AI agents call memory_prune to permanently remove resources in Rekal — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
'Prune' operations typically remove data irreversibly, and given the sibling tool 'memory_delete' exists on the same server, 'memory_prune' likely performs bulk or automatic removal of stale/old memories — an irreversible destructive action. Confidence is reduced due to the empty description, but the naming pattern and server context support a Destructive classification.
From the tool's definition Tool name 'memory_prune' — 'prune' strongly implies removal/deletion of data; description is empty and uninformative.
Documented attack patterns abuse exactly the kind of access memory_prune gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Rekal, and nothing reaches the server without passing your rules. This is the rule we recommend for memory_prune:
{
"version": "1",
"default": "deny",
"hide": [
"memory_prune"
]
}memory_prune disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.
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memory_prune. It is categorised as a Destructive tool in the Rekal MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Rekal MCP server in PolicyLayer and add a rule for memory_prune: 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 Rekal. Nothing to install.
memory_prune is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the memory_prune 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 memory_prune. 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.
memory_prune is provided by the Rekal MCP server (janbjorge/rekal). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Rekal, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
21 Rekal tools catalogued and risk-classified — across an index of 43,000+ MCP servers.