AI agents invoke gate_action to trigger actions in Pypi:asqav. 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.
The name 'gate_action' on an AI governance server suggests this tool controls whether an action is allowed to proceed — likely triggering authorization checks or enforcement decisions that affect downstream operations. In context with sibling tools like 'enforced_tool_call', 'preflight_check', and 'complete_action', it likely executes a gating/authorization step that may trigger external operations.
From the tool's definition Tool name 'gate_action' on a server described as providing 'policy enforcement, multi-party authorization' for AI agents. Description is empty.
Documented attack patterns abuse exactly the kind of access gate_action gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Pypi:asqav, and nothing reaches the server without passing your rules. This is the rule we recommend for gate_action:
{
"version": "1",
"default": "deny",
"tools": {
"gate_action": {
"limits": [
{
"counter": "gate_action_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}gate_action 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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gate_action. It is categorised as a Execute tool in the Pypi:asqav MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Pypi:asqav MCP server in PolicyLayer and add a rule for gate_action: 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 Pypi:asqav. Nothing to install.
gate_action 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 gate_action 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 gate_action. 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.
gate_action is provided by the Pypi:asqav MCP server (jagmarques/asqav-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Pypi:asqav, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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15 Pypi:asqav tools catalogued and risk-classified — across an index of 43,000+ MCP servers.