create_scoring_model
Creates the conversion model: which leads or contacts convert, learned from your own conversion history. Bind the outcome to a CRM template or, for an uploaded CSV, to your own columns. Field selection is optional, and label, identifier and personal-data columns are never selectable: naming one is refused rather than silently dropped. Returns the model id to train.
What it needs
| Argument | Type | Required | Description |
|---|---|---|---|
name | string | Yes | |
connection_id | string | Yes | |
template | string | No | |
success_field | any | No | |
success_operator | string | No | |
success_value | any | No | |
success_date_field | any | No | |
fields | any | No | |
exclude_fields | any | No | |
created_date_field | any | No | |
include_engagement | any | No | |
description | any | No |
What it returns
The new model's id and the score it belongs to, confirmation that it is assigned to that score's rule without being activated, the validated contract it was built from, and the columns, date binding and activity sources it will train on.
Credential
An app session token, from an authenticated app.ax1om.ai session. Call it with only an API key and you get a plain refusal saying so, rather than a confusing 401.
Network call: yes. The full credential split is on authentication.
Example call
Most readers never write the protocol themselves: the agent does, once the server is connected. Both forms are the same call.
Use the ax1om MCP server.Call create_scoring_model: creates the conversion model: which leads or contacts convert, learned from your own conversion history.{ "jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": { "name": "create_scoring_model", "arguments": {} }}The arguments object is whatever this tool's own input schema declares. An MCP client reads that schema from the server, so it is the server that answers for the exact shape rather than this page.