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Create a custom autouser

autousers_create

Create a custom AI persona for team-based UX evaluation by defining its name, system prompt, role, behavior profile, and runtime configuration. Simulate realistic user interactions to test product experiences.

Instructions

Create a team-scoped custom autouser. Example: { teamId, name: 'Skeptic', systemPrompt: 'You are...' }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
teamIdYesOwning team id (caller must be Editor+).
nameYesDisplay name for the persona.
descriptionNo
roleNoShort role label, defaults to 'autouser'.
avatarNo
systemPromptYesSystem prompt fed to Gemini at run time.
statusNoDefaults to 'published'.
visibilityNoDefaults to 'private'.
capabilitiesNoPersona metadata bundle (stored in `capabilities` JSON column).
configNoRuntime model config (stored in `config` JSON column).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
teamIdNo
nameNo
roleNo
descriptionNo
statusNo
visibilityNo
isSystemNo
sourceNo
capabilitiesNo
configNo
createdAtNo
updatedAtNo
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description indicates a create operation, which is consistent with annotations (readOnlyHint=false). However, it does not disclose additional behavioral traits such as side effects, required permissions (though partially hinted in parameter descriptions), or rate limits. The annotations already carry the safety profile, and the description adds minimal extra context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exceptionally concise: one sentence and an example. It front-loads the core purpose and includes necessary context without any redundant or wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (10 parameters, nested objects) and the existence of an output schema, the description is sufficient for an AI to understand the basic function. It could be more complete by mentioning the return value, but the output schema likely covers that. The description is adequate for selection among siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 80% schema description coverage, the baseline is 3. The description adds value by providing a concrete example (teamId, name, systemPrompt), which aids understanding of parameter usage. However, it does not further explain the semantics of nested objects like capabilities or config beyond what is in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool creates a team-scoped custom autouser, using a specific verb and resource. The example further clarifies the inputs. This distinguishes it from sibling tools like autousers_update or autousers_delete, as it is the creation operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is used when a new custom autouser needs to be created within a team. However, it does not explicitly provide guidance on when to use this tool versus alternatives like autousers_duplicate, nor does it specify prerequisites or situations where it should not be used.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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