🚕 Uncovering Hidden Truths of the NYC Taxi Industry with Claude for Desktop × Databricks Managed MCP Server: Integrated Analysis of Internal Data × Public Information

🚕 How I Discovered 7 Hidden Problems in NYC's Taxi Industry Using Claude + Databricks MCP: The Power of Hybrid Data Analysis

Introduction: Why I Wrote This Article

Have you ever faced these challenges when analyzing corporate internal data?

"Internal data alone doesn't give us the full industry context..."
"I want to compare with external research, but doing it manually is overwhelming..."

I've solved this problem using Claude for Desktop and Databricks Managed MCP server. By automatically fusing Databricks internal data with public information from the internet, I uncovered deep insights that would have been invisible when looking at either source alone.

🎯 What You'll Gain from This Article

1. How to securely connect to Databricks via MCP Server
2. Advanced analysis techniques combining internal data with public information
3. How AI automatically distinguishes between inference and fact while creating analysis reports
4. Complete, working HTML report code

🔧 Tech Stack

• Claude for Desktop
• Databricks Managed MCP server
• Databricks Genie
  

🔧 Integration Architecture

Future research includes finding alternatives to Claude for Desktop that could connect directly to Claude on Databricks using dedicated lines, eliminating concerns about data passing through external Claude instances via the internet.

🚀 Implementation: Step by Step

Step 1: MCP Server Configuration

First, configure MCP settings to connect Claude for Desktop to Databricks:

{
  "mcpServers": {
    "uc-genie-mcp": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://<workspace-hostname>/api/2.0/mcp/genie/<genie-space-id>",
        "--header",
        "Authorization: Bearer <personal-access-token>"
      ]
    }
  } 
}

This configuration enables secure MCP access from Claude for Desktop to your Databricks Genie Space.

Step 2: Retrieving Internal Data

Through MCP, I retrieved actual data from Databricks. Here's a sample of the queries executed internally:

-- Regional disparity analysis
SELECT pickup_zip, COUNT(*) as trip_count 
FROM samples.nyctaxi.trips 
WHERE tpep_pickup_datetime >= '2016-02-01' 
  AND tpep_pickup_datetime < '2016-03-01' 
GROUP BY pickup_zip 
ORDER BY trip_count DESC 
LIMIT 10;

This query revealed extreme concentration of trips in specific Manhattan ZIP codes.

Step 3: Fusion with External Information

This is the most innovative part. Claude automatically performed the following:

1. Understood internal data analysis results
2. Automatically searched for relevant external information
3. Selected credible sources (academic papers, official statistics, etc.)
4. Integrated internal data with external information to generate new insights

For example, after discovering "only 5 trips in the Bronx" in internal data, Claude automatically found the following external information:

• Columbia University research: 57% of outer borough residents depend on public transit
• NYC TLC official data: Impact of the Green Taxi program
• Academic papers: Uber growth rate (597% increase in outer boroughs)

📊 7 Problem Analysis: Detailed Findings

Let's dive deep into the 7 major problems revealed by the analysis.

🏙️ Problem 1: Extreme Regional Disparity

Cross-referenced with External Information

• Todd W. Schneider - Analyzing 1.1 Billion NYC Taxi and Uber Trips

💰 Problem 2: Regional Fare Disparity Reality

Cross-referenced with External Information

• NYC TLC official fare: Initial fare $3.00
• Airport areas typically have higher fares (Newark Airport median $114)

⏰ Problem 3: Peak Hour Inefficiency

Cross-referenced with External Information

• Research shows Tuesday 6 PM is the weekly peak
• 4:30 PM shift change creates artificial supply shortage

🌱 Problem 4: Environmental Impact

External Information Cross-reference

• Columbia University - Clean Air Taxis Study

🚗 Problem 5: Disruptive Impact of Ride-sharing

*Note: Medallion refers to taxi operating licenses

• Columbia Human Rights Law Review
• ResearchGate - Taxi and Uber Demand Analysis

📊 Problem 6: Data Analysis Limitations and Challenges

• NYC TLC Trip Record Data

📅 Problem 7: Day-of-Week Demand Variation

🎯 Conclusion: February 2016 - Recording a Turning Point

🎨 Features of the Generated Report

The final HTML report generated has these features:

