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Fattly

by io.github.industriesfatty-spec
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fattly_create_avatar

Ad Studio: generates your own creator avatar from a description (20 credits). Returns the actorId — pass it to fattly_generate_ad / fattly_generate_ad_video so the SAME creator presents your product. Adults only, enforced server-side.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "description": "<description>"
    },
    "name": "fattly_create_avatar"
  }
}

Input Schema

NameRequiredDescriptionDefault
descriptionYesWhat the creator looks like, e.g. 'a woman with curly red hair and freckles, casual denim jacket'. Any language — translated server-side.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations only indicate that the operation is non-readOnly, non-idempotent, and non-destructive. The description adds meaningful behavior: it costs 20 credits, returns an actorId, enforces adults-only server-side, and guarantees the same creator is reused. This goes well beyond the annotations and the schema.

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 pack the core function, cost, output, downstream usage, and content policy without repetition. The purpose is front-loaded and every sentence contributes information an agent needs.

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?

For a one-parameter generation tool with no output schema, the description covers the essential return value (actorId), how to consume it, the credit cost, and the content restriction. It is slightly thin on operational details like processing delays or failure modes, but nothing required to call it correctly is missing.

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% and the parameter description already explains what to write plus an example and server-side translation. The tool-level description only reiterates that the avatar is 'from a description', so it adds no substantial meaning beyond 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 uses a specific verb ('generates'), names the exact resource ('your own creator avatar'), and states the input source ('from a description') and output ('actorId'). It also differentiates this from the many sibling generation tools by tying the outcome to a reusable creator that can be passed into fattly_generate_ad / fattly_generate_ad_video.

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

Usage Guidelines4/5

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

It gives clear context for when to use the tool: when you want a persistent creator avatar for ad generation, with the actorId to reuse across ad types. It does not explicitly state when NOT to use it or name an alternative, but no sibling offers the same avatar-creation capability, so the usage context is unambiguous.

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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