Combined agent-status + policy-allowed check; returns CLEARED / NOT CLEARED summary.
AI agents call preflight_check to retrieve information from Pypi:asqav without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This is a read-only governance check combining agent status and policy validation. It queries existing data (agent state, policy rules) to produce an authorization decision summary, with no side effects, data modification, code execution, or resource consumption. The return of a CLEARED/NOT CLEARED status is informational output used for downstream authorization decisions, not the decision enforcement itself.
From the tool's definition Tool name 'preflight_check' and description indicate a status/policy check operation that 'returns CLEARED / NOT CLEARED summary' — a query that retrieves and evaluates agent and policy state without modifying data or executing external operations.
Documented attack patterns abuse exactly the kind of access preflight_check 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 preflight_check:
{
"version": "1",
"default": "deny",
"tools": {
"preflight_check": {}
}
}preflight_check is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Combined agent-status + policy-allowed check; returns CLEARED / NOT CLEARED summary. It is categorised as a Read tool in the Pypi:asqav MCP Server, which means it retrieves data without modifying state.
Register the Pypi:asqav MCP server in PolicyLayer and add a rule for preflight_check: 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.
preflight_check is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the preflight_check 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 preflight_check. 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.
preflight_check 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.
Free to start. No card required.
15 Pypi:asqav tools catalogued and risk-classified — across an index of 43,000+ MCP servers.