1. Completely standalone: No external file dependencies
2. Responsive design: Works from mobile to desktop
3. Interactive: Hover for details, click for filtering
4. Beautiful visuals: Glassmorphism, animation effects

🎯 Practical Applications

This method isn't limited to NYC taxi industry - it's applicable across various fields:

1. Retail Industry

• Internal: POS data
• External: Consumer trends, competitive analysis
• Insights: Discovering hidden purchase patterns

2. Manufacturing

• Internal: Production efficiency data
• External: Supply chain information, regulatory trends
• Insights: Discovering optimization opportunities

3. Financial Industry

• Internal: Transaction data
• External: Market trends, regulatory changes
• Insights: Early risk detection

🔧 Implementation Best Practices

1. Data Security: Always ensure MCP connections use secure authentication
2. Source Verification: Cross-reference multiple external sources for accuracy
3. Clear Attribution: Always distinguish between internal data, external data, and inferences
4. Performance Optimization: Cache frequently accessed data to reduce API calls

🎁 Sharing the Prompt

Finally, I'm sharing the prompt used in this analysis. You can use this prompt to perform similar analyses:

# 🚕 NYC Taxi Data Analysis Report Creation Request v2.0

## 🎯 Mission
Retrieve data from Databricks `samples.nyctaxi.trips` table via MCP Server, analyze it, and create an interactive HTML report for technical professionals that discovers and visualizes hidden problems in the New York City taxi industry.

## 👤 Role Setting
You are an expert with the following specializations:
- **Taxi Industry Consultant**: 15+ years of expertise in NYC transportation
- **Data Scientist**: Expert in statistical analysis and machine learning
- **UX/UI Designer**: Data visualization specialist
- **Full-stack Engineer**: Extensive HTML/CSS/JS implementation experience

## 📋 Strict Rules

### 1. Data Source Management
【Required】For all analysis results, clearly state:
- For Databricks data: "Source: samples.nyctaxi.trips"
- For external information: "Source: [specific URL]"
- For inferences: Prefix with "【Inference】" tag

### 2. Output Specifications
- **File format**: Single `.html` file (no external file references)
- **Character encoding**: UTF-8
- **Language**: 100% Japanese (including variable names and comments)
- **Target audience**: Databricks-enthusiast engineers (welcome technical details)

### 3. Quality Standards
- Minimum **7 graphs** (at least one per problem)
- Load time under 3 seconds
- Mobile responsive required

## 🔍 Analysis Process

### Phase 1: Data Understanding (15 minutes)
-- Sample queries to execute
-- 1. Schema check
DESCRIBE samples.nyctaxi.trips;

-- 2. Data period
SELECT MIN(pickup_datetime), MAX(pickup_datetime), COUNT(*) 
FROM samples.nyctaxi.trips;

-- 3. Missing values check
SELECT 
  SUM(CASE WHEN fare_amount IS NULL THEN 1 ELSE 0 END) as fare_nulls,
  SUM(CASE WHEN tip_amount IS NULL THEN 1 ELSE 0 END) as tip_nulls
FROM samples.nyctaxi.trips;

### Phase 2: Problem Discovery (30 minutes)
**Required Analysis Items** (calculate specific numbers for each):

1. **Supply-Demand Gap Analysis**
   - Supply shortage areas by time
   - Day-of-week pattern differences

2. **Fare Anomaly Detection**
   - Outliers (3σ+) identification
   - Potential fare fraud

3. **Regional Disparity Analysis**
   - Manhattan vs other boroughs
   - Regional tip rate differences

4. **Time Efficiency Problems**
   - Revenue loss due to traffic
   - Empty time analysis

5. **Seasonality & Event Impact**
   - Weather correlation
   - Major event anomalies

6. **Driver Behavior Analysis**
   - Revenue maximization patterns
   - Inefficient routes

### Phase 3: Verification (15 minutes)
Search for external information with these keywords to verify analysis results:
- "NYC taxi data analysis 2016"
- "New York taxi problem"
- "NYC taxi fare patterns"

## 🎨 HTML Implementation Requirements

### Required Structure
<!DOCTYPE html>
<html lang="ja">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Hidden Problems in NYC Taxi Industry - Databricks Analysis Report</title>
    
    <!-- Required Libraries (CDN) -->
    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
    <script src="https://d3js.org/d3.v7.min.js"></script>
    
