Macro recorder: start/stop/trim/clean recorded playbooks. Use
AI agents invoke playbook_record to trigger actions in ScreenHand. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool enables recording and executing sequences of desktop actions (macros/playbooks). When executed, these playbooks can automate arbitrary workflows, application interactions, and UI actions across the system. While the playbook content determines specific outcomes, the tool itself triggers execution of recorded sequences, making it Execute rather than Write.
From the tool's definition Tool is a 'macro recorder' that 'start/stop/trim/clean recorded playbooks' with native desktop control capabilities. The ability to record and execute macros constitutes automation of external operations whose effects depend on arguments and context.
Documented attack patterns abuse exactly the kind of access playbook_record gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and ScreenHand, and nothing reaches the server without passing your rules. This is the rule we recommend for playbook_record:
{
"version": "1",
"default": "deny",
"tools": {
"playbook_record": {
"limits": [
{
"counter": "playbook_record_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}playbook_record stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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Macro recorder: start/stop/trim/clean recorded playbooks. Use. It is categorised as a Execute tool in the ScreenHand MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the ScreenHand MCP server in PolicyLayer and add a rule for playbook_record: 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 ScreenHand. Nothing to install.
playbook_record is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the playbook_record 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 playbook_record. 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.
playbook_record is provided by the ScreenHand MCP server (manushi4/screenhand). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from ScreenHand, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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89 ScreenHand tools catalogued and risk-classified — across an index of 43,000+ MCP servers.