The fastest way to find playable bets on ANY book in one call. Specifies a target book (or books), scans all leagues × markets for sharp plays with independent consensus confirmation, runs player context research on candidates, and returns ranked results with risk flags. The target book price doe...
AI agents call quick_screen to retrieve information from PropProfessor MCP without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
quick_screen performs market scanning and analysis to identify betting opportunities, then returns filtered and ranked results to the user. This is purely informational data retrieval with no side effects. While the context is financial (betting/sports analytics), the tool itself only reads and synthesizes existing data; it does not execute trades, place bets, or move money.
From the tool's definition Tool description indicates it 'scans', 'finds', and 'returns ranked results' — all read operations. The workflow involves querying data across books and leagues, researching player context, and presenting information.
Attacks that exploit this kind of access
The fastest way to find playable bets on ANY book in one call. Specifies a target book (or books), scans all leagues × markets for sharp plays with independent consensus confirmation, runs player context research on candidates, and returns ranked results with risk flags. The target book price does not need to be the best — just playable (not. It is categorised as a Read tool in the PropProfessor MCP MCP Server, which means it retrieves data without modifying state.
Register the PropProfessor MCP server in PolicyLayer and add a rule for quick_screen: 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.
quick_screen 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 quick_screen 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 quick_screen. 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.
quick_screen 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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