Cross-category impact analysis — how a topic affects multiple sectors and asset classes. WHEN TO USE: For understanding second-order effects of events. E.g., how a Fed rate decision impacts tech stocks, bonds, crypto, and real estate simultaneously. RETURNS: Impact scores across categories, affec...
AI agents call veroq_intelligence to retrieve information from Veroq without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and analyzes financial data to understand relationships and impacts across sectors and asset classes. It retrieves information and produces analytical reports but does not execute trades, move money, modify data, or trigger irreversible actions. The output is intelligence for decision-making, not an action that commits financial or operational consequences.
From the tool's definition Tool performs 'cross-category impact analysis' and 'returns impact scores across categories, affected tickers, transmission channels, and risk assessment' — purely informational retrieval and analysis of market data with no modification, deletion, or…
Attacks that exploit this kind of access
Cross-category impact analysis — how a topic affects multiple sectors and asset classes. WHEN TO USE: For understanding second-order effects of events. E.g., how a Fed rate decision impacts tech stocks, bonds, crypto, and real estate simultaneously. RETURNS: Impact scores across categories, affected tickers, transmission channels, and risk assessment. COST: 5 credits. EXAMPLE: {. It is categorised as a Read tool in the Veroq MCP Server, which means it retrieves data without modifying state.
Register the Veroq MCP server in PolicyLayer and add a rule for veroq_intelligence: 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 Veroq. Nothing to install.
veroq_intelligence 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 veroq_intelligence 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 veroq_intelligence. 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.
veroq_intelligence is provided by the Veroq MCP server (veroq-ai/veroq-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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