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Create mascot

create_mascot

Generates a new mascot character from a description (2 credits, charged only on success). Returns a generation_id immediately; poll it with check_generation (about 10-40 seconds). The mascot anchors every later pose and animation, so describe species, personality and look in one sentence.

Input Schema

NameRequiredDescriptionDefault
nameNoOptional display name for the mascot.
styleNoOptional art direction, e.g. 'flat vector', 'soft 3d', 'pixel art', 'line art'.
descriptionYesThe character, e.g. 'a cheerful blue elephant plumber with a tiny red cap'.
reference_pose_idNoOptional pose id from get_board to use as an additional visual reference.
reference_mascot_idNoOptional mascot id from get_board to build on, so a recolour or restyle keeps the original design. Describe the character itself rather than only the change: this description is reused for every later pose, and 'a blue version of that one' carries no appearance into them.

Output Schema

NameRequiredDescriptionDefault
okYes
noteNo
statusYes
targetNo
next_stepNo
generation_idYes
cost_on_successNo
expected_secondsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only declare write/non-idempotent/open-world; the description adds the concrete cost model ('2 credits, charged only on success'), the asynchronous contract (returns generation_id immediately), and expected latency of 10-40 seconds. It also flags the downstream consequence that the mascot anchors every later pose and animation, which matters for a non-idempotent create.

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 sentences, zero filler, ordered purpose → cost/async contract → design advice. The most decision-critical facts (what it makes, what it costs, how to get the result) come first.

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

Completeness5/5

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

For a write tool with an output schema and full schema coverage, the description supplies exactly the missing behavioural layer: billing, sync-vs-async, latency, and the reuse consequence. No return-value explanation is needed since an output schema exists.

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?

Schema coverage is 100%, so baseline is 3, but the description adds real guidance for the required description field ('describe species, personality and look in one sentence') and hints at the reference_mascot_id behaviour by warning that the description is reused for every later pose. Not full credit because style, name and reference_pose_id get no mention 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?

States a specific verb and resource ('Generates a new mascot character from a description') and scopes the output as a generation job. The 'anchors every later pose and animation' line makes clear how it differs from create_pose and create_animation without naming them.

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?

Explicitly says to poll the returned generation_id with check_generation and gives a rough wait window, so the workflow is unambiguous. It also advises on how to write the description input. It stops short of stating when to use create_pose or reference_mascot_id instead, leaving that routing to the schema.

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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