splitifi_predict_game_theory
Predict Game Theory / Strategy case outcome using ML models trained on 3.52B+ court records. Provide jurisdiction and case_facts (dispute_type, parties, payoff_structure, negotiation_rounds, reservation_values).
This record as markdown: /tools/io-github-mysplitifi-splitifi-mcp/splitifi-predict-game-theory.md
What splitifi_predict_game_theory does on Splitifi Intelligence MCP
AI agents call splitifi_predict_game_theory to retrieve information from Splitifi Intelligence MCP without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
judge_id | string | — | Optional — judge ID for judge-specific modifier |
case_facts | object | Yes | Case details for Game Theory / Strategy. Key fields: dispute_type, parties, payoff_structure, negotiation_rounds, reservation_values |
jurisdiction | string | Yes | State/province/country code, e.g. "TX", "CA", "ON" |
Parameters from the server's own tool schema.
Why splitifi_predict_game_theory is rated Low
This tool retrieves and analyzes data from trained ML models to generate outcome predictions—a read-only operation with no side effects on external systems or data stores.
From the tool's definition Tool performs prediction/analysis ('Predict Game Theory / Strategy case outcome using ML models') with inputs limited to factual case parameters (jurisdiction, dispute_type, parties, payoff_structure, negotiation_rounds, reservation_values).
Attacks that exploit this kind of access
The rule that runs splitifi_predict_game_theory safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Splitifi Intelligence MCP, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For splitifi_predict_game_theory, this is the rule to start with:
splitifi_predict_game_theory is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Splitifi Intelligence MCP, apply this rule, and every splitifi_predict_game_theory call is checked against it from then on.
Questions about splitifi_predict_game_theory
Predict Game Theory / Strategy case outcome using ML models trained on 3.52B+ court records. Provide jurisdiction and case_facts (dispute_type, parties, payoff_structure, negotiation_rounds, reservation_values). It is categorised as a Read tool in the Splitifi Intelligence MCP MCP Server, which means it retrieves data without modifying state.
splitifi_predict_game_theory accepts 3 parameters: judge_id, case_facts, jurisdiction. Required: case_facts, jurisdiction. The full parameter table on this page comes from the server's own tool schema.
Register the Splitifi Intelligence MCP server in PolicyLayer and add a rule for splitifi_predict_game_theory: 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 Splitifi Intelligence MCP. Nothing to install.
splitifi_predict_game_theory 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 splitifi_predict_game_theory 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 splitifi_predict_game_theory. 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.
splitifi_predict_game_theory is provided by the Splitifi Intelligence MCP server (splitifi-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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