AI agents invoke stop_sharing 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.
This tool executes a command that modifies the state of an active video call by stopping screen sharing. While not destructive (the action is reversible by re-sharing), it qualifies as Execute because it triggers an external operation with immediate effects on meeting participants. The empty description lowers confidence slightly, but the name and context with other meeting control tools clarifies intent.
From the tool's definition Tool name 'stop_sharing' in context of video call management; sibling tools include 'share_screen' and meeting control actions like 'join_meeting', 'leave_meeting', 'mute_yourself'.
Documented attack patterns abuse exactly the kind of access stop_sharing 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 stop_sharing:
{
"version": "1",
"default": "deny",
"tools": {
"stop_sharing": {
"limits": [
{
"counter": "stop_sharing_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}stop_sharing 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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stop_sharing. 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 stop_sharing: 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.
stop_sharing 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 stop_sharing 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 stop_sharing. 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.
stop_sharing 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.