Scrape official docs, help center, keyboard shortcuts for a platform. Crawls pages via Chrome and extracts structured data into a reference JSON.
AI agents call platform_learn 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 performs read-only operations by crawling web pages and extracting information into structured data. It retrieves and queries existing documentation without creating, modifying, deleting, or executing any operations that could affect system state or cause harm. The high confidence reflects the clear read-only nature of documentation scraping.
From the tool's definition The tool description states it will 'Scrape official docs, help center, keyboard shortcuts' and 'extracts structured data into a reference JSON'. The verbs 'scrape' and 'extract' indicate data retrieval operations.
Documented attack patterns abuse exactly the kind of access platform_learn 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 platform_learn:
{
"version": "1",
"default": "deny",
"tools": {
"platform_learn": {}
}
}platform_learn is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Scrape official docs, help center, keyboard shortcuts for a platform. Crawls pages via Chrome and extracts structured data into a reference JSON. 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 platform_learn: 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.
platform_learn 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 platform_learn 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 platform_learn. 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.
platform_learn 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.