Execute a saved playbook by ID or auto-match by task description. Playbooks run deterministically without AI calls. If a step fails, AI automatically recovers and patches the playbook for next time.
AI agents invoke playbook_run 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 executes saved playbook sequences that can perform arbitrary desktop automation actions (app control, browser interaction, UI manipulation) determined by the playbook content.
From the tool's definition "Execute a saved playbook by ID" - runs automation workflows; "Playbooks run deterministically without AI calls" - executes pre-defined sequences; context shows this server controls native desktop applications, browser sessions, and can automate workflows via…
Documented attack patterns abuse exactly the kind of access playbook_run 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_run:
{
"version": "1",
"default": "deny",
"tools": {
"playbook_run": {
"limits": [
{
"counter": "playbook_run_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}playbook_run 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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Execute a saved playbook by ID or auto-match by task description. Playbooks run deterministically without AI calls. If a step fails, AI automatically recovers and patches the playbook for next time. 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_run: 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_run 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_run 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_run. 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_run 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.