Genderize MCP for AI. Predict Gender from Names, By Country.
Works with every AI agent you already use
…and any MCP-compatible client








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Genderize MCP predicts a person's likely gender based on their first name using global statistical data. This tool provides probability scores and localizes results by country, helping you quickly enrich lead lists or profile data.
It handles single names, bulk batches of up to 10 names, and supports specific regional checks for countries like the US, UK, and Brazil.
What your AI can do
Verify api connection
Runs a check to confirm the MCP's connection status with the underlying data source.
Estimate gender brazil
Specifically predicts the gender probability for names originating in Brazil.
Estimate gender spain
Predicts the gender probability of a single first name using data specific to Spain.
Get an immediate probability assessment for one first name.
Send up to 10 names at once and get all the gender predictions back in a single request.
Adjust name probability checks using specific ISO codes (e.g., US or FR) for accurate regional results.
Check if the MCP can successfully connect and communicate with the underlying data source.
Ask an AI about this
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Genderize: 8 Tools for Demographic Estimation
Use these tools to predict gender probabilities from first names. You can estimate single names, run bulk checks on multiple entries, or localize results by country.
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.
Start using Genderize on VinkiusVerify Api Connection
Runs a check to confirm the MCP's connection status with the underlying data source.
Estimate Gender Brazil
Specifically predicts the gender probability for names originating in Brazil.
Estimate Gender Spain
Predicts the gender probability of a single first name using data specific to Spain.
Estimate Gender France
Predicts the gender probability for names localized to France.
Estimate Gender Uk
Determines gender probabilities for names associated with the UK region.
Estimate Gender
Predicts the gender probability of a single first name using global data.
Estimate Gender Us
Predicts the gender probability of a single first name using US-specific data patterns.
Estimate Genders Bulk
Processes and predicts genders for multiple names in one batch request.
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Make Your AI Do More
Start with Genderize, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
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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 Genderize. 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 8 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually cleaning lead lists takes forever.
Today, if you get a new batch of leads in your CRM, the first thing is usually manually checking names. You open Excel, filter by country, and then spend hours cross-referencing name databases or calling colleagues just to confirm basic demographics. It's slow, error-prone, and only happens when someone has time.
With this MCP, you simply tell your agent the task—for example, 'Check these 10 names from Brazil.' The service handles the data lookups across its massive database in seconds. You get clean, statistically verifiable gender estimates back without leaving your chat window.
Get localized predictions with estimate_gender_us.
Without this MCP, if you have US leads and run a general name check, the results might be skewed because they don't account for specific American naming trends. You’d risk mis-segmenting your entire campaign based on bad data.
Now, by using `estimate_gender_us`, you force the tool to look only at patterns relevant to that region. This guarantees you get a result accurate for US leads—no guessing games.
What your AI can actually do with this
You've got a list of leads, but your CRM is missing gender markers. This MCP connects directly to a massive database containing over 114 million name records, letting you estimate gender probabilities right from your chat interface. It’s not just guessing; it provides statistical certainty scores (0.0 to 1.0) and tells you how many records informed the prediction.
You can even specify a country using ISO codes—this massively improves accuracy for regional naming patterns. Using this MCP through Vinkius means you don't have to leave your AI client or mess with an API key flow; you just ask, and it handles the data crunching. Need to run predictions on hundreds of names? Use the bulk function.
It makes cleaning up large datasets simple, turning a manual spreadsheet task into a quick conversation.
019d75a3-4089-73c4-8838-590c2d15df58 Here's how it actually works
The bottom line is, you talk to your agent like normal and it handles the database lookups automatically.
Connect this MCP through Vinkius to your preferred AI client.
Optionally, enter your API key into the settings for higher rate limits.
Ask your agent a direct question, like 'What is the predicted gender for John in the US?'
Who is this actually for?
Data Analysts who hate spending hours cleaning messy spreadsheets. Marketing Ops engineers whose job relies on accurate segmentation. Growth Managers needing quick demographic data for campaign targeting.
Needs to enrich a list of new leads with gender probabilities before passing them to the sales team.
Writes code or prompts that automatically validate user profile data based on known naming patterns and regional rules.
Manages client onboarding processes, requiring accurate gender estimates for initial segmentation reports.
What Changes When You Connect
Stop guessing gender. Use estimate_gender to get statistical certainty scores (0.0 to 1.0) for any name instantly.
