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

---

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