pet still exists), not financial, not a read-only query, and not code execution. It fits the Write category as a state modification. Severity is low because the only blast radius is a single pet's virtual state with no external consequences or data loss.
From the tool's definition Tool modifies pet state by putting it to sleep, which changes the pet's energy level and status. The description 'Put your virtual pet to sleep to restore energy' indicates a state-changing operation on the pet object.
Documented attack patterns abuse exactly the kind of access put_to_bed gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and MCPet, and nothing reaches the server without passing your rules. This is the rule we recommend for put_to_bed:
{
"version": "1",
"default": "deny",
"tools": {
"put_to_bed": {
"limits": [
{
"counter": "put_to_bed_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}put_to_bed stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
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Put your virtual pet to sleep to restore energy. It is categorised as a Write tool in the MCPet MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the MCPet MCP server in PolicyLayer and add a rule for put_to_bed: 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 MCPet. Nothing to install.
put_to_bed 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 put_to_bed 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 put_to_bed. 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.
put_to_bed is provided by the MCPet MCP server (shreyaskarnik/mcpet). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from MCPet, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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6 MCPet tools catalogued and risk-classified — across an index of 43,000+ MCP servers.