Extract structured playbook steps from a video transcript (e.g. YouTube captions). Converts tutorial narration into actionable automation steps with tool mappings.
AI agents call ingest_tutorial to retrieve information from ScreenHand without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool processes input data (video transcripts) and produces structured output (automation steps). It reads and parses content but does not execute actions, modify data on external systems, delete anything, or commit financial transactions.
From the tool's definition The tool 'ingest_tutorial' is described as extracting and converting video transcript content into structured steps.
Documented attack patterns abuse exactly the kind of access ingest_tutorial 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 ingest_tutorial:
{
"version": "1",
"default": "deny",
"tools": {
"ingest_tutorial": {}
}
}ingest_tutorial is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Extract structured playbook steps from a video transcript (e.g. YouTube captions). Converts tutorial narration into actionable automation steps with tool mappings. It is categorised as a Read tool in the ScreenHand MCP Server, which means it retrieves data without modifying state.
Register the ScreenHand MCP server in PolicyLayer and add a rule for ingest_tutorial: 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.
ingest_tutorial 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 ingest_tutorial 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 ingest_tutorial. 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.
ingest_tutorial 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.