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generate_avatar

Generate an AI avatar image from a text prompt. Always saved as a persistent asset. Costs 5 credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoImage model to use
promptYesDetailed description of the avatar to generate
avatarNameNoName for the saved avatar
aspectRatioNoAspect ratio (default 1:1)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses two non-schema traits: the result is always persisted as an asset and it costs 5 credits. It stops short of permissions, failure/error behavior, or how the generated asset is retrieved afterward.

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?

Three short sentences, zero filler, with the core purpose front-loaded ahead of the persistence and cost facts. Every sentence earns its place.

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

Completeness3/5

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

For a 4-parameter generation tool with no output schema and no annotations, the description covers purpose, persistence, and cost but omits how to obtain the resulting avatar, credit-check prerequisites, and model selection advice. Adequate but with clear gaps.

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

Parameters3/5

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

Schema description coverage is 100%, so each of the 4 parameters (model, prompt, avatarName, aspectRatio) is already documented, including enum values. The description adds no parameter-level syntax or defaults, so the baseline 3 applies.

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

Purpose4/5

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

States a specific verb and resource: 'Generate an AI avatar image from a text prompt.' The purpose is unambiguous. However, it never differentiates itself from the sibling generate_image, so an agent must guess which image-generation tool to pick.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no prerequisites, and no mention of when to prefer generate_image or face_swap instead. The only context offered is that output 'Always saved as a persistent asset' and a credit cost, which inform the call but do not route the agent among alternatives.

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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