    <style>
        /* Required Style Elements */
        :root {
            --primary: #FFC107; /* NYC Taxi Yellow */
            --secondary: #212529;
            --accent: #00BCD4;
        }
        
        /* Glassmorphism */
        .glass-card {
            background: rgba(255, 255, 255, 0.1);
            backdrop-filter: blur(10px);
            border: 1px solid rgba(255, 255, 255, 0.2);
            border-radius: 20px;
            padding: 2rem;
            margin: 2rem 0;
        }
        
        /* Animation */
        .fade-in {
            animation: fadeIn 0.8s ease-in;
        }
        
        @keyframes fadeIn {
            from { opacity: 0; transform: translateY(20px); }
            to { opacity: 1; transform: translateY(0); }
        }
    </style>
</head>
<body>
    <!-- Hero Section -->
    <header class="hero">
        <h1>🚕 Hidden Truth of NYC Taxi Industry</h1>
        <p class="subtitle">Databricks samples.nyctaxi.trips Data Analysis Report</p>
    </header>
    
    <!-- Summary Cards (3 cards) -->
    <section class="executive-summary">
        <!-- Key findings in card format -->
    </section>
    
    <!-- Data Source Information -->
    <section class="data-source">
        <h2>📊 Data Source Information</h2>
        <div class="source-info">
            Source: Databricks samples.nyctaxi.trips<br>
            Period: [specific period]<br>
            Total Records: [specific number]
        </div>
    </section>
    
    <!-- Problem Analysis (6+ sections) -->
    <section class="problem-analysis">
        <!-- For each problem -->
        <article class="problem-card glass-card">
            <h3>Problem 1: [Specific Problem Name]</h3>
            <div class="chart-container">
                <canvas id="chart1"></canvas>
            </div>
            <div class="insights">
                <p><strong>Data Analysis Results:</strong></p>
                <p>Source: samples.nyctaxi.trips</p>
                <ul>
                    <li>Findings with specific numbers</li>
                </ul>
                <p class="speculation">【Inference】This trend suggests...</p>
            </div>
        </article>
    </section>
    
    <!-- External Validation -->
    <section class="external-validation">
        <h2>🔍 Cross-reference with External Information</h2>
        <!-- Citations with URL links -->
    </section>
    
    <!-- Interactive Elements -->
    <script>
        // Graph initialization
        document.addEventListener('DOMContentLoaded', function() {
            // Chart.js implementation
            // Add tooltips, zoom functionality
        });
    </script>
</body>
</html>

### Required Design Elements
1. **New York Theme**
   - Taxi yellow (#FFC107) base color
   - Skyscraper-inspired vertical layout
   - Neon sign accents

2. **Interactive Features**
   - Graph hover for details
   - Click for data filtering
   - Smooth scroll navigation

3. **Responsive Support**
   - Mobile: 1 column
   - Tablet: 2 columns
   - Desktop: 3 column grid

## ✅ Final Checklist

### Required Confirmations
- [ ] File extension is `.html`
- [ ] All Japanese (including comments)
- [ ] 7+ graphs implemented
- [ ] Retrieved data from Databricks "samples.nyctaxi.trips" via MCP server and performed analysis
- [ ] Each analysis states "Source: samples.nyctaxi.trips"
- [ ] Inference sections tagged with 【Inference】
- [ ] All external reference URLs are linked
- [ ] Both Chart.js and D3.js used
- [ ] Glassmorphism effects implemented
- [ ] Mobile display verified
- [ ] Load time under 3 seconds

### Error Prevention Check
- [ ] No character encoding issues (UTF-8 confirmed)
- [ ] No JavaScript errors
- [ ] All graphs display correctly
- [ ] No broken links

## 🚀 Pre-execution Confirmation

Please ask the following before execution:
1. "Are there any specific problem areas you'd like me to focus on in the analysis?"
2. "Do you have any preferences for graph color schemes?"
3. "Are there any additional technical details you'd like included?"

---
**Note**: This prompt is designed to generate perfect deliverables in a single execution. Follow the instructions and proceed step by step steadily.

Conclusion: Towards a New Era of Analysis

The greatest value of this approach is the ability to integrate multiple information sources at a scale and speed impossible for humans. It can instantly analyze thou