Namsor MCP for AI. Enrich Leads: Predict Identity Traits from Names

Q: What is the difference between predictorigin and predictcountry?

predictorigin guesses where the name was originally established (its source culture). predictcountry attempts to determine the most likely current country of residency.

Q: Does Namsor only work for US ethnicity predictions?

The predictethnicity tool is optimized for US-specific models (Hispanic, Asian, Black, White). For other global classifications, you'll need to use different tools or services.

Claude

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Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

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Namsor predicts demographic data directly from name strings. Use this MCP Server to analyze a full list of names and automatically enrich records with predicted gender, country of origin, ethnicity, and diaspora group.

It breaks down complex names and gives you probabilistic insights for lead scoring and marketing segmentation.

What your AI can do

Predict country

Estimates the current country of residency based on the provided name.

Predict diaspora

Predicts an individual's diaspora group or ethnic cluster for a given name and context.

Predict ethnicity

Calculates the predicted US ethnicity (Hispanic, Asian, Black, White) from a name string.

+ 3 more capabilities included

Parse Full Names

Breaks a single full name string into component parts like title, first name, last name, or suffix.

Predict Country Residency

Guesses the country where the person associated with the name currently lives.

Predict Diaspora Group

Identifies the predicted diaspora group or ethnic cluster for a given name in a specific region.

Predict US Ethnicity

Analyzes a name to predict US-specific ethnicity, including Hispanic, Asian, Black, and White categories.

Predict Gender

Provides the probability of the person's gender based on their first and last names.

Predict Country of Origin

Guesses the country where the name originally comes from.

Ask an AI about this

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AI Agent

Namsor MCP Server: 6 Tools for Name Data Prediction

These six tools let your AI agent analyze name strings to predict demographic traits like gender, country of origin, and ethnicity.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

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Predict Country

Estimates the current country of residency based on the provided name.

Predict Diaspora

Predicts an individual's diaspora group or ethnic cluster for a given name and...

Predict Ethnicity

Calculates the predicted US ethnicity (Hispanic, Asian, Black, White) from a name...

Predict Gender

Determines the likely gender of an individual based on their names and returns a...

Predict Origin

Predicts the country where the name was originally given or came from.

Parse Full Name

Separates a full name string into its distinct components, identifying titles, first, and last names.

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The Namsor integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "namsor": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the Namsor tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"namsor": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Namsor. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 6 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Manually cleaning up unstructured names shouldn't take an hour per batch.

Think about it: You get a CSV dump from a partnership. Names are all mashed together—titles, multi-part surnames, and initials mixed in. Before you can even start segmenting or scoring the lead, you have to spend half your time copying data into Google Sheets just to figure out which parts belong where. It's tedious, error-prone cleanup.

With Namsor, that whole process vanishes. You ask your agent to run `parse_full_name` on the column. The output immediately separates Title: Dr., First Name: Maria Elena, Last Name: Rodriguez-Garcia. You get perfectly structured data fields back in seconds.

Namsor MCP Server: Use name analytics to segment users by predicted ethnicity.

Before Namsor, if you wanted to target a specific demographic group (like Asian or Hispanic), you were stuck relying on self-reported data in forms, which is unreliable. You'd manually sort and filter thousands of names based on limited criteria.

Now, running `predict_ethnicity` allows your agent to analyze the name pattern itself. It provides a clear score for US ethnicities (Hispanic, Asian, Black, White), letting you segment users instantly without asking them anything.

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What your AI can actually do with this

Look, you got names. A big list of raw strings. You can’t just throw those at an AI agent and hope for the best—you need actual data points. This MCP Server is built to handle name analytics, giving your agent structured demographic insights from simple strings. It takes messy input and spits out actionable records.

When you connect this server, your AI client can run several distinct tools right off the bat. First up, if you've got a full name that looks like a mess—maybe it includes titles or suffixes—you use parse_full_name. This tool separates everything into its individual components, making sure you know exactly what constitutes the title, the first name, and the last name.

It cleans up the data structure immediately.

