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generate_mascot

Generate a mascot sprite with AI and save it to the account library. Spends credits. Returns imageUrl and poses like get_sprite, with a preview of each pose. Does not place it on a card.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptNoWhat to draw, up to 2000 characters. Optional when presetId is set.
qualityNo
presetIdNo
referenceUrlNoOptional https image URL used as a visual reference.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does real work: it discloses a credit spend, a persistent side effect (saved to the account library), the return shape (imageUrl plus poses with previews), and an explicit non-action (not placed on a card). It stops short of covering auth requirements, whether generation is reversible, or failure/latency behavior.

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 with zero filler. The core capability, the cost, the return contract, and the scope limit are each front-loaded and distinct.

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 credit-spending generation mutation with no output schema and 50% parameter coverage, the description covers cost, persistence, and return shape but leaves the two undocumented parameters (quality, presetId) unexplained. It also outsources the return format to 'like get_sprite', forcing a cross-tool lookup.

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

Parameters2/5

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

Schema description coverage is only 50% — prompt and referenceUrl are documented in the schema, while quality and presetId expose only bare enums with no explanation of tradeoffs (e.g., credit cost of 'high'). The description mentions no parameter at all, so it adds no meaning beyond the schema and fails to compensate for the coverage gap.

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 a mascot sprite with AI and save it to the account library'), which separates it from generic generate_asset/generate_background siblings. It also clarifies downstream scope ('Does not place it on a card'). It never names the adjacent generation tools explicitly, so sibling differentiation is inferential rather than stated.

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 credit cost and the 'does not place it on a card' exclusion imply a usage boundary, but there is no explicit when-to-use guidance. Nothing tells the agent when to prefer this over generate_asset, or when to choose a presetId versus a free-form prompt.

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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