genderize.io valid
Genderize.io
Genderize.io is a REST API that predicts the gender of a person from their name, returning a probability score and the number of data rows examined. It is operated by Demografix ApS (Denmark) and is part of a suite of three name-based demographic APIs.
For agents: REST API predicting a person's gender from a first or full name with probability score; documented API reference, examples and MCP server.
gender predictionname apirest apidemographic apimcp server
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-21T14:11:22.762Z. Cached copy · Source on genderize.io
# Genderize.io
> Genderize.io is a REST API that predicts the gender of a person from their name, returning a probability score and the number of data rows examined. It is operated by Demografix ApS (Denmark) and is part of a suite of three name-based demographic APIs.
Genderize.io has been in operation for more than 12 years and bases its predictions on a dataset of over 908 million data rows covering 240+ countries and territories. It has been cited in over 1,400 academic papers (Google Scholar) and is used by organizations including The Guardian, MIT, Uber, Columbia University, Shopify, Nature, The Washington Post, and Yale University.
Important: Genderize.io predicts statistical gender likelihood — it does not determine biological sex or gender identity. Results are probabilistic, not deterministic.
## Method properties
- Every prediction carries a probability and a `count`, the number of data rows behind it, so results can be thresholded and uncertainty reported rather than assumed.
- Output is reproducible. The same name and country return the same prediction and the same count.
- Predictions are scoped by country from per-country counts, which is why `kim` resolves male in DK and female in US.
- Accuracy is published per prediction at https://genderize.io/our-data, so the method can be checked independently.
## Documentation
- [Documentation Hub](https://genderize.io/documentation): Overview of all integration surfaces — API, MCP server, spreadsheet tool, and no-code workflows
## API
- [API Reference](https://genderize.io/documentation/api/reference): Full API reference with request/response formats, parameters, error codes, and examples
- [Name handling](https://genderize.io/documentation#name-handling): Send a first name or a full name; full names give the most accurate predictions because every part of the name carries signal. Name parts are identified by evidence, not position, and titles, initials, and accents are handled.
- [Example request](https://api.genderize.io?name=peter): `GET https://api.genderize.io?name=peter` returns `{"count":1352696,"name":"peter","gender":"male","probability":1.0}`
- [Country-scoped request](https://api.genderize.io?name=kim&country_id=dk): `GET https://api.genderize.io?name=kim&country_id=dk` returns `{"count":196601,"name":"kim","gender":"male","country_id":"DK","probability":0.97}` — while `?name=kim&country_id=us` returns female (0.94). Use `country_id` (ISO 3166-1 alpha-2) to scope predictions to a specific country.
- [Batch requests](https://genderize.io/documentation): Up to 100 names per request using `name[]=` parameter. Each name counts as one lookup against your quota.
- [Our Data](https://genderize.io/our-data): Open accuracy benchmarks — the live Genderize API scored against public datasets with a known gender for every person. 96.37% accuracy on 88,153 Olympic athletes (country-scoped), with every individual prediction published so the numbers can be checked.
## Pricing
- [Prici
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