EXAMPLES.md

# 💼 Real-World Use Cases & Examples

This document showcases practical, real-world applications of MissionControlMCP's tools.

---

## 🏢 Enterprise Use Cases

### Use Case 1: Automated Report Generation
**Scenario:** Monthly business reporting automation

**Workflow:**
1. **pdf_reader** → Extract data from quarterly reports
2. **text_extractor** → Summarize key findings
3. **kpi_generator** → Calculate business metrics
4. **data_visualizer** → Create performance charts

**Business Value:** Saves 10+ hours per month of manual work

---

### Use Case 2: Customer Support Intelligence
**Scenario:** Automated email triage and routing

**Workflow:**
1. **email_intent_classifier** → Categorize incoming emails
2. Route based on intent:
   - Complaints → Priority queue
   - Inquiries → Sales team
   - Urgent → Immediate escalation

**Business Value:** 80% faster email routing, improved response times

---

### Use Case 3: Market Research Automation
**Scenario:** Competitive analysis from web sources

**Workflow:**
1. **web_fetcher** → Collect competitor website content
2. **text_extractor** → Extract key information
3. **rag_search** → Find relevant insights across sources
4. **text_extractor** → Generate executive summary

**Business Value:** Real-time market intelligence, faster decision making

---

### Use Case 4: Knowledge Base Search
**Scenario:** Internal document search system

**Workflow:**
1. **pdf_reader** → Index company documents
2. **rag_search** → Semantic search across knowledge base
3. Find relevant information even with different wording

**Business Value:** Instant access to company knowledge, reduced information silos

---

### Use Case 5: Data Analysis Pipeline
**Scenario:** Convert and visualize business data

**Workflow:**
1. **file_converter** → Convert PDF reports to CSV
2. **data_visualizer** → Generate trend charts
3. **kpi_generator** → Calculate performance metrics

**Business Value:** Automated data transformation, visual insights

---

## 🎯 Specific Examples

### Example 1: Text Processing Chain

**Input:**
```
Long technical document with 5000 words about machine learning algorithms...
```

**Processing:**
```python
# Step 1: Clean the text
cleaned = text_extractor(text, operation="clean")

# Step 2: Extract keywords
keywords = text_extractor(text, operation="keywords")

# Step 3: Create summary
summary = text_extractor(text, operation="summarize", max_length=300)
```

**Output:**
- Clean text: Formatted, ready for analysis
- Keywords: "machine learning, neural networks, algorithms, training, optimization"
- Summary: 300-word executive summary

---

### Example 2: Business Intelligence Dashboard

**Input Data:**
```json
{
  "revenue": 5000000,
  "costs": 3000000,
  "customers": 2500,
  "current_revenue": 5000000,
  "previous_revenue": 4200000,
  "employees": 50
}
```

**Processing:**
```python
# Generate KPIs
kpis = kpi_generator(data, metrics=["revenue", "growth", "efficiency"])

# Visualize monthly trends
chart = data_visualizer(monthly_data, chart_type="line", title="Revenue Trends")
```

**Output:**
- Profit margin: 40%
- Revenue growth: 19%
- Revenue per employee: $100,000
- Interactive chart showing trends

---

### Example 3: Email Routing System

**Sample Emails:**

1. **"I need help with my order #12345 that hasn't arrived"**
   - Intent: `complaint` + `order` (Confidence: 0.8)
   - Action: Route to support + Priority flag

2. **"Can we schedule a meeting to discuss the proposal?"**
   - Intent: `meeting` (Confidence: 0.9)
   - Action: Route to calendar system

3. **"URGENT: Server down, customers can't access site"**
   - Intent: `urgent` + `complaint` (Confidence: 1.0)
   - Action: Immediate escalation to DevOps

