AI agents use feed_pet 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 about a virtual pet's state in a reversible manner. Feeding is a Write operation as it updates the pet's internal state without destructive consequences. The action is benign, affecting only a local game object with no external side effects, resulting in low severity.
From the tool's definition Tool name 'feed_pet' and description 'Feed your virtual pet' indicate a state modification action that changes the pet's attributes (hunger level, health, etc.)
Documented attack patterns abuse exactly the kind of access feed_pet 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 feed_pet:
{
"version": "1",
"default": "deny",
"tools": {
"feed_pet": {
"limits": [
{
"counter": "feed_pet_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}feed_pet 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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Feed your virtual pet. 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 feed_pet: 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.
feed_pet 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 feed_pet 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 feed_pet. 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.
feed_pet 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.