Start recording user actions to auto-generate a playbook. Do the task manually while recording, then call recording_stop to save.
AI agents invoke recording_start 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.
recording_start initiates external system operations (screen/input recording via Accessibility APIs) whose consequences depend on arguments (what user does while recording) and downstream actions (playbook generation and execution). This matches Execute: triggers external operations with argument-dependent effects.
From the tool's definition 'Start recording user actions' indicates the tool initiates capture of user interactions on native desktop and browser controls.
Documented attack patterns abuse exactly the kind of access recording_start 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 recording_start:
{
"version": "1",
"default": "deny",
"tools": {
"recording_start": {
"limits": [
{
"counter": "recording_start_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}recording_start 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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Start recording user actions to auto-generate a playbook. Do the task manually while recording, then call recording_stop to save. 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 recording_start: 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.
recording_start 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 recording_start 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 recording_start. 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.
recording_start 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.
Free to start. No card required.
89 ScreenHand tools catalogued and risk-classified — across an index of 43,000+ MCP servers.