Process entire lead lists fast. The estimate_genders_bulk tool handles up to 10 names in one go, saving huge amounts of time on data enrichment.
Get regional accuracy. Instead of general predictions, use specialized tools like estimate_gender_us or estimate_gender_brazil for perfect localization.
Maintain connection integrity. Run verify_api_connection to make sure your MCP is online and ready before a critical batch job runs.
It works through natural chat. You never need to worry about API calls or complex syntax; just talk to your agent.
See it in action
Cleaning up a global CRM export
A Marketing Ops engineer gets a list of 50 names from Brazil and the US. Instead of running two separate scripts, they prompt their agent: 'Predict gender for this batch of names using Brazilian and US data.' The agent uses estimate_genders_bulk combined with specialized tools like estimate_gender_brazil to return a complete, localized profile.
Validating user signup forms
A developer needs to build a conditional logic gate in their application. They prompt: 'Verify the name Sasha for Russia.' The agent uses estimate_gender_us (or another regional tool) to get an immediate, statistically backed answer, preventing bad data from entering the system.
Analyzing target demographics
A Growth Manager wants to know if a specific name is more likely male or female in the UK. They ask: 'What's the predicted gender for Emily in the UK?' The agent calls estimate_gender_uk and returns the probability, allowing them to adjust campaign targeting immediately.
Testing data pipeline reliability
A Data Analyst needs to ensure their entire naming pipeline is ready before a big launch. They run verify_api_connection first, confirming that the MCP can talk to the source database, eliminating integration risk.
The honest tradeoffs
Using general predictions for local data
Running 'What is the gender of Juan?' without specifying a country. This might give you a global guess that doesn't match reality in Spain.
Always use localized tools when possible. For names from Spain, run estimate_gender_spain. If it’s general US data, use estimate_gender_us.
Trying to process thousands of records manually
Copying and pasting 10 names into an external website or tool one by one because they don't know what bulk processing is.
Use the estimate_genders_bulk function. Feed it up to 10 names in a single prompt, and get all results back at once.
Assuming API access without checking
Writing a critical script that relies on gender data but never checks if the connection is actually live due to rate limiting or an outage.
Always run verify_api_connection first. This confirms the MCP can talk to the source before you build complex workflows around it.
When It Fits, When It Doesn't
Use this MCP if your primary need is statistically backed, localized gender estimation based on a name. You need high accuracy for segmentation or data enrichment (e.g., 'Is this lead likely female in France?'). If you only need to guess general demographics without regional context, this works fine. However, don't use it if the core data point you need is salary range, industry vertical, or company size; that requires a different type of lookup tool entirely. Also, if your naming patterns are based on cultural nuances not covered by its 114 million records (e.g., very niche tribal names), this MCP won't help—you'll have to find a custom data source.
Questions you might have
Is an API Key required for Genderize.io? +
No, you can use the service for free without an API key for up to 100 requests per day. For higher volume, you can obtain a key from genderize.io.
How accurate is the gender prediction? +
The API returns a 'probability' score between 0.0 and 1.0. A score of 0.99 means the API is 99% certain of the associated gender based on its database.
Can I localise the results for a specific country? +
Yes! Use the 'countryId' parameter with an ISO 3166-1 alpha-2 code (e.g., 'BR' for Brazil) to get results optimized for that specific region.
How many names can I check at once? +
The 'estimate_genders_bulk' tool allows you to check up to 10 names in a single API request, which is efficient for processing small lists.
What steps should I take if I'm troubleshooting my setup using the `verify_api_connection` tool? +
The verify_api_connection tool checks your API credentials and general service connectivity. If it fails, double-check that you entered a valid key in the Vinkius Marketplace settings or that your rate limit hasn't been exceeded.
If I need to process more than 10 names at once, how should I handle high volume requests with `estimate_genders_bulk`? +
You must use a paid API key for bulk processing. Once you upgrade your account and provide the key, the rate limits increase significantly, allowing you to send large lists of names efficiently.
When I run `estimate_gender`, what specific data points does my AI agent get back? +
Your agent receives the predicted gender (male/female), the statistical probability score (0.0 to 1.0), and the total record count used for that calculation. This gives you clear context on the prediction's reliability.
Do I have to use dedicated tools like `estimate_gender_brazil` instead of the main tool when checking regional names? +
It’s best practice to use the specialized country tools for maximum accuracy. These specific endpoints are built with local naming conventions, giving you better results than relying solely on the general name estimator.
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