AI agents call await_block_done to retrieve information from Pl without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool monitors the state of a computation block and waits until it completes. It does not modify, delete, or create any data; it only observes the system state. The worst misuse would be waiting indefinitely (causing a hang), but there is no data impact.
From the tool's definition 'Wait for a block to finish computation and outputs to stabilize' — purely a waiting/polling operation with no side effects
Attacks that exploit this kind of access
Wait for a block to finish computation and outputs to stabilize. It is categorised as a Read tool in the Pl MCP Server, which means it retrieves data without modifying state.
Register the Pl MCP server in PolicyLayer and add a rule for await_block_done: 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.
await_block_done is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the await_block_done 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 await_block_done. 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.
await_block_done 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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