AI agents invoke share_screen to trigger actions in Joinly. 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.
Screen sharing triggers an external operation that broadcasts the agent's screen content to all meeting participants in real time. Based on the server context (video call participation tools) and the tool name, this triggers a live action with meaningful blast radius — sensitive information could be exposed to all participants.
From the tool's definition Tool name 'share_screen' on a server that enables AI agents to actively participate in video calls; description is empty and uninformative.
Documented attack patterns abuse exactly the kind of access share_screen gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Joinly, and nothing reaches the server without passing your rules. This is the rule we recommend for share_screen:
{
"version": "1",
"default": "deny",
"tools": {
"share_screen": {
"limits": [
{
"counter": "share_screen_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}share_screen 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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share_screen. It is categorised as a Execute tool in the Joinly MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Joinly MCP server in PolicyLayer and add a rule for share_screen: 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 Joinly. Nothing to install.
share_screen 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 share_screen 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 share_screen. 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.
share_screen is provided by the Joinly MCP server (joinly-ai/joinly). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Joinly, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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12 Joinly tools catalogued and risk-classified — across an index of 43,000+ MCP servers.