Next, you can predict where a person is from in two ways. You run predict_origin to figure out the country that originally gave the name; it gives you the birthplace context. Separately, if you want to know where they live right now, you use predict_country, which estimates their current country of residency based on the full name string.

For deeper demographic profiling, there's a whole suite of prediction tools. You run predict_gender to determine the likely gender of the individual; it doesn't just guess—it returns a concrete probability score alongside the result. When you need to categorize US ethnicity, you use predict_ethnicity, which calculates the likelihood that the name belongs to one of the major groups: Hispanic, Asian, Black, or White.

To get more specific cultural context, you've got predict_diaspora. This tool identifies a person's predicted diaspora group or ethnic cluster based on the provided name and any associated region. It gives you a layer of insight beyond just general ethnicity. And remember that grouping? You can run predict_country for residency and predict_origin for birth country, giving you two different geographic data points about one person.

When your agent uses these tools together, the workflow is straightforward. If you feed it a list of names, you don't just get back text; you get structured fields. You process raw leads and instantly enrich those records with multiple layers of demographic detail—gender probability, probable country of origin, current residency, predicted ethnicity group (US), and diaspora cluster.

This makes your data ready for anything: lead scoring models or marketing segmentation campaigns. It's all about taking ambiguity and turning it into concrete, usable data points.

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Server ID 019dd12b-a05b-70b8-a194-4ba827ec8e88

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Here's how it actually works

The bottom line is... your AI agent does the heavy lifting; you just provide the name and the key.

First, subscribe to the Namsor MCP Server and grab your API v2 Key.

Second, pass that key into your AI client (Claude, Cursor, etc.).

Third, ask your agent to run a specific tool—like predict_gender or parse_full_name—on your raw data.

Who is this actually for?

Data analysts, marketing operations specialists, and researcher teams need this. If your job involves cleaning up lead lists or scoring leads based on their background, you know the pain: manual data entry is slow, and guesswork kills segmentation campaigns. This server plugs directly into your existing AI workflow to automate that messy step.

Marketing Operations Manager

Runs batch enrichment jobs on newly acquired lead lists, using predict_country or predict_gender before passing data to the CRM.

Data Analyst

Needs to structure unstructured name fields for database entry. They run parse_full_name to get clean columns for title, first, and last names.

Market Researcher

Compares naming conventions across different regions by running tools like predict_diaspora or predict_ethnicity on sample populations.

What Changes When You Connect

Stop guessing on lead quality. By using predict_gender, you get a probability score (e.g., 98% Male), not just a guess, making your segmentation reliable.

Clean up messy databases instantly. The parse_full_name tool takes unstructured strings and turns them into usable columns for any CRM or database schema.

Segment markets by heritage. Run predict_ethnicity to filter out leads that don't fit your target demographics, improving campaign ROI.

Understand geographic reach. Use predict_origin and predict_country together to map where your potential customers actually come from versus where they live now.

Identify niche groups. The predict_diaspora tool lets you segment users by specific ethnic clusters, which is crucial for highly personalized outreach.

See it in action

01 01

Cleaning up a bulk lead import

A marketing ops manager gets a spreadsheet with thousands of names. Instead of manually checking each one, they prompt their agent: 'Run parse_full_name and then predict_gender on this column.' The agent returns the name broken down into Title/First/Last plus the gender probability score, ready for direct database import.

02 02

Validating a new market target

A researcher needs to know if a naming convention in a small town points to a specific cultural group. They use predict_diaspora combined with predict_origin on sample names, confirming the expected ethnic cluster and geographic source.

03 03

Optimizing regional ad targeting

A global sales team needs to know if a name suggests a user is physically located in their target zone. They run predict_country against a list of leads, instantly filtering out names that suggest residency outside the desired country.

04 04

Structuring complex academic data sets

A historian has historical records with very long, messy names (e.g., 'Dr. Maria Elena Rodriguez-Garcia'). They use parse_full_name to reliably separate the title, first name, and compound last surname before analysis.

