CreditScoreImprovementPlan
The evaluation will assess the FinTech advisor system's ability to accurately analyze simulated financial data, provide personalized and relevant credit-building advice, and correctly utilize MCP tool integrations. Emphasis will be on the coherence of the LangGraph workflow and the actionable quality of the recommendations.
Evaluation overview
How the linked challenge is judged: tasks, benchmarks, and criteria count.
Task templates
Inputs and expected outputs.
CreditScoreImprovementPlan
Given simulated user financial data (income, expenses, debt, mock Esusu rent history), generate a personalized, actionable plan to improve their credit score.
{'user_profile': {'income': float, 'expenses': float, 'debt': float, 'credit_score': int}, 'rent_history': [{'month': 'YYYY-MM', 'paid_on_time': bool}]}
{'credit_plan_summary': 'string', 'action_items': [{'step': int, 'description': 'string', 'expected_impact': 'string'}], 'mcp_tool_calls': ['list of tool calls']}
FinancialDataAnalysis
Analyze simulated monthly financial transactions and rent history to identify patterns affecting credit and provide insights.
{'transactions': [{'date': 'YYYY-MM-DD', 'amount': float, 'category': 'string'}], 'rent_history': [{'month': 'YYYY-MM', 'paid_on_time': bool}]}
{'analysis_report': 'string', 'insights': ['string'], 'mcp_tool_calls': ['list of tool calls']}