MCP-Enabled AI Venture Scout
Develop an advanced AI agent system designed to act as a 'Venture Scout' for investment firms, specifically targeting the challenge of investor wariness towards unproven AI businesses. This system will leverage Gemini 3 Pro's multimodal reasoning capabilities within a structured LangGraph workflow to analyze startup business plans, market potential, and technical viability. The goal is to provide a comprehensive risk assessment and strategic feedback, enabling investors to make informed decisions and helping promising smaller AI companies articulate their value proposition more effectively.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a graph-based multi-agent system using LangGraph with Gemini 3 Pro for autonomous risk assessment and strategic feedback for AI startups, featuring MCP tool integration.
How work is evaluated
The evaluation will assess the system's ability to accurately analyze AI startup profiles, generate comprehensive risk assessments, and provide actionable feedback. It will be judged on the quality of its reasoning, the accuracy of its financial and technical insights, and the clarity of its strategic recommendations.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Design MCP tool integration for real-time market data and financial report analysis.
Build graph-based agents for structured financial and technical analysis.
Orchestrate complex workflows for iterative business plan evaluation.
Implement hybrid reasoning with Gemini 2.5 Pro for nuanced risk assessment.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The evaluation will assess the system's ability to accurately analyze AI startup profiles, generate comprehensive risk assessments, and provide actionable feedback. It will be judged on the quality of its reasoning, t...
Challenge input
{'startup_name': 'str', 'business_plan_summary': 'str', 'financial_projections': 'json', 'technical_overview': 'str', 'market_data_query': 'str'}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'risk_assessment': {'overall_score': 'int', 'financial_risk': 'float', 'technical_risk': 'float', 'market_risk': 'float'}, 'swot_analysis': {'stre...
- Ensure the system's output for each task is well-formed JSON and adheres to the specified schema.
- Verify that the SWOT analysis contains at least 2 distinct points for each category (Strengths, Weaknesses,...
- ReasoningQualityScore target: 85
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Ensure the system's output for each task is well-formed JSON and adheres to the specified schema.
- Verify that the SWOT analysis contains at least 2 distinct points for each category (Strengths, Weaknesses, Opportunities, Threats).
Metrics
- ReasoningQualityScore · Target 85
- AccuracyOfInsights · Target 80
- MCPToolIntegrationSuccessRate · Target 95
Workflow tasks
- Task 1
Input: {'startup_name': 'str', 'business_plan_summary': 'str', 'financial_projections': 'json', 'technical_overview': 'str', 'market_data_query': 'str'}
Output: {'risk_assessment': {'overall_score': 'int', 'financial_risk': 'float', 'technical_risk': 'float', 'market_risk': 'float'}, 'swot_analysis': {'strengths': ['str'], 'weaknesses': ['str'], 'opportunities': ['str'], 'threats': ['str']}, 'strategic_feedback': 'str', 'confidence_score': 'float'}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.