py-nameplate 1.0

MCP server and library for parsing US addresses and names into structured components

py-nameplate

A Python library, MCP server, and REST API for parsing unstructured US contact strings into structured components.

The Problem

You have messy contact data:

Dr. John Smith Jr. 742 Evergreen Terrace
JANE DOE 123 MAIN ST APT 2B BOSTON MA 02101
Smith, Robert "Bob" 456 Oak Ave, Chicago, IL 60601

You need structured data you can actually use.

The Solution

One function that handles it all:

from nameplate import parse

result = parse("Dr. John Smith Jr. 742 Evergreen Terrace, Springfield, IL 62701")

# Name components
result.name.prefix      # "Dr."
result.name.first       # "John"
result.name.last        # "Smith"
result.name.suffix      # "Jr."

# Address components
result.address.street_number  # "742"
result.address.street_name    # "Evergreen"
result.address.street_type    # "Terrace"
result.address.city           # "Springfield"
result.address.state          # "IL"

result.input_type       # "contact"
result.validated        # True (city/state in database)

How It Works

The parse() function uses token-based segmentation to automatically find the boundary between name and address:

Dr. John Smith Jr. 742 Evergreen Terrace
└───── name ─────┘ └────── address ─────┘

Segmentation algorithm:

1. Tokenize input into words
2. Scan for first numeric token that isn't a name suffix (III, 1ST, etc.)
3. Verify remaining tokens contain street indicators (St, Ave, ZIP, state, etc.)
4. Split at that boundary

Address parsing works backwards from the end:

• Extract ZIP code (5 or 9 digits)
• Extract state (2-letter code)
• Extract city (validated against database)
• Extract unit (Apt, Suite, #)
• Remaining tokens are street components

Street-based enhancement fills in missing city/state:

• If address has a street but no city, look up the street in the database
• If street exists in exactly one location, auto-fill city and state
• Common streets like "Main Street" exist in many cities and won't enhance

Installation

pip install py-nameplate

Or with uv:

uv add py-nameplate

Usage

Basic Parsing

from nameplate import parse

# Auto-detects input type
result = parse("123 Main St, Boston, MA 02101")
result.input_type  # "address"

result = parse("Dr. Jane Doe")
result.input_type  # "name"

result = parse("John Smith 123 Main St, Boston, MA 02101")
result.input_type  # "contact"

Enhancement

# Without enhancement - street alone has no city/state
result = parse("100 Dunwoody Club Dr")
result.address.city   # ""
result.address.state  # ""

# With enhancement - city/state auto-filled if street is unique in database
result = parse("100 Dunwoody Club Dr", enhance=True)
result.address.city   # "Atlanta" (auto-filled)
result.address.state  # "GA" (auto-filled)
result.enhanced       # True

Normalization

# Smart title case
result = parse("PATRICK O'BRIEN 123 MAIN ST", normalize=True)
result.name.last       # "O'Brien" (not "O'brien")
result.address.city    # "Boston" (not "BOSTON")

result = parse("RONALD MCDONALD", normalize=True)
result.name.last  # "McDonald" (not "Mcdonald")

Batch Processing

from nameplate import parse_batch

texts = [
    "Dr. John Smith",
    "123 Main St, Boston, MA 02101",
    "Jane Doe 456 Oak Ave, Chicago, IL 60601",
]
result = parse_batch(texts, enhance=True)
result.total           # 3
result.parsed_count    # 3
result.enhanced_count  # number with enhanced data

Supported Formats

Names

Format	Example
Simple	John Smith
With prefix	Dr. Jane Doe, Lt. Col. John Smith
With suffix	John Smith Jr., Jane Doe PhD
Last, First	Smith, John
With nickname	Robert "Bob" Smith
Name particles	Ludwig van Beethoven, Juan de la Vega
Roman numerals	Henry Ford III

Addresses

Format	Example
Standard	123 Main St, Boston, MA 02101
With unit	456 Oak Ave Apt 2B, Chicago, IL 60601
PO Box	PO Box 789, Miami, FL 33101
Directional	100 N Main St, Denver, CO 80202
ZIP+4	123 Main St, Boston, MA 02101-1234

Contacts

Any combination of name followed by address:

John Smith 123 Main St, Boston, MA 02101
Dr. Jane Doe Jr. PO Box 456, Seattle, WA 98101

MCP Server

Use with Claude Desktop or Claude.ai as an MCP tool.

