AI agents invoke mute_yourself 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.
With an empty description, classification relies on the tool name and server context. 'mute_yourself' implies toggling/changing the audio mute state of the AI agent in an active meeting — an external operation that affects real-time meeting participation. It is not purely read, and while reversible (unmute could restore), the act of muting triggers a live external side-effect, placing it in Execute.
From the tool's definition Tool name: 'mute_yourself'; description is empty. Inferred from context of sibling tools (join_meeting, leave_meeting, speak_text, send_chat_message) that this tool triggers an external operation on an active video call session.
Documented attack patterns abuse exactly the kind of access mute_yourself 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 mute_yourself:
{
"version": "1",
"default": "deny",
"tools": {
"mute_yourself": {
"limits": [
{
"counter": "mute_yourself_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}mute_yourself 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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mute_yourself. 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 mute_yourself: 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.
mute_yourself 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 mute_yourself 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 mute_yourself. 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.
mute_yourself 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.