chat_persona and saves it to your Percio account. Once created, the persona is immediately available for test runs in both the web app and the agent.
Inputs
| Field | Type | Description |
|---|---|---|
name | string | Persona’s full name. |
mainObjective | string | Main life or work goal, 30–200 chars. |
techLevel | number | Tech expertise, 1–5. |
occupation | string | Job title or role. |
description | string | 2–3 sentence background. |
systemPrompt | string | Full evaluation system prompt. |
focusAreas | string[] | 3–5 things the persona pays attention to. |
painPoints | string[] | 2–4 frustrations the persona brings. |
behavioralTraits | string[] | 3–5 personality traits. |
deviceContext | string | Primary device and usage context. |
emoji | string (optional) | Emoji for the persona. |
Output
The saved persona, including its new UUID, plus a ready-to-use CLI command for running a test with it:When to use it
- After a
chat_personaloop returnsisComplete: true, to save the generated persona. - Directly, if Cursor has enough structured input to skip the conversation.
Cost
Creating a persona is free. It does not consume credits.Typical MCP flow
- User asks Cursor to create a persona.
- Cursor calls
chat_personaand relays the Q&A. - When the conversation returns
isComplete: true, Cursor callscreate_personawith the generated data. - Cursor confirms the persona was saved and optionally offers to run a test with it.