Local (uvx)

Add to ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "nameplate": {
      "command": "uvx",
      "args": ["nameplate"]
    }
  }
}

Hosted

{
  "mcpServers": {
    "nameplate": {
      "type": "url",
      "url": "https://nameplate.mcp.danheskett.com/"
    }
  }
}

Available Tools

Tool	Description
parse	Parse any input with auto-detection and optional enhancement
parse_batch	Batch parse multiple inputs

Example Prompts

"Parse this: Dr. John Smith 742 Evergreen Terrace, Springfield, IL"

"Parse with enhancement: Jane Doe 100 Dunwoody Club Dr"

"Parse these contacts: John Smith, 123 Main St Boston MA, Jane Doe 456 Oak Ave Chicago IL"

REST API

Use the REST API for direct HTTP access without MCP.

Base URL: https://nameplate.mcp.danheskett.com

Endpoints

Endpoint	Method	Description
/api/parse	POST	Parse a single input
/api/parse/batch	POST	Parse multiple inputs
/health	GET	Health check

Request Format

{
  "text": "Dr. John Smith 123 Main St, Boston, MA 02101",
  "normalize": false,
  "enhance": false
}

For batch requests, use texts (array) instead of text:

{
  "texts": ["John Smith", "123 Main St, Boston, MA 02101"],
  "normalize": true,
  "enhance": true
}

Examples

Basic parse:

curl -X POST https://nameplate.mcp.danheskett.com/api/parse \
  -H "Content-Type: application/json" \
  -d '{"text": "Dr. John Smith 123 Main St, Boston, MA 02101"}'

Parse with enhancement:

curl -X POST https://nameplate.mcp.danheskett.com/api/parse \
  -H "Content-Type: application/json" \
  -d '{"text": "Jane Doe 100 Dunwoody Club Dr", "enhance": true}'

Batch parsing:

curl -X POST https://nameplate.mcp.danheskett.com/api/parse/batch \
  -H "Content-Type: application/json" \
  -d '{"texts": ["John Smith", "123 Main St, Boston, MA 02101"], "normalize": true}'

Health check:

curl https://nameplate.mcp.danheskett.com/health

Python API Reference

parse(text, normalize=False, enhance=False) -> ParseOutput

Parameter	Type	Description
text	str	Input string to parse
normalize	bool	Apply smart title case
enhance	bool	Fill in missing data from database

ParseOutput

Field	Type	Description
input_type	str	"name", "address", or "contact"
name	NameOutput	Parsed name components
address	AddressOutput	Parsed address components
parsed	bool	True if parsing succeeded
validated	bool	True if city/state found in database
enhanced	bool	True if data was enhanced
enhanced_fields	list[str]	Fields that were enhanced
errors	list[str]	Any parsing errors

NameOutput

Field	Type	Description
prefix	str	Dr., Mr., Mrs., Rev., etc.
first	str	First/given name
middle	str	Middle name(s)
last	str	Last/family name
suffix	str	Jr., Sr., III, PhD, etc.
nickname	str	Nickname if present

AddressOutput

Field	Type	Description
street_number	str	House/building number
street_name	str	Street name
street_type	str	St, Ave, Blvd, etc.
street_direction	str	N, S, E, W, etc.
unit_type	str	Apt, Suite, Unit, etc.
unit_number	str	Unit/apartment number
city	str	City name
state	str	Two-letter state code
zip_code	str	5 or 9 digit ZIP

Data Sources

• US Cities: 29,880 city/state combinations from kelvins/US-Cities-Database (MIT)
• Street Names: 500k+ street/location mappings from USGS (ODbL)

Development

git clone https://github.com/dannyheskett/py-nameplate.git
cd py-nameplate
uv sync --extra dev

# Run tests
uv run pytest

# Lint
uv run ruff check src/ tests/
uv run ruff format src/ tests/

Privacy

The hosted MCP server does not store, log, or retain any data. All parsing happens in memory. See the source code to verify.

License

BSD-3-Clause