The honest tradeoffs

Treating predictions as fact

Anti-pattern

Assuming that because Namsor says a name is 'Asian', you can treat it as definitive proof for marketing copy. This risks alienating users and generating inaccurate campaigns.

The Fix

Always use the probabilistic output. When running predict_ethnicity, don't just take the label; check the confidence score provided by your agent to know how reliable that data point is.

Ignoring name structure

Anti-pattern

Copy-pasting a full name into a single field and hoping your CRM or database can figure out which part is the first name, last name, or title.

The Fix

Always run parse_full_name first. It guarantees that every component—Title: Dr., First Name: Maria Elena, Last Name: Rodriguez-Garcia—is correctly isolated and structured.

Mixing prediction types

Anti-pattern

Trying to predict the origin of a name without considering its current residency. For instance, assuming a Brazilian national is always from Brazil.

The Fix

Use predict_origin for historical source data and then run predict_country separately to get the most likely current country of residency. The two tools give different information.

When It Fits, When It Doesn't

Use this Namsor server if your process requires enriching raw name strings with structured demographic or geographic metadata. For example, you need to know: 'What is the probability that Name X belongs to a Male individual who currently resides in Country Y?'

Don't use it if you are doing identity verification (you can't prove someone is from a place; you only get a prediction) or if your data is already perfectly structured. If all your fields are clean and separated, you don't need parse_full_name.

If your goal is purely linguistic analysis of phonetics without needing demographic context, other specialized NLP models might work better. But for general lead list cleanup and segmentation based on name patterns, Namsor provides the best set of tools.

Questions you might have

How accurate is Namsor's `predict_gender` tool? +

The service provides a probability metric (e.g., 95% Male), which lets you weigh the confidence of the prediction. It’s designed to guide your segmentation, not serve as an absolute fact.

What is the difference between `predict_origin` and `predict_country`? +

predict_origin guesses where the name was originally established (its source culture). predict_country attempts to determine the most likely current country of residency.

Can I use Namsor's `parse_full_name` on names with titles? +

Yes. The tool is specifically built for this. It correctly identifies and separates professional prefixes or academic titles, like 'Dr.' or 'Mr.', keeping your data clean.

Does Namsor only work for US ethnicity predictions? +

The predict_ethnicity tool is optimized for US-specific models (Hispanic, Asian, Black, White). For other global classifications, you'll need to use different tools or services.

What authentication steps are required to run the `predict_gender` tool? +

You need a valid Namsor API v2 Key. This key is generated in your account dashboard and must be passed to the MCP endpoint for every request. Without this credential, the agent will return an authorization failure.

If I hit rate limits while running `predict_origin`, how quickly can I retry? +

The service allows a certain number of calls per minute; exceeding that limit triggers a 429 status code. You must wait for the cooldown period to reset before retrying the call.

Can `parse_full_name` handle names with multiple middle names or suffixes? +

Yes, it handles complex structures by treating all components as distinct parts of speech. The output will provide separate fields for title, first name, and the full last name structure.

What should I expect if `predict_diaspora` fails to find a match? +

If no strong match is found, the tool returns null or an explicit 'No Match' status. This indicates that the provided name does not fit known diaspora patterns in the database.

Can I predict gender using only a name? +

Yes! Use the predict_gender tool. Provide the first and last name, and the agent will return the most likely gender and its probability.

Connect to your AI in seconds.

Predict country

Predict diaspora

Predict ethnicity

Namsor MCP Server: 6 Tools for Name Data Prediction

Make your AI actually useful.

Predict Country

Predict Diaspora

Predict Ethnicity

Predict Gender

Predict Origin

Parse Full Name

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

Manually cleaning up unstructured names shouldn't take an hour per batch.

Namsor MCP Server: Use name analytics to segment users by predicted ethnicity.

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Cleaning up a bulk lead import

Validating a new market target

Optimizing regional ad targeting

Structuring complex academic data sets

The honest tradeoffs

Treating predictions as fact

Ignoring name structure

Mixing prediction types

When It Fits, When It Doesn't

Questions you might have