Drive-Time Plotter MCP

A Model Context Protocol (MCP) server that analyzes traffic patterns throughout the day using Google Maps API, providing fast visual insights and recommendations.

Motivated by the Hugging Face MCP Hackathon - A production-ready MCP server for intelligent commute planning.

What It Does

Analyzes traffic patterns across 24 hours with 15-minute intervals
Shows optimistic/pessimistic/average drive time predictions
Completes full analysis in ~4 seconds using parallel API requests (120x faster than sequential!)
Works with Claude Desktop, Claude Code, and standalone CLI
Generates both terminal ASCII plots and route maps

Quick Start

1. Install

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pip install -e .

2. Setup API Key

Get your Google Maps API key from https://console.cloud.google.com/apis/credentials

Enable these APIs:

Geocoding API
Directions API
Maps Static API

Create .env file from template: ```bash cp .env.example .env

Edit .env and add your key:

GOOGLE_MAPS_API_KEY=your_key_here


### 3. Test

./test.sh ```

You should see traffic analysis in ~4 seconds!

---

Usage

Terminal (Fastest)

./test_custom.sh

Edit test_custom.sh to customize:

Origin/Destination (default: Evans Hall, UC Berkeley → SFO)
Date
Time range
Interval

Or use the CLI directly:

python cli/driveplot_fast.py \
  --origin "Your Home" \
  --destination "Your Work" \
  --date 2025-11-20 \
  --start 06:00 --end 10:00 \
  --interval 15 \
  --ascii --save-map outputs/route.png --yes

As MCP Server with Claude

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "drivetime-plotter": {
      "command": "python",
      "args": ["-m", "mcp_server.server"],
      "env": {
        "GOOGLE_MAPS_API_KEY": "your_key_here"
      },
      "cwd": "/path/to/mcp-hackathon"
    }
  }
}

Restart Claude Desktop. You can now ask Claude: "When should I drive from X to Y?"

---

How It Works

For Terminal Users

Run ./test_custom.sh
Get results in ~4 seconds (parallel API calls)
See two outputs:

Colored ANSI plot (beautiful terminal visualization)
Simple text plot (plain text using +, o, * characters for Claude Code)

For Claude/Claude Code

When you ask "When should I drive from X to Y?":

Quick Response (< 10 seconds):

   [Show simple text plot]
   🟢 BEST: 5-7 AM (35 min)
   🔴 WORST: 2-4 PM (60 min)
   💡 Recommendation: Leave before 7 AM

   Want more details?

Detailed Response (if you say yes):

Hour-by-hour breakdown
Specific scenarios
Multiple options

Key AI Behavior

Human-Realistic Recommendations:

✅ "Leave 5-7 AM" (reasonable)
❌ "Leave 2 AM" (unrealistic for most people - humans need sleep!)

Adapts to Specificity:

Vague question → Quick answer + ask for more
Specific constraint → Calculate and provide options
Flight time → Work backwards with buffer

---

Architecture

File Structure

mcp-hackathon/
├── .env                       # API key (gitignored, copy from .env.example)
├── .env.example               # Template for API key
├── requirements.txt           # Python dependencies
├── setup.py                   # Package setup (enables `pip install -e .`)
├── test.sh                    # Quick smoke test (~2 sec)
├── test_custom.sh             # Main usage script (customizable route)
├── outputs/                   # Generated maps and plots (gitignored)
├── cli/
│   └── driveplot_fast.py      # Fast CLI with parallel requests
└── mcp_server/
    ├── __init__.py            # Makes mcp_server a Python package
    ├── server.py              # MCP server (exposes tools to Claude)
    ├── google_maps.py         # Google Maps API integration
    └── utils.py               # Helper functions (LatLng, minute_grid, etc.)

Why These Files?

Core Files:

cli/driveplot_fast.py - Standalone terminal tool (works without MCP)
mcp_server/server.py - MCP server for Claude integration
mcp_server/google_maps.py - API calls (shared by CLI and MCP)
mcp_server/utils.py - Shared utilities
mcp_server/__init__.py - Required for Python package (enables from mcp_server import ...)

Configuration:

.env - Your API key (never committed to git)
.env.example - Template for new users
setup.py - Package metadata and dependencies (needed for pip install -e .)
requirements.txt - Direct dependencies list

Testing:

test.sh - Quick verification (1-hour window, ~2 seconds)
test_custom.sh - Real usage (24 hours, ~4 seconds)

API Key Management

Single Source of Truth:

.env file (GOOGLE_MAPS_API_KEY=...)
  ↓
  ├─→ Shell scripts (loaded via `export $(grep -v '^#' .env | xargs)`)
  └─→ Python code (os.getenv("GOOGLE_MAPS_API_KEY"))

Only mcp_server/google_maps.py defines: ``python GOOGLE_KEY = os.getenv("GOOGLE_MAPS_API_KEY", "") ``

All other code imports and uses this variable.

Performance

Parallel API calls: Up to 30 concurrent requests (first pass)
Smart retry with while loop: Retries only failed requests with reduced concurrency (10 workers)
Multiple retry rounds: Up to 3 rounds total, ensures maximum success rate
Speed: ~4 seconds for full 24-hour analysis (96 data points)
120x speedup vs sequential approach

---

MCP Tools Exposed

When running as an MCP server, Claude can use these tools:

1. `geocode(query: str)`

Converts address to coordinates
Returns candidates with formatted addresses and lat/lng
Example: "Berkeley, CA" → [(37.8715, -122.2730), ...]

