boston-harbor-ferries

boston-harbor-ferries

MCP server to track Boston Harbor commuter ferries in real-time, providing vessel positions, routes, and schedules.

Category
Visit Server

README

Boston Harbor Ferries

PyPI version Python 3.10+ License: MIT Code style: ruff APRS.fi

APRS-based Boston Harbor commuter ferry tracker with MCP server support.

Tightly scoped to track Seaport Ferry vessels operating in Boston Harbor.

Data provided by aprs.fi - https://aprs.fi

Features

  • Track all 4 Seaport Ferry vessels in real-time
  • In-memory caching with 120-second TTL (respects aprs.fi API terms)
  • Rate limiting: 10 requests/minute (configurable)
  • Rich CLI with beautiful terminal output
  • MCP server for integration with Claude Code and other AI assistants
  • Run with uvx (no installation required)
  • Live position tracking and historical data export

Tracked Vessels

Seaport Ferry - North Station Route

  • PHILLIS WHEATLEY (MMSI: 368227350)
  • SAMUEL WHITTEMORE (MMSI: 368227370)
  • COMMONWEALTH (MMSI: 368351390)

Route: LoveJoy Wharf (North Station) ↔ Fan Pier (Seaport) ↔ Pier 10 Travel time: ~30 minutes

Seaport Ferry - East Boston Route

Route: Lewis Mall Wharf (East Boston) ↔ Fan Pier (Seaport) Travel time: ~10 minutes

🚒 Latest Position: 42.351318Β°N, 71.038918Β°W (Course: 339Β°) πŸ“Š View 24-Day Activity Analysis - Speed patterns, hourly heatmaps, route visualization πŸ“ Live Position Data - Real-time JSON feed

Historical Data: 4,777 positions over 24 days (Sep 15 - Oct 10) β€’ Avg speed: 12.4kn β€’ Operating hours: 05:00-19:00

πŸ“Š Activity Heatmap (Last 2 Weeks)

Activity by hour - darker blocks indicate more position reports:

      00  04  08  12  16  20
      β”‚   β”‚   β”‚   β”‚   β”‚   β”‚
09-29 β”‚    β–‘β–“β–ˆβ–ˆβ–ˆβ–ˆβ–’ β–‘β–’β–‘β–ˆβ–ˆβ–ˆβ–“β–’
09-30 β”‚    β–‘β–“β–ˆβ–ˆβ–ˆβ–ˆβ–‘  β–’β–“β–ˆβ–ˆβ–ˆβ–ˆβ–’
10-01 β”‚     β–’β–ˆβ–ˆβ–ˆβ–ˆβ–’    β–ˆβ–ˆβ–ˆβ–ˆβ–’
10-02 β”‚    β–‘β–“β–ˆβ–ˆβ–ˆβ–ˆβ–‘    β–ˆβ–ˆβ–ˆβ–ˆβ–’
10-03 β”‚     β–“β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–“β–‘β–’β–’β–ˆβ–ˆβ–ˆβ–ˆβ–’
10-06 β”‚    β–‘β–’β–ˆβ–ˆβ–ˆβ–ˆβ–“β–‘β–’β–’β–’β–ˆβ–ˆβ–ˆβ–ˆβ–’
10-07 β”‚    β–‘β–“β–ˆβ–ˆβ–ˆβ–ˆβ–’   β–‘β–ˆβ–ˆβ–ˆβ–ˆβ–’
10-08 β”‚     β–“β–ˆβ–‘β–“β–ˆβ–‘   β–‘β–ˆβ–ˆβ–ˆβ–ˆβ–’
10-09 β”‚    β–‘β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–’β–’β–ˆβ–‘β–“β–ˆβ–ˆβ–ˆβ–ˆβ–‘
10-10 β”‚    β–‘β–“β–ˆβ–ˆβ–ˆβ–ˆβ–’

Legend: β–‘ Low β–’ Medium β–“ High β–ˆ Peak Pattern: Clear AM (06-10) and PM (15-18) commute peaks

🚒 Speed Distribution

Speed (knots) β”‚ Frequency
──────────────┼──────────────────────────────────────────────────
        2- 4 β”‚ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 188
        4- 6 β”‚ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 202
        6- 8 β”‚ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 148
        8-10 β”‚ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 176
       10-12 β”‚ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 229
       12-14 β”‚ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 349
       14-16 β”‚ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 673
       16-18 β”‚ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 1123
       18-20 β”‚ β–ˆβ–ˆβ–ˆ 93
       20-22 β”‚  21

Peak Speed: 16-18 knots (75% of time cruising >10kn)

πŸ—ΊοΈ Route Map

           Boston Harbor
    ╔══════════════════════╗
    β•‘                      β•‘

  Lewis Mall Wharf    Fan Pier
   (East Boston)      (Seaport)
         β”‚                β”‚
         β”‚   ~10 min      β”‚
         β”‚   8-12 kn      β”‚
         β””β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                 β”‚
            CRISPUS ATTUCKS
            (90 passengers)

Operating: 05:00-19:00 daily β€’ Peak: 06-10, 15-18


Installation

Run with uvx (recommended)

# Set your API key
export APRS_API_KEY="your-key-from-aprs.fi"

# Run commands directly
uvx --from . harbor-ferry list-vessels
uvx --from . harbor-ferry track 368157410
uvx --from . harbor-ferry track-all

Install in development mode

cd boston_harbor_ferries
pip install -e .

