splitifi_predict_personal_injury
Predict Personal Injury case outcome using ML models trained on 3.52B+ court records. Provide jurisdiction and case_facts (injury_type, liability_clarity, medical_costs, lost_wages, insurance_limit, fault_percentage).
This record as markdown: /tools/io-github-mysplitifi-splitifi-mcp/splitifi-predict-personal-injury.md
What splitifi_predict_personal_injury does on Splitifi Intelligence MCP
AI agents call splitifi_predict_personal_injury 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 Personal Injury. Key fields: injury_type, liability_clarity, medical_costs, lost_wages, insurance_limit, fault_percentage |
jurisdiction | string | Yes | State/province/country code, e.g. "TX", "CA", "ON" |
Parameters from the server's own tool schema.
Why splitifi_predict_personal_injury is rated Low
This is a query/analysis tool that accepts case parameters and returns outcome predictions from pre-trained models. It has no side effects on data or systems—it only reads from the model and returns analysis. While the predictions could inform financial decisions in personal injury litigation, the tool itself does not move money, execute external operations, or modify any records.
From the tool's definition Tool description states it 'Predict[s]' outcomes using ML models trained on court records. The inputs are passive case facts (injury_type, liability_clarity, medical_costs, lost_wages, insurance_limit, fault_percentage) provided by the user.
Attacks that exploit this kind of access
The rule that runs splitifi_predict_personal_injury 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_personal_injury, this is the rule to start with:
splitifi_predict_personal_injury 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_personal_injury call is checked against it from then on.
Questions about splitifi_predict_personal_injury
Predict Personal Injury case outcome using ML models trained on 3.52B+ court records. Provide jurisdiction and case_facts (injury_type, liability_clarity, medical_costs, lost_wages, insurance_limit, fault_percentage). It is categorised as a Read tool in the Splitifi Intelligence MCP MCP Server, which means it retrieves data without modifying state.
splitifi_predict_personal_injury 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_personal_injury: 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_personal_injury 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_personal_injury 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_personal_injury. 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_personal_injury 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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