Analyze line history across multiple time windows (1h, 2h, 6h, 12h, 24h, 48h) to detect sustained sharp book consensus movement. Returns plays ranked by how many windows show ALL sharp books moving supportive. Use when you want to understand WHY a play ranks, not just WHAT ranks —
AI agents call sharp_consensus 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.
This tool queries and analyzes existing betting market data (line history across time windows) to detect consensus movement patterns and rank plays. It is purely analytical with no side effects—it retrieves and processes information without modifying any data, executing external operations, or affecting financial transactions. The output is informational ranking of plays based on sharp book consensus patterns.
From the tool's definition Tool performs analysis and returns ranked plays based on line history data; uses verbs like 'Analyze' and 'Returns' with no mention of creating, modifying, executing commands, deleting data, or moving money.
Attacks that exploit this kind of access
Analyze line history across multiple time windows (1h, 2h, 6h, 12h, 24h, 48h) to detect sustained sharp book consensus movement. Returns plays ranked by how many windows show ALL sharp books moving supportive. Use when you want to understand WHY a play ranks, not just WHAT ranks —. 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 sharp_consensus: 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.
sharp_consensus 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 sharp_consensus 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 sharp_consensus. 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.
sharp_consensus 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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