EXAMPLES.md

💼 Real-World Use Cases & Examples

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

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🏢 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

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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

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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

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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

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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

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🎯 Specific Examples

Example 1: Text Processing Chain

Input:

Long technical document with 5000 words about machine learning algorithms...

Processing:

# 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

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Example 2: Business Intelligence Dashboard

Input Data:

{
  "revenue": 5000000,
  "costs": 3000000,
  "customers": 2500,
  "current_revenue": 5000000,
  "previous_revenue": 4200000,
  "employees": 50
}

Processing:

# 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

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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

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Example 4: Research Assistant Workflow

Task: Research "AI safety frameworks"

Automated Process:

# 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

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Example 5: Document Processing Pipeline

Scenario: Process 100 contract PDFs

Automated Workflow:

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%

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📊 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

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🎓 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

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🔗 Integration Examples

With Claude Desktop

{
  "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"

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🚀 Quick Start Templates

Template 1: Document Summarizer

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

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

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")

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💡 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! 🚀