AI agents use close_project to create or update resources in Pl — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Pl environment.
Closing a project changes application state and releases resources, which constitutes a write/modification operation. It is reversible (the project can be reopened), so it does not qualify as Destructive.
From the tool's definition The tool description states it 'Close an opened project, releasing its resources' - this modifies the state of an application by closing an open project, which is a reversible action (the project can be reopened).
Attacks that exploit this kind of access
Close an opened project, releasing its resources. It is categorised as a Write tool in the Pl MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Pl MCP server in PolicyLayer and add a rule for close_project: 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 Pl. Nothing to install.
close_project 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 close_project 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 close_project. 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.
close_project is provided by the Pl MCP server (@milaboratories/pl-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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