Configuration

Get your free API key from https://aprs.fi (requires registration).

Set the API key via environment variable:

export APRS_API_KEY="your-api-key-here"

Or create a .env file:

APRS_API_KEY=your-api-key-here
APRS_CACHE_TTL_SECONDS=120
APRS_MAX_REQUESTS_PER_MINUTE=10

CLI Usage

# List all known ferries
harbor-ferry list-vessels

# Show routes and schedules
harbor-ferry routes

# Track a specific ferry
harbor-ferry track 368157410

# Track all ferries
harbor-ferry track-all

# Force fresh data (bypass cache)
harbor-ferry track 368157410 --no-cache

# Cache management
harbor-ferry cache-info
harbor-ferry clear-cache

MCP Server Usage

The MCP server allows AI assistants like Claude Code to track ferries in real-time.

Quick Start

# Test the MCP server
gmake test-mcp

# Expected output:
# βœ“ MCP server responding to JSON-RPC
# Tools: list_ferries, track_ferry, track_all_ferries, get_ferry_routes, clear_cache

Available MCP Tools

  • list_ferries - List all known Boston Harbor ferries with details
  • get_ferry_routes - Get route information and schedules
  • track_ferry - Track specific ferry by MMSI number
  • track_all_ferries - Get all ferry positions at once
  • clear_cache - Force fresh data (bypasses 2-minute cache)

Claude Code/Desktop Integration

See docs/CLAUDE_CODE_INTEGRATION.md for complete integration guide.

Adding to Claude Code

From within this repository directory:

# Add the MCP server to Claude Code
claude mcp add-json boston-harbor-ferries \
  '{"command":"uv","args":["run","python","-m","boston_harbor_ferries.mcp_server"],"env":{"APRS_API_KEY":"'"${APRS_API_KEY}"'"}}'

# Verify it's connected
claude mcp list
# Should show: boston-harbor-ferries - βœ“ Connected

# View available tools
claude mcp get boston-harbor-ferries

Claude Desktop Configuration

For global access in all Claude Desktop conversations:

Linux: ~/.config/Claude/claude_desktop_config.json macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "boston-harbor-ferries": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/full/path/to/boston-harbor-ferries",
        "python",
        "-m",
        "boston_harbor_ferries.mcp_server"
      ],
      "env": {
        "APRS_API_KEY": "your-api-key-here"
      }
    }
  }
}

Example Questions for Claude

Once configured, you can ask Claude:

  • "Where is CRISPUS ATTUCKS right now?"
  • "Show me all active ferries"
  • "What are the ferry routes in Boston Harbor?"
  • "Clear the cache and check ferry positions again"

Understanding Position Data

Operating Hours: Ferries transmit APRS positions during service hours (05:00-19:00 daily)

  • Peak times: 06:00-10:00 (morning) and 15:00-18:00 (evening)
  • Off-hours: No position data when ferries are not operating
  • Data age: Position reports update every 1-2 minutes when active

If you see old position data (>1 hour), the ferry is likely docked between runs or service has ended for the day. Check during peak commute hours for live tracking.

All 6 MCP tools tested and working βœ… (see gmake test)

Bonus: FreeBSD Leave Reminder MCP Server

This repository also includes a standalone MCP server that wraps FreeBSD's leave(1) command for setting reminders.

See LEAVE_MCP.md for complete documentation.

Quick test:

gmake test-leave-mcp

Tools available:

  • set_reminder - Set a reminder for a specific time or duration
  • check_reminders - View active reminders
  • cancel_reminders - Cancel all reminders

Example: "Remind me to leave in 30 minutes" β†’ Uses +0030 format

Python API

from boston_harbor_ferries import APRSClient, VESSELS

# Initialize client (loads API key from env)
with APRSClient() as client:
    # Track specific ferry
    position = client.get_vessel_position("368157410")
    if position:
        print(f"{position.vessel.name} at {position.latitude}, {position.longitude}")

    # Track all ferries
    positions = client.get_all_ferries()
    for pos in positions:
        print(f"{pos.vessel.name}: {pos.age_seconds:.0f}s old")

API Terms Compliance

This tool complies with aprs.fi API terms of service:

  • βœ… Credits aprs.fi as data source in all output
  • βœ… Provides link back to aprs.fi
  • βœ… Free to use for all users
  • βœ… Includes User-Agent header with app name/version
  • βœ… Each user uses their own API key
  • βœ… Built-in rate limiting (10 req/min default)
  • βœ… Intelligent caching (2 min TTL default)
  • βœ… Only queries when actively needed (no background polling)

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Type checking
mypy boston_harbor_ferries

License

MIT

Acknowledgments

Data provided by aprs.fi - Hessu's excellent APRS infrastructure service.

Ferry service operated by Seaport Ferry.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured