Manages Terraform workspaces: list, select, create, or delete workspaces.
AI agents call workspace to permanently remove resources in Python — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
While the tool also provides read-like operations (list, select) and write-like operations (create), the inclusion of 'delete' makes this Destructive. Deleting a Terraform workspace can result in loss of state management and inability to track or manage infrastructure that was provisioned under that workspace.
From the tool's definition Tool description explicitly includes 'delete workspaces' as a capability. Workspace deletion in Terraform irreversibly removes infrastructure state and configuration context.
Documented attack patterns abuse exactly the kind of access workspace gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Python, and nothing reaches the server without passing your rules. This is the rule we recommend for workspace:
{
"version": "1",
"default": "deny",
"hide": [
"workspace"
]
} workspace 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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Manages Terraform workspaces: list, select, create, or delete workspaces. It is categorised as a Destructive tool in the Python MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Python MCP server in PolicyLayer and add a rule for workspace: 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 Python. Nothing to install.
workspace 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 workspace 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 workspace. 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.
workspace is provided by the Python MCP server (Dave-London/Pare). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Python, 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.
202 Python tools catalogued and risk-classified — across an index of 43,000+ MCP servers.