Gender Detection Skill

Overview

Gender Detector works by analyzing the name provided and predicting the likely gender of the person. It uses a machine learning model trained on a large dataset of names and their associated genders to make the prediction.

Live Test Gender Detection Skill Skill →

The tool

Once your client is connected to the VerveKit server, this appears in its tool list as GenderDetectionSkill. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

Tool call

{
  "name": "GenderDetectionSkill",
  "arguments": {
    "name": "John Mcdonald"
  }
}

You do not name the tool yourself; the model picks it. Asking about John Mcdonald in the terms this skill covers is enough for it to reach for GenderDetectionSkill on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every skill in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervekit": {
      "url": "https://api.vervekit.com/v1/mcp"
    }
  }
}

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

Argument	Type	Description
`name`Required	string	The name for which you want to detect the gender
`country`Optional	string	The country code for the name (e.g., US). Recorded and echoed back in the response, but detection does not yet vary by country - the same name returns the same gender whichever code you pass default us · length 2–2

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

Result

{
  "status": "ok",
  "error": null,
  "data": {
    "name": "John Mcdonald",
    "country": "US",
    "detected": true,
    "gender": "male"
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Field	Type	Example	Description
`name`	string	`John Mcdonald`	The input name submitted for gender detection analysis
`country`	string	`US`	The country code as submitted, echoed back. Detection does not yet vary by country
`detected`	boolean	`true`	Always true on a successful response - a name the detector cannot place returns a 404 rather than detected: false
`gender`	string	`male`	The predicted gender of the person based on name analysis

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

Status	What it means
`400` / `422`	The arguments did not validate. The message names the offending one.
`401`	The OAuth session is invalid or expired — reconnect the server.
`403`	Blocked by a key restriction or an IP allow-list. Never a bad identity.
`404`	This skill is not part of VerveKit. Check the catalog.
`429`	Out of credits, or a brief rate limit. The message tells them apart.

A call costs 2 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Email Salutation Selection: When drafting automated outreach, sales platforms choose correct honorifics and greeting styles based on the predicted gender returned for each recipient.
Account Profile Onboarding: Assign default avatars and profile pronouns automatically when a user signs up by matching their entered first name against regional gender predictions.
Audience Demographic Audits: To evaluate subscriber breakdowns without intrusive survey forms, newsletter editors tabulate aggregate gender distributions across their imported subscriber lists.
Personalized Catalog Navigation: Ecommerce fashion apps preselect relevant apparel landing tabs for new accounts by checking customer first names against country-specific gender results.

Other ways to use Gender Detection Skill

Set up Gender Detection Skill on VerveKit, or reach the same source a different way. Your VerveKit account and credits work on all of them — one key, one balance.

Call it as a REST APIOne HTTPS endpoint and an x-api-key header, with SDKs for Node, Python and .NET.APIVerve →Reference →

Google Sheets or ExcelA =VERVE() formula fills a column — no script, no export, recalculates in place.VerveSheets →Reference →

Ground an agent on itA cited, machine-checkable fact your model can't produce on its own.VerveContext →Reference →