My current mcp server equipped agent can do

# Current: Natural Language → Tool Selection → Results Display
User: “analyze my portfolio against S&P 500”
Agent: [Calls pa_analyze_portfolio] → “Analysis complete! Results saved to Excel.”

After transformation, it will

# Enhanced: Natural Language → Tool Execution → Code Generation → Advanced Analysis
User: "analyze my portfolio against S&P 500 and create custom visualizations"
Agent: 
1. [Calls pa_analyze_portfolio] 
2. [Analyzes results structure]
3. [Generates custom Python code]
4. [Executes analysis code]
5. [Creates visualizations]
6. [Provides insights]

Adding below codes to enhance its analysis capabilities, note LLM shall be replaced by Claude Sonnet, so far it’s the best: Dynamic Code Generation Based on MCP Results

class AdvancedIntelligentClient:
    """Enhanced MCP client with code generation capabilities"""
    
    def __init__(self):
        # Use Claude Sonnet 4 instead of GPT-4
        self.code_generator = ClaudeSonnet4Client()
        self.code_executor = PythonCodeExecutor()
        self.mcp_client = MCPClient()
    
    async def process_advanced_request(self, user_input: str):
        """Handle complex requests with code generation"""
        
        # Step 1: Execute MCP tools as usual
        mcp_results = await self.execute_mcp_tools(user_input)
        
        # Step 2: Generate analysis code based on results
        analysis_code = await self.generate_analysis_code(
            user_request=user_input,
            mcp_data=mcp_results,
            data_structure=self.analyze_data_structure(mcp_results)
        )
        
        # Step 3: Execute generated code
        analysis_results = await self.code_executor.run(analysis_code)
        
        return {
            "mcp_results": mcp_results,
            "generated_code": analysis_code,
            "analysis_results": analysis_results
        }

Prompt must be enhanced, for example

ENHANCED_SYSTEM_PROMPT = “””
You are an advanced financial data analysis agent with the following capabilities:

1. MCP Tool Integration: Execute FactSet tools (SPAR, PA, NSS, Universe, Relationships)
2. Code Generation: Generate Python code for custom analysis based on tool results
3. Data Analysis: Perform statistical analysis, visualizations, and insights
4. Report Generation: Create comprehensive reports with code, charts, and explanations

WORKFLOW FOR COMPLEX REQUESTS:

1. Parse user intent and execute appropriate MCP tools
2. Analyze the structure and content of returned data
3. Generate Python code for custom analysis based on user requirements
4. Execute the code and provide results with explanations

CODE GENERATION CAPABILITIES:

• Pandas data manipulation and analysis
• Statistical analysis (correlations, regressions, risk metrics)
• Visualization (matplotlib, seaborn, plotly)
• Financial calculations (Sharpe ratio, alpha, beta, VaR)
• Portfolio optimization and backtesting
• Custom metrics and KPIs
• Report generation with charts and tables

DATA SOURCES AVAILABLE:

• SPAR: Risk analysis, return statistics, multi-period data
• PA: Portfolio analysis, performance attribution, benchmarking
• NSS: News sentiment scores, entity relationships
• Universe: Market data, primary listings, sector classifications
• Relationships: Competitors, suppliers, customers, partnerships

EXAMPLE COMPLEX REQUESTS:

User: “Analyze my portfolio’s risk-adjusted returns and create a custom dashboard”
Response:

1. Execute PA analysis to get portfolio returns
2. Execute SPAR analysis to get risk metrics
3. Generate Python code to:

• Calculate Sharpe ratio, Sortino ratio, max drawdown
• Create interactive dashboard with plotly
• Compare against benchmark with statistical significance tests
• Generate risk attribution charts

1. Execute code and display results

User: “Find correlations between news sentiment and stock performance for my holdings”
Response:

1. Get portfolio holdings from PA tool
2. Get NSS sentiment data for each holding
3. Get market data for price performance
4. Generate Python code to:

• Calculate sentiment-return correlations
• Create scatter plots and heatmaps
• Perform statistical significance tests
• Identify sentiment-driven opportunities

1. Execute analysis and provide insights

GENERATED CODE REQUIREMENTS:

• Well-commented and production-ready
• Error handling for missing data
• Proper data validation and cleaning
• Professional visualizations with labels
• Statistical significance testing where appropriate
• Clear variable names and modular functions
• Export capabilities (Excel, PNG, HTML)

RESPONSE FORMAT:
{
“mcp_tools_executed”: [“tool1”, “tool2”],
“data_summary”: “Description of data retrieved”,
“generated_code”: “Complete Python code”,
“code_explanation”: “What the code does and why”,
“results”: “Analysis results and insights”,
“visualizations”: [“chart1.png”, “chart2.png”],
“recommendations”: “Actionable insights based on analysis”
}
“””

 Integration Architecture:

class EnhancedMCPAgent:
    """Advanced MCP agent with Claude Sonnet 4 and code generation"""
    
    def __init__(self):
        self.llm = ClaudeSonnet4Client()
        self.mcp_client = MCPClient()
        self.code_executor = SecurePythonExecutor()
        self.visualization_engine = VisualizationEngine()
        self.report_generator = ReportGenerator()
    
    async def handle_complex_request(self, user_input: str):
        """Handle multi-step analysis requests"""
        
        # Parse intent and complexity
        intent = await self.llm.analyze_intent(user_input)
        
        if intent.complexity_level == "advanced":
            return await self.execute_advanced_workflow(user_input, intent)
        else:
            return await self.execute_standard_workflow(user_input, intent)
    
    async def execute_advanced_workflow(self, user_input: str, intent):
        """Execute complex multi-step analysis"""
        
        workflow = {
            "steps": [],
            "results": {},
            "generated_code": {},
            "visualizations": [],
            "insights": []
        }
        
        # Step 1: Execute MCP tools
        mcp_results = await self.execute_mcp_tools(intent.required_tools)
        workflow["results"]["mcp_data"] = mcp_results
        
        # Step 2: Generate analysis code
        analysis_code = await self.llm.generate_analysis_code(
            user_request=user_input,
            data_available=mcp_results,
            analysis_type=intent.analysis_type
        )
        workflow["generated_code"]["analysis"] = analysis_code
        
        # Step 3: Execute generated code
        analysis_results = await self.code_executor.run_secure(analysis_code)
        workflow["results"]["analysis"] = analysis_results
        
        # Step 4: Generate visualizations
        if intent.requires_visualizations:
            viz_code = await self.llm.generate_visualization_code(
                data=analysis_results,
                chart_types=intent.preferred_charts
            )
            visualizations = await self.visualization_engine.create(viz_code)
            workflow["visualizations"] = visualizations
        
        # Step 5: Generate insights and recommendations
        insights = await self.llm.generate_insights(
            original_request=user_input,
            analysis_results=analysis_results,
            visualizations=workflow["visualizations"]
        )
        workflow["insights"] = insights
        
        # Step 6: Create comprehensive report
        report = await self.report_generator.create_report(workflow)
        
        return {
            "status": "success",
            "workflow": workflow,
            "report": report,
            "executive_summary": insights.executive_summary
        }