# Namsor Alternative MCP > Namsor Alternative processes name strings to enrich data points like gender, country of origin, and US race/ethnicity. It also validates and formats international phone numbers, taking context from a person's full name for precise classification. ## Overview - **Category:** artificial-intelligence - **Price:** Free - **Tags:** data-enrichment, name-analysis, gender-detection, ethnicity-classification, onomastics, contact-validation ## Description When you connect this MCP to your agent, it treats raw names and contact details as structured data problems. Instead of just seeing 'John Smith,' the system tells you that John is likely male, originates in the US, and belongs to a specific ethnic group. It can also take messy full name strings and split them into clean components like first name and last name. For phone numbers, it doesn't just check length; it validates the number using the context of the person associated with it. This capability for deep data enrichment is what makes this MCP so valuable in any process that touches lead lists or customer profiles. You can build a robust data validation step right into your agent workflow by connecting to Vinkius, making messy inputs instantly usable. ## Tools ### get_gender Predicts the gender (male or female) associated with a given name. ### get_origin Identifies the most probable country of origin for any name provided. ### parse_name Splits complex full name strings into discrete first and last components. ### format_phone Validates an international phone number and formats it correctly for global use, using context. ### get_us_race_ethnicity Classifies a name according to specific US Census race/ethnicity categories. ## Prompt Examples **Prompt:** ``` What is the likely gender for the name Alex Johnson? ``` **Response:** ``` I've analyzed the name. 'Alex Johnson' is classified as likely Male with a high confidence score. ``` **Prompt:** ``` Identify the country of origin for the name 'Satoshi Nakamoto'. ``` **Response:** ``` Based on the name 'Satoshi Nakamoto', the most likely country of origin is Japan (JP). ``` **Prompt:** ``` Format the phone number 5551234567 for a person named Maria Garcia. ``` **Response:** ``` I've formatted the number using the context for Maria Garcia. The validated international format is +1 555-123-4567. ``` ## Capabilities ### Determine name demographics Predicts if a name is likely male or female and identifies the probable country of origin for any given name. ### Classify by US census categories Assigns names to specific US Race and Ethnicity classifications (White, Black, API, Hispanic). ### Structure full names Separates complex or poorly formatted full name strings into distinct first and last components. ### Validate and format phone numbers Checks an international phone number for validity and formats it correctly, using the person's name as context. ## Use Cases ### Cleaning up sales lead lists A Sales Ops Manager receives an Excel sheet of 5,000 leads with inconsistent formats. They ask their agent to run the entire list through `get_us_race_ethnicity` and `format_phone`. The result is a validated CSV where every name has structured data and every phone number is ready for immediate dialer integration. ### Personalizing global marketing emails A Marketing team needs to segment leads by cultural background. They pass names into the MCP, which uses `get_origin` and `get_gender`. This allows the agent to tailor specific email messaging that resonates with the predicted country or gender. ### Structuring unstructured database entries A Data Scientist pulls name strings from an old system. They use `parse_name` first, then pass the resulting components into `get_origin`. This automatically transforms vague text blobs into a structured record including the country code. ### Validating contact info before import Before importing data into a CRM, an engineer runs all phone numbers through `format_phone` and simultaneously checks the name using `get_gender`. This prevents bad data from entering the system and ensures accurate records. ## Benefits - Predict gender and origin instantly. Instead of guessing, you use `get_gender` or `get_origin` to assign demographic data points based solely on the name provided. - Clean up unstructured text fast. If your lead sheet has 'John Smith Jr.' combined in one cell, `parse_name` splits it into clean first and last names for reliable downstream processing. - Guarantee global contact quality. Don't send a badly formatted number. Use `format_phone` to validate and format any international phone number using the person's name context. - Meet compliance standards with demographics. Need to track ethnicity or race? The `get_us_race_ethnicity` tool classifies names against US Census categories, making your data richer for analysis. - Go from raw text to structured insight. By chaining tools like `parse_name` and then feeding the result into `get_origin`, you turn a simple string into multiple actionable data points. ## How It Works The bottom line is you get clean, validated, and deeply classified data back from messy inputs without having to write complex parsing code yourself. 1. Subscribe to this MCP on Vinkius and input your unique Namsor API Key. 2. Instruct your AI client to pass a raw name or contact record to the appropriate function, providing all necessary context (e.g., 'Analyze the name John Doe from India'). 3. The MCP executes the classification and formatting logic, returning structured, enriched data points that your agent can use immediately. ## Frequently Asked Questions **What can the Namsor Alternative MCP do with phone numbers?** The `format_phone` tool validates and formats international phone numbers. Crucially, it uses the context of a person's name to ensure the formatting is correct for that individual. **Does Namsor Alternative classify names by country?** Yes, the `get_origin` tool identifies the most probable country of origin (ISO2) for any given name string. It's useful for regional targeting in marketing campaigns. **Can I separate a full name into parts using Namsor Alternative MCP?** Absolutely. The `parse_name` tool takes complex full name strings and reliably splits them into structured first and last name components, cleaning up data for further use. **How does the get_us_race_ethnicity tool work?** It classifies names according to specific US Census race and ethnicity categories. This is useful for data science projects requiring demographic segmentation of a population list. **Is Namsor Alternative MCP just an API wrapper?** No, it’s an integrated toolset within the Vinkius catalog that your AI agent calls. It allows you to trigger complex data enrichment workflows (like parsing and then getting origin) through natural conversation.