Poll the status of an RFQ. Returns the original request, all responses received so far, and booking details if booked.
Part of the Local Intel server.
Free to start. No card required.
AI agents call local_intel_rfq_status to retrieve information from Local Intel without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though local_intel_rfq_status only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"local_intel_rfq_status": {}
}
}See the full Local Intel policy for all 27 tools.
These attack patterns abuse exactly the kind of access local_intel_rfq_status gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Poll the status of an RFQ. Returns the original request, all responses received so far, and booking details if booked.. It is categorised as a Read tool in the Local Intel MCP Server, which means it retrieves data without modifying state.
Register the Local Intel MCP server in PolicyLayer and add a rule for local_intel_rfq_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 Local Intel. Nothing to install.
local_intel_rfq_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 local_intel_rfq_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 local_intel_rfq_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.
local_intel_rfq_status is provided by the Local Intel MCP server (https://gsb-swarm-production.up.railway.app/api/local-intel/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 27 Local Intel tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.