AI agents invoke enforced_tool_call 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 implies executing an external tool call under policy governance. Given the server context (AI agent governance, policy enforcement, multi-party authorization), this tool likely triggers or proxies tool executions on behalf of AI agents. Without a description, confidence is reduced, but the 'call' suffix and sibling tools like 'gate_action' and 'complete_action' suggest an Execute classification.
From the tool's definition Tool name 'enforced_tool_call' suggests executing a tool call with policy enforcement; description is empty and uninformative.
Documented attack patterns abuse exactly the kind of access enforced_tool_call 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 enforced_tool_call:
{
"version": "1",
"default": "deny",
"tools": {
"enforced_tool_call": {
"limits": [
{
"counter": "enforced_tool_call_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}enforced_tool_call 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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enforced_tool_call. 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 enforced_tool_call: 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.
enforced_tool_call 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 enforced_tool_call 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 enforced_tool_call. 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.
enforced_tool_call 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.