predict_age
Pack: agify · Endpoint: https://gateway.pipeworx.io/agify/mcp
No MCP client? Call it directly: GET https://gateway.pipeworx.io/v1/tools/predict_age for the schema, then POST the same URL with its arguments for the data.
Estimate someone’s age from their first name using global statistics. Returns predicted age and confidence count based on name frequency data.
Parameters
| Name | Type | Required | Description |
|---|---|---|---|
name | string | yes | First name to predict age for. |
Example call
Arguments
{
"name": "John"
}
curl
curl -X POST https://gateway.pipeworx.io/agify/mcp \
-H 'Content-Type: application/json' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"predict_age","arguments":{"name":"John"}}}'
TypeScript (@pipeworx/sdk)
import { Pipeworx } from '@pipeworx/sdk';
const pipeworx = new Pipeworx();
const result = await pipeworx.call('predict_age', {
"name": "John"
});
More examples
{
"name": "Maria"
}
Response shape
Always returns: name, predicted_age, sample_size
| Field | Type | Description |
|---|---|---|
name | string | The name that was analyzed |
predicted_age | number | null | Predicted age based on name statistics, or null if unavailable |
sample_size | number | Number of data points used for prediction (confidence measure) |
Full JSON Schema
{
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "The name that was analyzed"
},
"predicted_age": {
"type": [
"number",
"null"
],
"description": "Predicted age based on name statistics, or null if unavailable"
},
"sample_size": {
"type": "number",
"description": "Number of data points used for prediction (confidence measure)"
}
},
"required": [
"name",
"predicted_age",
"sample_size"
]
}
Connect
Add this to your MCP client config, or use one-click install buttons:
{
"mcpServers": {
"agify": {
"url": "https://gateway.pipeworx.io/agify/mcp"
}
}
}
See Getting Started for client-specific install steps.