# Namsor MCP for AI Agents AI Agent Connect > Namsor MCP lets your AI agent predict gender, origin, and ethnicity from names. It turns raw name strings into structured demographic data for lead enrichment and marketing personalization. ## Overview - **Category:** marketing-automation - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_7qlG4Y48MpPkJSSv65uwwU2yGlq3T7F1Mc2D5Dj3/ai-agent-connect - **Tags:** demographic-analysis, gender-prediction, ethnicity-tracking, data-enrichment, personalization ## Description Namsor lets you turn a list of names into a structured demographic database without doing any manual research. If you're looking at a pile of leads and need to know who you're actually talking to, this Connector handles the heavy lifting. You can ask your agent to figure out things like gender, country of origin, or even specific ethnicity for any name you throw at it. It's built to handle the nuance of different cultures, giving you high-probability scores rather than just guesses. It also cleans up your data by breaking down full names into first, last, and middle components so your CRM actually stays organized. You get to stop guessing and start using real data to personalize your outreach. It's a straightforward way to get more out of your customer data, and it's easy to get running through the Vinkius catalog. ## Tools ### predict_country Predicts which country a person likely lives in based on their name. It helps you target regional ads more accurately. ### predict_diaspora Identifies specific diaspora groups or ethnic clusters within a country. This is great for niche marketing. ### predict_ethnicity Predicts US-specific ethnicity like Hispanic, Asian, or White. Use this for localized demographic reporting. ### predict_gender Predicts gender from a name and gives you a probability score. It helps you tailor your messaging to the right audience. ### predict_origin Predicts the country of origin for a name. This helps you understand a customer's background for better personalization. ### parse_full_name Breaks a full name string into first, last, prefix, and suffix components. It keeps your database organized and searchable. ## Prompt Examples **Prompt:** ``` Predict the gender for 'Jean Dupont'. ``` **Response:** ``` Based on the analysis, 'Jean Dupont' is predicted as: - **Gender**: Male - **Probability**: 98% - **Context**: High confidence for France. ``` **Prompt:** ``` What's the origin for Yuki Tanaka? ``` **Response:** ``` The name Yuki Tanaka shows: - **Origin**: Japan - **Confidence**: 97.2% - **Region**: East Asia ``` **Prompt:** ``` Parse 'Dr. Maria Elena Rodriguez-Garcia'. ``` **Response:** ``` Parsed Name Components: - **Title**: Dr. - **First Name**: Maria Elena - **Last Name**: Rodriguez-Garcia - **Notes**: Hispanic compound surname detected. ``` ## Capabilities ### Predict name gender Get gender predictions and probability scores from any first or last name. ### Identify country of origin Determine where a person is likely from based on their name. ### Detect current residency Predict which country a person currently lives in. ### Track US ethnicity Get localized demographic insights for US-specific ethnicity models. ### Identify diaspora groups Predict specific ethnic clusters or diaspora groups within a country. ### Break down full names Split complex name strings into structured first, last, prefix, and suffix components. ## Use Cases ### Personalizing Cold Outreach A salesperson asks the agent to find the likely country of origin for a list of 100 leads to adjust the tone of the emails. ### CRM Data Cleanup A database manager gives the agent a messy list of names and asks it to parse them into first and last name columns. ### Market Research A brand strategist asks the agent to predict the ethnicity of a new user base to see if they match their target demographic. ### Lead Scoring A marketing team uses predict_gender to filter out certain demographics from a high-value product launch. ## Benefits - Get accurate gender probabilities for your leads using predict_gender instead of guessing. - Clean up your CRM automatically with parse_full_name to separate titles and suffixes. - Target your ads more effectively by using predict_country to see where your leads live. - Understand your audience's background with predict_origin and predict_diaspora data. - Segment your US audience by ethnicity using predict_ethnicity for better localization. ## How It Works The bottom line is you get instant, high-accuracy demographic data for any name string without manual research. 1. Subscribe to the Namsor MCP on Vinkius. 2. Add your Namsor API v2 Key to the configuration. 3. Ask your agent to enrich names or predict demographics in any compatible client. ## Frequently Asked Questions **What does Namsor MCP do for my marketing data?** It predicts demographics like gender, country of origin, and ethnicity from name strings. This helps you personalize your outreach and understand your audience better. **Can Namsor MCP tell me where a lead lives?** Yes, it predicts the likely country of residency based on the name provided. This is useful for regional targeting. **How does Namsor MCP help with CRM cleanup?** It parses full names into structured components like first name, last name, and titles. This keeps your database clean and searchable. **Is Namsor MCP accurate for gender prediction?** It provides high-probability scores based on onomastic analysis, giving you a much more reliable metric than manual guessing. **Can I use Namsor MCP to find US ethnicity?** Yes, it includes specific models to predict US ethnicity, such as Hispanic, Asian, and White, for localized insights. **Does Namsor MCP work with my current AI client?** It works with any MCP-compatible client like Claude, Cursor, or Windsurf once you connect it via Vinkius. **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.