Check the job queue: active job, pending queue, completed and failed job history.
AI agents call flow_queue_status to retrieve information from Google Flow Browser MCP without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool performs read-only status checking of job queue information. It retrieves historical and current state data with no side effects—no creation, modification, deletion, or execution of operations. The blast radius of misuse is minimal; an attacker gaining access could observe queue metadata but cannot alter workflows, generate content, or cause harm beyond information disclosure.
From the tool's definition Tool name 'flow_queue_status' and description 'Check the job queue' indicate a query/monitoring operation that retrieves status information about jobs (active, pending, completed, failed) without modifying any data.
Documented attack patterns abuse exactly the kind of access flow_queue_status gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Google Flow Browser MCP, and nothing reaches the server without passing your rules. This is the rule we recommend for flow_queue_status:
{
"version": "1",
"default": "deny",
"tools": {
"flow_queue_status": {}
}
}flow_queue_status is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Check the job queue: active job, pending queue, completed and failed job history. It is categorised as a Read tool in the Google Flow Browser MCP MCP Server, which means it retrieves data without modifying state.
Register the Google Flow Browser MCP server in PolicyLayer and add a rule for flow_queue_status: 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 Google Flow Browser MCP. Nothing to install.
flow_queue_status 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 flow_queue_status 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 flow_queue_status. 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.
flow_queue_status is provided by the Google Flow Browser MCP server (tmsss05/google-flow-browser-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Google Flow Browser MCP, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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17 Google Flow Browser MCP tools catalogued and risk-classified — across an index of 43,000+ MCP servers.