SEC MCP

# Research Administrator Examples ## 🚀 Quick Start Examples ### 1. Basic Research Request ``` "Use RESEARCH-ADMIN's create_research_plan tool to analyze AAPL" ``` ### 2. Quick Investment Check (5 min) ``` "Use RESEARCH-ADMIN's create_research_plan tool with ticker TSLA and depth quick" ``` ### 3. Standard Analysis (15 min) ``` "Use RESEARCH-ADMIN's create_research_plan tool with ticker MSFT, depth standard, and sector technology" ``` ### 4. Deep Dive Research (45 min) ``` "Use RESEARCH-ADMIN's create_research_plan tool with ticker AMZN, depth deep, sector technology, and focus_areas ['cloud growth', 'AI investments', 'retail margins']" ``` ### 5. Natural Language Processing ``` "Use RESEARCH-ADMIN's parse_research_request tool with request 'Is Netflix a good buy after earnings?'" ``` ## 📊 Complete Workflow Example ### Step 1: Create Research Plan ``` Use RESEARCH-ADMIN's create_research_plan tool with: - ticker: "NVDA" - depth: "standard" - sector: "technology" - focus_areas: ["AI chip demand", "data center growth"] ``` ### Step 2: Execute the Plan The Research Administrator will return a plan with specific MCP calls like: 1. Use SEC-SCRAPER's scrape_10k_financials with ticker "NVDA" 2. Use SEC-SCRAPER's get_current_price with ticker "NVDA" 3. Use INDUSTRY-ASSUMPTIONS's generate_full_dcf_assumptions with ticker "NVDA" and industry "technology" 4. Use NEWS-SENTIMENT's get_aggregate_sentiment with ticker "NVDA" 5. Use ANALYST-RATINGS's get_consensus_rating with ticker "NVDA" 6. Use INSTITUTIONAL's track_institutional_changes with ticker "NVDA" ### Step 3: Generate Report Outline After collecting data: ``` Use RESEARCH-ADMIN's generate_report_outline with: - ticker: "NVDA" - collected_data: { "sec_data": true, "sentiment_data": true, "analyst_data": true, "institutional_data": true, "dcf_data": true } ``` ### Step 4: Create Executive Summary ``` Use RESEARCH-ADMIN's generate_executive_summary with: - ticker: "NVDA" - key_findings: { "recommendation": "BUY", "price_target": 850, "upside": 25, "conviction": "High", "valuation_thesis": "Trading below DCF fair value despite AI leadership", "momentum_thesis": "Strong institutional buying and positive sentiment", "growth_thesis": "Data center revenue accelerating, confirmed by hiring data", "main_risk": "Valuation multiple compression if growth slows", "catalyst": "Next-gen chip announcement" } ``` ## 🎯 Sector-Specific Examples ### Technology Sector ``` { "ticker": "META", "depth": "standard", "sector": "technology", "focus_areas": ["metaverse ROI", "ad revenue recovery", "AI infrastructure"] } ``` ### Financial Sector ``` { "ticker": "BAC", "depth": "standard", "sector": "finance", "focus_areas": ["net interest margin", "loan loss provisions", "trading revenue"] } ``` ### Healthcare/Biotech ``` { "ticker": "PFE", "depth": "deep", "sector": "healthcare", "focus_areas": ["pipeline value", "patent cliffs", "M&A strategy"] } ``` ### Energy Sector ``` { "ticker": "CVX", "depth": "standard", "sector": "energy", "focus_areas": ["production growth", "capex discipline", "dividend sustainability"] } ``` ### Retail Sector ``` { "ticker": "TGT", "depth": "standard", "sector": "retail", "focus_areas": ["inventory management", "e-commerce growth", "margin pressure"] } ``` ## 📈 Advanced Research Scenarios ### Earnings Analysis ``` "Parse this request: Analyze GOOGL after earnings miss, focus on cloud and AI" ``` ### M&A Target Analysis ``` { "ticker": "ATVI", "depth": "deep", "focus_areas": ["acquisition premium", "regulatory risks", "synergies"] } ``` ### Turnaround Situation ``` { "ticker": "DIS", "depth": "deep", "focus_areas": ["streaming profitability", "parks recovery", "content strategy"] } ``` ### High Growth Tech ``` { "ticker": "SNOW", "depth": "standard", "focus_areas": ["revenue growth sustainability", "path to profitability", "competitive moat"] } ``` ## 🔄 Multi-Stock Comparison ### Compare Tech Giants ``` First: Create research plan for AAPL with depth "quick" Then: Create research plan for MSFT with depth "quick" Then: Create research plan for GOOGL with depth "quick" Finally: Compare the executive summaries ``` ### Sector Screening ``` "Parse request: Quick check on top 5 semiconductor stocks for AI exposure" ``` ## 💡 Tips for Best Results 1. **Always specify sector** for accurate DCF assumptions 2. **Use focus_areas** to target specific concerns 3. **Start with "standard" depth** unless you need quick/deep 4. **Chain multiple requests** for comparative analysis 5. **Use natural language** for complex requests ## 🎪 Complete Example Conversation ``` User: "I'm interested in investing in AI stocks. Can you analyze Nvidia?"