2. `static_map(origin_lat, origin_lng, dest_lat, dest_lng)`

Generates Google Maps static image URL
Shows route with markers
Returns URL to PNG image

3. `eta_series(...)`

Gets traffic data across time range
Returns optimistic/pessimistic/average drive times
Parameters:
origin_lat, origin_lng
dest_lat, dest_lng
date, start_time, end_time
interval_minutes
include_plot (optional, for matplotlib PNG in Claude chat)

Google Maps APIs Used

Geocoding API: Address → Coordinates
Directions API: Traffic-aware route duration
Static Maps API: Route visualization

Traffic Models:

optimistic - Best-case scenario (light traffic)
pessimistic - Worst-case scenario (heavy traffic)
Average - Calculated from both (realistic expectation)

---

Output Format

Simple Text Plot (for Claude Code)

 66 min |                                  o
 64 min |                                 o ooo
 62 min |                               oo
 ...
 32 min |        **********************
 30 min | +++++++++++++++++++++++++++++++
        +------------------------------------------------------------
          0   1   2   3   4   5   6   7   8   9   10  11  12  ...
          Hour of Day

LEGEND:
  + = Optimistic  |  o = Pessimistic  |  * = Average
  B = Best time   |  W = Worst time

Features:

Shows all 24 hours
Three traffic scenarios
Marks best/worst times
Human-readable x-axis (proper spacing for single/double-digit hours)
No ANSI codes (renders in Claude Code responses)

Colored ANSI Plot (for Terminal)

Same layout but with beautiful colors using plotext library.

---

Troubleshooting

API Errors

"Missing GOOGLE_MAPS_API_KEY" → Check .env file exists and has your key → Make sure you copied from .env.example: cp .env.example .env

"REQUEST_DENIED" → Enable APIs in Google Cloud Console (Geocoding, Directions, Maps Static) → Verify billing is active (you get $200/month free credit)

Performance Issues

Retrying failed queries multiple times → Some API requests failing due to rate limits → Script automatically retries with reduced concurrency → Wait for completion - most queries eventually succeed

Slow (> 10 seconds) → Network issues → Try again later → Default: 30 workers → 10 workers for retries (adjustable in code)

Installation Issues

"Module not found" when running MCP server → Run pip install -e . to install package in editable mode

"Permission denied" on .sh files → Run chmod +x test.sh test_custom.sh

Output files not saved → Check outputs/ folder exists (created automatically) → Verify path in --save-map flag

---

Cost

Very cheap!

Full 24-hour query (96 points): ~$0.001
$200/month free credit = ~200,000 queries
You won't hit limits during development or normal usage

---

For Hackathon Judges

What We Built

A production-ready MCP server for traffic analysis with:

✅ 120x speedup (10 min → 4 sec via parallel processing)
✅ Beautiful terminal visualizations (dual output: ANSI + plain text)
✅ Human-friendly AI behavior (considers sleep/work schedules)
✅ Smart retry logic (while loop with multiple rounds, only retries failures)
✅ Clean, publishable code (minimal necessary files, comprehensive docs)

Key Innovations

Two-tier response system

Quick answer first (< 10 sec)
Detailed analysis on request
Prevents slow, overwhelming responses

Human-realistic constraints

Don't recommend 2 AM departures
Consider work hours, sleep needs
Practical time windows

Dual output formats

Colored ANSI for terminal users
Simple text (+, o, *) for Claude Code responses
Solves ANSI rendering limitation

Robust API handling

Parallel requests with ThreadPoolExecutor
While loop retry (not just 2 passes)
Smart concurrency reduction (30 → 10 workers)
Graceful degradation on failures

Track

Track 1: Building MCP

This is a complete, production-ready MCP server that:

Exposes useful tools (geocode, eta_series, static_map)
Solves real problems (commute planning, meeting scheduling)
Works with both Claude Desktop and Claude Code
Has excellent documentation
Is ready to publish and use

---

Advanced Usage

Custom Time Range

python cli/driveplot_fast.py \
  --origin "Your Home" \
  --destination "Your Work" \
  --date 2025-11-20 \
  --start 06:00 --end 10:00 \  # Morning only
  --interval 30 \               # Every 30 min
  --ascii --yes

Different Intervals

15 min: Detailed analysis (96 data points, ~4 sec)
30 min: Balanced (48 points, ~2 sec)
60 min: Quick overview (24 points, ~1 sec)

Save Map to outputs/

python cli/driveplot_fast.py \
  --origin "Berkeley, CA" \
  --destination "San Francisco, CA" \
  --date 2025-11-20 \
  --ascii --save-map outputs/my_route.png --yes

---

Security

✅ API key protected:

Stored in .env (gitignored)
Never committed to version control
Single source of truth
Easy to rotate
Template provided (.env.example)

✅ No hardcoded secrets anywhere in codebase

✅ Best practices:

Environment variables via python-dotenv
Clear separation of code and config
Comprehensive .gitignore

---

Future Improvements

Ideas for extension:

Add Apple Maps support (already structured for it)
Historical data tracking (compare traffic patterns over time)
Calendar integration (auto-suggest departure times)
Multiple routes comparison (A vs B)
Web UI with Gradio (for Track 2 submission)

---

License

MIT License - use freely!

This is the standard license for MCP servers. Feel free to:

Use in commercial projects
Modify and redistribute
Incorporate into closed-source software

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Credits

Built for the Hugging Face MCP Hackathon (Track 1)

Technologies used:

FastMCP - MCP server framework
Google Maps Platform APIs - Geocoding, Directions, Static Maps
plotext - Terminal plotting
rich - Beautiful terminal output
ThreadPoolExecutor - Parallel API requests

---

Questions?

For issues or questions:

Check the Troubleshooting section above
Verify your .env file is set up correctly
Run ./test.sh to verify installation
Check that Google Maps APIs are enabled in Cloud Console

Drive-Time Plotter MCP