spriteship

Generate a character pose

generate_pose

PAID (~150 credits). Generate a new pose for a character from a text description. DEFAULTS TO A COST PREVIEW — see the dryRun argument. Returns { poseId } once executed; poses have no jobId, so poll list_character_poses until status is done or error.

Input Schema

TableJSON Schema

Name	Required	Description
`name`	Yes	Short pose name, e.g. "casting".
`dryRun`	No	DEFAULTS TO TRUE. While true this returns only a cost quote ({ estimatedCredits, balance, spendCapDaily, spentLast24h, capRemaining }) and executes nothing. Show the user estimatedCredits and get an explicit yes for that amount, THEN re-call with dryRun:false to actually spend.
`imageModel`	No	Override the image model.
`characterId`	Yes	Character id.
`idempotencyKey`	No	Optional Idempotency-Key for the real (dryRun:false) call. Omit and one is minted per call. Reuse the SAME value when retrying a call that failed with ENTITY_BUSY / 402 / 429 so the retry cannot double-dispatch.
`poseDescription`	Yes	What the character should be doing, e.g. "kneeling, shield raised".
`referencePoseId`	No	Seed from an existing DONE pose instead of the base image.

TDQS

A4.5/5.0

Behavior5/5

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

The description enriches annotations substantially: it reveals paid usage, the dryRun default returning a cost quote, the actual execution flow, absence of jobId, and the need to poll. This goes far beyond the annotations (readOnlyHint=false, openWorldHint=true, etc.) and clearly discloses important behaviors.

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 two sentences with high information density: paid status, generation purpose, dryRun default, return value, polling instruction. It is front-loaded and every phrase earns its place, with no redundant wording.

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 tool with 7 parameters, no output schema, and cost implications, the description thoroughly covers the essential workflow: cost preview, actual execution, return shape ({ poseId }), polling behavior, and idempotency retry guidance. Combined with the rich schema, it leaves no obvious 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%, and the schema already explains all parameters in detail, including dryRun, idempotencyKey, and referencePoseId. The tool description reiterates the dryRun behavior but doesn't add new parameter meaning beyond what the schema provides, so baseline 3 is appropriate.

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 'Generate a new pose for a character from a text description' with a specific verb and resource. It distinguishes from sibling tools by emphasizing text-based pose generation and mentions the output format ({ poseId }).

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?

Provides strong usage context: explains the cost-preview default, the two-step dryRun flow, and instructs to poll list_character_poses for completion. However, it doesn't explicitly mention when not to use this tool or name specific alternatives, so it misses the full 'when-not' criterion.

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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TDQS

A3.9/5.0

Disambiguation4/5

Most tools have clearly distinct purposes, and the descriptions are extremely detailed with cross-references (e.g., animate_asset vs frame_animation vs generate_character_animation). A few pairs like reprocess_asset vs revise_asset could be confused initially, but their descriptions and use cases are explicit enough to prevent misselection.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., create_project, get_asset, cancel_job). Verbs are imperative and nouns are appropriately singular/plural, making the API predictable and readable.

Tool Count2/5

At 41 tools, the server is far beyond the 15-25 range considered reasonable for most APIs. While the domain is broad (project, assets, characters, animations, jobs, exports, credits), the sheer number creates a heavy surface that may overwhelm agents and suggests the API could be consolidated into higher-level operations.

Completeness4/5

The tool set covers the full creative pipeline: project creation, asset/character generation, animation (both AI and frame-based), revisions, exports, and job management. Minor gaps include lack of delete operations for assets/characters/projects and no listing of all jobs, but these are not critical for the core workflow and are likely intentional for a generative art platform.

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