Remove single-dimensional entries
Part of the Pypi:mcp Numpy server.
Free to start. No card required.
AI agents may call np_squeeze to permanently remove or destroy resources in Pypi:mcp Numpy. Without a policy, an autonomous agent could delete critical data in a loop with no way to undo the damage. PolicyLayer blocks destructive tools by default and requires explicit human approval before enabling them.
Without a policy, an AI agent could call np_squeeze in a loop, permanently destroying resources in Pypi:mcp Numpy. There is no undo for destructive operations. PolicyLayer blocks this tool by default and only allows it when a human explicitly approves the action.
Destructive tools permanently remove data. Block by default. Only enable with explicit approval workflows.
{
"version": "1",
"default": "deny",
"hide": [
"np_squeeze"
]
}See the full Pypi:mcp Numpy policy for all 29 tools.
These attack patterns abuse exactly the kind of access np_squeeze gives an agent. Each links to the full case and the policy that stops it:
Other destructive tools across the catalogue. The same approach applies to each: deny by default, or require human approval.
Remove single-dimensional entries. It is categorised as a Destructive tool in the Pypi:mcp Numpy MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Pypi:mcp Numpy MCP server in PolicyLayer and add a rule for np_squeeze: 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 Pypi:mcp Numpy. Nothing to install.
np_squeeze 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 np_squeeze 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 np_squeeze. 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.
np_squeeze is provided by the Pypi:mcp Numpy MCP server (pypi:mcp-numpy). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 29 Pypi:mcp Numpy tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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