TESTING.md

🧪 Testing Guide

Quick Start: Test with Sample Files

We've created sample files in the examples/ directory to demonstrate all MissionControlMCP tools.

Run All Tests

python demo.py

This will test:

• ✅ Text Extraction - Keywords & summarization from business report
• ✅ Email Classification - Intent detection on 3 sample emails
• ✅ Data Visualization - Line and bar charts from CSV data
• ✅ KPI Generation - Calculate business metrics
• ✅ RAG Semantic Search - Semantic search across documents

---

Test Individual Tools

1. Text Extractor

from tools.text_extractor import extract_text

# Read sample report
with open("examples/sample_report.txt", "r") as f:
    text = f.read()

# Extract keywords
keywords = extract_text(text, operation="keywords")
print(keywords)

# Generate summary
summary = extract_text(text, operation="summarize", max_length=200)
print(summary['result'])

2. Email Intent Classifier

from tools.email_intent_classifier import classify_email_intent

# Test complaint email
with open("examples/sample_email_complaint.txt", "r") as f:
    email = f.read()

result = classify_email_intent(email)
print(f"Intent: {result['intent']} (confidence: {result['confidence']})")

3. Data Visualizer

from tools.data_visualizer import visualize_data

# Load CSV data
with open("examples/business_data.csv", "r") as f:
    data = f.read()

# Create revenue trend chart
chart = visualize_data(
    data=data,
    chart_type="line",
    x_column="month",
    y_column="revenue",
    title="Revenue Trends"
)

# Save chart
import base64
with open("revenue_chart.png", "wb") as f:
    f.write(base64.b64decode(chart['image_base64']))

4. KPI Generator

from tools.kpi_generator import generate_kpis
import json

data = {
    "revenue": 5500000,
    "costs": 3400000,
    "customers": 2700,
    "current_revenue": 5500000,
    "previous_revenue": 5400000,
    "employees": 50
}

result = generate_kpis(json.dumps(data), metrics=["revenue", "growth", "efficiency"])
print(f"Generated {len(result['kpis'])} KPIs")
print(result['summary'])

5. RAG Semantic Search

from tools.rag_search import search_documents

# Load sample documents
with open("examples/sample_documents.txt", "r") as f:
    content = f.read()

documents = [doc.strip() for doc in content.split("##") if doc.strip()]

# Search
results = search_documents("What is machine learning?", documents, top_k=3)
for res in results['results']:
    print(f"Score: {res['score']:.4f} - {res['document'][:100]}...")

---

Test with Claude Desktop

1. Configure Claude Desktop

Edit %AppData%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "mission-control": {
      "command": "python",
      "args": ["C:/path/to/mission_control_mcp/mcp_server.py"]
    }
  }
}

2. Restart Claude Desktop

3. Try These Prompts

Text Processing:

Extract keywords from this text: [paste sample_report.txt content]

Email Classification:

Classify this email: [paste sample_email_complaint.txt content]

Data Visualization:

Create a line chart showing revenue trends from this data: [paste business_data.csv]

KPI Generation:

Calculate KPIs from this business data: {"revenue": 5000000, "costs": 3000000, "customers": 2500}

Semantic Search:

Search these documents for information about AI: [paste sample_documents.txt]

---

Test MCP Server Directly

Run the MCP Server

python mcp_server.py

Test Individual Tools

python test_individual.py

This runs isolated tests on each tool (8 total).

MCP Server Tests

python demo.py

Tests all MCP tool handlers and server integration.

---

Sample Files Overview

File	Purpose	Tool
sample_report.txt	Business report (2,200 chars)	Text Extractor
business_data.csv	12 months financial data	Data Visualizer, KPI Generator
sample_email_complaint.txt	Customer complaint	Email Classifier
sample_email_inquiry.txt	Sales inquiry	Email Classifier
sample_email_urgent.txt	Urgent system alert	Email Classifier
sample_documents.txt	5 topic documents	RAG Search

---

Expected Results

Text Extraction

• Keywords: customer, revenue, growth, operational, market, performance
• Summary: ~200 character executive summary

Email Classification

• Complaint: request + order intents (confidence: 1.00)
• Inquiry: meeting + inquiry intents (confidence: 1.00)
• Urgent: urgent intent (confidence: 1.00)

Data Visualization

• Line Chart: 48KB base64 PNG (1000x600px)
• Bar Chart: 26KB base64 PNG (1000x600px)

KPI Generation

• 9 KPIs calculated: total_revenue, profit, profit_margin_percent, revenue_growth, etc.
• Summary: Executive insights on revenue growth and profitability

RAG Search

• Query: "What is machine learning?"
• Top Result: Document 1 (AI Overview) - Score: 0.56
• Semantic matching: Finds relevant content even with different wording

---

Troubleshooting

FAISS Errors

pip install faiss-cpu sentence-transformers

Import Errors

cd mission_control_mcp
pip install -r requirements.txt

Python Version

Requires Python 3.11+. Check with:

python --version

---

Performance Benchmarks

Tool	Sample File	Execution Time
Text Extractor	2,200 chars	~0.5s
Email Classifier	500 chars	~0.1s
Data Visualizer	12 data points	~1.2s
KPI Generator	10 metrics	~0.3s
RAG Search	6 documents	~2.5s (first run, includes model load)

---

Next Steps

1. ✅ Run python demo.py to verify all tools work
2. ✅ Try individual tool tests with your own data
3. ✅ Configure Claude Desktop integration
4. ✅ Test with Claude using sample prompts
5. ✅ Create custom workflows combining multiple tools

Happy Testing! 🚀