Get recent news, tweets, and a computed risk flag for a player. Returns up to 30 recent tweets mentioning the player from X plus a Google News RSS layer (with ESPN as tertiary fallback). Each item is scored 0-100 for source authority. USE THIS BEFORE PLACING A BET: if riskFlag ===
AI agents call player_context 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 only reads and aggregates publicly available information (news, tweets, risk scores) without modifying any data or triggering external actions. The instruction 'USE THIS BEFORE PLACING A BET' is advisory guidance to the user, not an action the tool itself performs.
From the tool's definition Tool performs data retrieval: 'Get recent news, tweets, and a computed risk flag for a player. Returns up to 30 recent tweets...plus a Google News RSS layer'.
Attacks that exploit this kind of access
Get recent news, tweets, and a computed risk flag for a player. Returns up to 30 recent tweets mentioning the player from X plus a Google News RSS layer (with ESPN as tertiary fallback). Each item is scored 0-100 for source authority. USE THIS BEFORE PLACING A BET: if riskFlag ===. 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 player_context: 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.
player_context 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 player_context 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 player_context. 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.
player_context 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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