Portfolio risk analysis computing Value-at-Risk (VaR) and Conditional VaR (Expected Shortfall) using the variance-covariance (delta-normal) method. Given portfolio weights and historical asset returns, quantifies potential losses at a given confidence level. Use for risk management, portfolio con...
AI agents call analyze_portfolio_risk to retrieve information from Oraclaw without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool reads/analyzes input data (portfolio weights and historical returns) and outputs risk metrics (VaR, CVaR). It does not move money, place trades, or modify any financial positions. It is a pure analytical/read operation. Severity is low because misuse at most produces incorrect risk estimates; it does not directly cause financial harm.
From the tool's definition 'Portfolio risk analysis computing Value-at-Risk (VaR) and Conditional VaR' and 'quantifies potential losses' — the tool performs statistical computation and analysis on provided data, producing metrics without executing trades or moving money.
Documented attack patterns abuse exactly the kind of access analyze_portfolio_risk gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Oraclaw, and nothing reaches the server without passing your rules. This is the rule we recommend for analyze_portfolio_risk:
{
"version": "1",
"default": "deny",
"tools": {
"analyze_portfolio_risk": {}
}
}analyze_portfolio_risk is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
Free to start. No card required.
Portfolio risk analysis computing Value-at-Risk (VaR) and Conditional VaR (Expected Shortfall) using the variance-covariance (delta-normal) method. Given portfolio weights and historical asset returns, quantifies potential losses at a given confidence level. Use for risk management, portfolio construction, regulatory capital, or stress testing. It is categorised as a Read tool in the Oraclaw MCP Server, which means it retrieves data without modifying state.
Register the Oraclaw MCP server in PolicyLayer and add a rule for analyze_portfolio_risk: 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 Oraclaw. Nothing to install.
analyze_portfolio_risk 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 analyze_portfolio_risk 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 analyze_portfolio_risk. 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.
analyze_portfolio_risk is provided by the Oraclaw MCP server (@oraclaw/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Oraclaw, 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.
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