---

### Example 4: Research Assistant Workflow

**Task:** Research "AI safety frameworks"

**Automated Process:**
```python
# 1. Fetch relevant articles
urls = ["https://ai-safety-org.com/frameworks", 
        "https://research-institute.edu/ai-ethics"]
articles = [web_fetcher(url) for url in urls]

# 2. Extract content
summaries = [text_extractor(article, operation="summarize") 
             for article in articles]

# 3. Semantic search across all content
insights = rag_search("governance frameworks", summaries, top_k=5)

# 4. Generate final report
report = text_extractor(combined_insights, operation="summarize")
```

**Result:** Comprehensive research report in minutes

---

### Example 5: Document Processing Pipeline

**Scenario:** Process 100 contract PDFs

**Automated Workflow:**
```python
for contract in contracts:
    # Extract text from PDF
    text = pdf_reader(contract)
    
    # Extract key terms
    keywords = text_extractor(text, operation="keywords")
    
    # Search for specific clauses
    results = rag_search("termination clause", [text], top_k=1)
    
    # Store in database
    save_to_database(contract_id, text, keywords, results)
```

**Business Impact:**
- Manual processing: 5 minutes/contract = 8.3 hours
- Automated: 10 seconds/contract = 17 minutes
- Time saved: 90%

---

## 📊 ROI Examples

### Small Business (10 employees)
**Monthly Automation Savings:**
- Email classification: 20 hours → $600
- Report generation: 15 hours → $450
- Data analysis: 10 hours → $300
- **Total: 45 hours/$1,350 per month**

### Enterprise (500 employees)
**Annual Automation Value:**
- Customer support efficiency: $500K
- Knowledge management: $300K
- Business intelligence: $400K
- **Total: $1.2M annually**

---

## 🎓 Learning Path

### Beginner: Start Simple
1. Try **text_extractor** with a sample document
2. Use **email_intent_classifier** on sample emails
3. Create a basic chart with **data_visualizer**

### Intermediate: Build Workflows
1. Combine **web_fetcher** + **text_extractor**
2. Set up **rag_search** with your documents
3. Create a KPI dashboard with **kpi_generator**

### Advanced: Full Automation
1. Build complete document processing pipelines
2. Implement intelligent email routing systems
3. Create real-time business intelligence dashboards

---

## 🔗 Integration Examples

### With Claude Desktop
```json
{
  "mcpServers": {
    "mission-control": {
      "command": "python",
      "args": ["path/to/mcp_server.py"]
    }
  }
}
```

**Usage in Claude:**
- "Extract text from this PDF and summarize it"
- "Fetch this website and find information about pricing"
- "Calculate KPIs from this business data"

---

## 🚀 Quick Start Templates

### Template 1: Document Summarizer
```python
from tools.pdf_reader import read_pdf
from tools.text_extractor import extract_text

# Read PDF
content = read_pdf("document.pdf")

# Generate summary
summary = extract_text(content["text"], 
                      operation="summarize", 
                      max_length=500)

print(summary["result"])
```

### Template 2: Web Research Assistant
```python
from tools.web_fetcher import fetch_web_content
from tools.rag_search import search_documents

# Fetch multiple sources
urls = ["url1", "url2", "url3"]
docs = [fetch_web_content(url)["content"] for url in urls]

# Search for specific information
results = search_documents("your query", docs, top_k=3)
```

### Template 3: Business Dashboard
```python
from tools.kpi_generator import generate_kpis
from tools.data_visualizer import visualize_data

# Calculate KPIs
kpis = generate_kpis(business_data, 
                     metrics=["revenue", "growth"])

# Visualize trends
chart = visualize_data(trend_data, 
                      chart_type="line",
                      title="Q4 Performance")
```

---

## 💡 Tips for Success

1. **Chain Tools Together** - Combine multiple tools for powerful workflows
2. **Use RAG Search** - Best for finding information across documents
3. **Automate Repetitive Tasks** - Perfect for daily/weekly operations
4. **Start Small** - Test individual tools before building complex systems
5. **Monitor Performance** - Track time/cost savings from automation

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**Ready to automate your enterprise workflows? Start with these examples!** 🚀