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spriteship

by com.spriteship
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Generate a character pose

generate_pose

PAID (standard 1K: 60 credits; 2K: 100; 4K: 150; premium models may differ). 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
NameRequiredDescriptionDefault
nameYesShort pose name, e.g. "casting".
dryRunNoDEFAULTS TO TRUE. While true this returns only a cost quote ({ estimatedCredits, balance, spendCapDaily, spentLast24h, capRemaining }) and executes nothing. Show the user estimatedCredits, then ask a normal confirmation question. Prefer the client's native question UI with Approve / Decline / Discuss choices when available; otherwise accept any unambiguous conversational approval. Never require a fixed phrase or ask the user to type a magic word. Only after approval, re-call with dryRun:false to actually spend.
imageModelNoOverride the image model.
characterIdYesCharacter id.
idempotencyKeyNoOptional 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.
poseDescriptionYesWhat the character should be doing, e.g. "kneeling, shield raised".
referencePoseIdNoSeed from an existing DONE pose instead of the base image.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / dryRun / description
      Previous value: -"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."New value: +"DEFAULTS TO TRUE. While true this returns only a cost quote ({ estimatedCredits, balance, spendCapDaily, spentLast24h, capRemaining }) and executes nothing. Show the user estimatedCredits, then ask a normal confirmation question. Prefer the client's native question UI with Approve / Decline / Discuss choices when available; otherwise accept any unambiguous conversational approval. Never require a fixed phrase or ask the user to type a magic word. Only after approval, re-call with dryRun:false to actually spend."
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

The description discloses significant behavior beyond the annotations: credit costs, the dryRun default that changes the call's behavior, the { poseId } return, and the absence of a jobId requiring polling. This is exactly the behavioral context an agent needs and none of it conflicts with the annotations.

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 deliver cost, default behavior, return shape, and completion-polling instructions with no filler. The most decision-relevant facts (paid, dryRun default) are front-loaded.

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 paid, no-output-schema tool with a nonstandard dryRun flow, the description covers the essential workflow: pricing, preview behavior, execution, result shape, and polling. The parameters are fully covered by the schema, so nothing an agent needs to call this 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 schema already thoroughly documents dryRun, idempotencyKey, and the other parameters. The description does not add new parameter-level meaning; it merely references dryRun and the polling workflow, so the baseline of 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 states a specific verb, resource, and input: "Generate a new pose for a character from a text description." It is clearly distinguishable from sibling tools like generate_character_turn or generate_character_animation, and it adds the concrete result shape ({ 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?

The description gives clear operational context: this is a paid generation call, defaults to a dry-run cost preview, and must be polled via list_character_poses because it returns no jobId. It does not explicitly name alternatives or when-not-to-use conditions, but the intended use case 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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