One-call bet evaluation: given a player/team and book, returns the play details, validate_play verdict (movementDisposition, riskFlags, actionableSummary), best price across books, and staking recommendation. Equivalent to quick_screen + validate_play + find_best_price + staking_plan in one call....
AI agents use smart_bet to commit financial operations through PropProfessor MCP — usually the final step of a payment, billing, or trading workflow. A call moves real money.
The tool is explicitly designed to evaluate bets and produce staking recommendations, which constitutes advising on financial commitments. While it may not directly execute a transaction, it consolidates bet validation, price discovery, and stake sizing into a single recommendation pipeline. Misuse by an AI agent could lead to real financial decisions being made based on its output.
From the tool's definition 'staking recommendation', 'bet evaluation', 'validate_play verdict', 'actionableSummary' — the tool evaluates bets and provides staking plans, directly advising financial commitments on sports wagers
Attacks that exploit this kind of access
One-call bet evaluation: given a player/team and book, returns the play details, validate_play verdict (movementDisposition, riskFlags, actionableSummary), best price across books, and staking recommendation. Equivalent to quick_screen + validate_play + find_best_price + staking_plan in one call. Use when the user asks. It is categorised as a Financial tool in the PropProfessor MCP MCP Server, which means it involves financial transactions. Block by default and require explicit approval.
Register the PropProfessor MCP server in PolicyLayer and add a rule for smart_bet: 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 PropProfessor MCP. Nothing to install.
smart_bet is a Financial tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the smart_bet 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 smart_bet. 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.
smart_bet is provided by the PropProfessor MCP server (j17drake/propprofessor-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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