Australian Postcodes MCP Server

A high-performance MCP (Model Context Protocol) server providing Australian postcode and suburb data with intelligent fuzzy matching, designed specifically for AI assistants handling customer service interactions.

Features

🔍 Core Search Capabilities

• Postcode to Suburbs: Find all suburbs for a given postcode
• Suburb to Postcodes: Find postcodes for suburbs (with fuzzy matching)
• Smart Validation: Verify suburb-postcode combinations
• LGA Queries: List all suburbs in a Local Government Area (city/council)

🎯 AI-Optimized Features

• Fuzzy Matching: Handles typos and misspellings with confidence scoring
• Phonetic Search: Matches spoken names that may be misheard
• Autocomplete: Suggests completions for partial suburb names
• Smart Suggestions: Provides alternatives when exact matches aren't found
• Geographic Search: Find nearby suburbs within a radius

📊 Data Coverage

• ~17,000 Australian postcodes and suburbs
• Local Government Areas (LGAs)
• Statistical Areas (SA3/SA4)
• Geographic coordinates (latitude/longitude)
• Electoral divisions
• State and region information

Quick Start

Installation

# Clone the repository
git clone https://github.com/yourusername/australian-postcodes-mcp.git
cd australian-postcodes-mcp

# Install dependencies
pip install -r requirements.txt

# Import postcode data
python src/utils/data_loader.py

# Test locally
fastmcp dev src/server.py

Usage with Claude Desktop

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "australian-postcodes": {
      "command": "fastmcp",
      "args": ["run", "/path/to/australian-postcodes-mcp/src/server.py"]
    }
  }
}

Available Tools

Search Tools

• `search_by_postcode` - Find suburbs for a postcode
• `search_by_suburb` - Find postcodes for a suburb
• `validate_suburb_postcode` - Verify a combination is valid
• `get_location_details` - Smart search accepting either postcode or suburb

Fuzzy Matching Tools

• `find_similar_suburbs` - Find closest matches for misspelled suburbs
• `autocomplete_suburb` - Get completions for partial names
• `validate_spelling` - Suggest spelling corrections
• `phonetic_search` - Handle phone-misheard names

Location Tools

• `list_suburbs_in_lga` - All suburbs in a Local Government Area
• `find_lga_for_suburb` - Get the LGA/city for a suburb
• `list_suburbs_in_radius` - Find nearby postcodes within radius
• `get_neighboring_suburbs` - Find adjacent areas

Analytics Tools

• `get_state_statistics` - Postcode and suburb counts by state
• `list_all_lgas` - Available Local Government Areas
• `search_by_region` - Query by statistical area

Examples

Basic Search

# Find suburbs for postcode 2300
result = await search_by_postcode("2300")
# Returns: Newcastle, Newcastle West, etc.

# Find postcode for suburb
result = await search_by_suburb("Newcastle", state="NSW")
# Returns: 2300

Fuzzy Matching

# Handle misspellings
result = await find_similar_suburbs("Newcaslte", state="NSW")
# Returns: Newcastle (confidence: 0.92), New Lambton (confidence: 0.75)

# Phonetic search
result = await phonetic_search("new castle")
# Returns: Newcastle, Newcastle West

LGA Queries

# List suburbs in Newcastle LGA
result = await list_suburbs_in_lga("Newcastle", state="NSW")
# Returns: All suburbs in Newcastle city council area

Deployment

FastMCP Cloud (Recommended)

1. Push to GitHub

2. Connect repository at fastmcp.cloud

3. Configure environment variables

4. Deploy with one click

Local Development

# Run development server
fastmcp dev src/server.py

# Run production server
python src/server.py

Data Source

Data sourced from the community-maintained Australian Postcodes repository.

Performance

• SQLite with optimized indexes for fast queries
• Sub-100ms response time for most queries
• Efficient fuzzy matching using rapidfuzz library
• Cached phonetic encodings for voice queries

Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT License - See LICENSE for details.

Support

For issues or questions, please open an issue on GitHub or contact the maintainers.

Built with FastMCP for the Model Context Protocol