Query /screen and return hydrated ranked rows with consensus, movement, and freshness metadata for any market. This is the primary tool for getting tiered, ranked plays. Generic market names like Total or Spread are auto-resolved per league — e.g. NHL Total becomes Total Goals, MLB Spread becomes...
AI agents call screen_ranked 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.
The tool queries and retrieves ranked data rows with metadata. It performs a read/fetch operation against a screening endpoint, returning analytical information about markets. There are no side effects, writes, executions, or financial transactions involved.
From the tool's definition Query /screen and return hydrated ranked rows with consensus, movement, and freshness metadata for any market
Attacks that exploit this kind of access
Query /screen and return hydrated ranked rows with consensus, movement, and freshness metadata for any market. This is the primary tool for getting tiered, ranked plays. Generic market names like Total or Spread are auto-resolved per league — e.g. NHL Total becomes Total Goals, MLB Spread becomes Run Line. League-specific defaults: Soccer → Draw No Bet / Match Handicap / Total Goals (NOT Moneyline/Spread/Total). Tennis → Game Handicap / Set Handicap / Total Games. UFC → Moneyline / Method of Victory. NFL → Spread / Total / Moneyline. Each row includes. 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 screen_ranked: 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.
screen_ranked 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 screen_ranked 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 screen_ranked. 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.
screen_ranked 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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