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spriteship

by com.spriteship

Retry a failed job

retry_job

FREE when the original KIE task or a validated stored artifact can be recovered; otherwise PAID only for the sub-steps that still need to run. Re-runs the FAILED step of a job whose status is "error" — the prompt, model and every setting are already on the job, so this is the cheap way out of a failure instead of creating the whole thing again. Work that already succeeded is NOT redone or re-charged: a generation that failed after its image landed reuses the paid result. get_job reports retryMode, retryRecovery and retryCost, and the dryRun quote here is that same number. A non-retryable phase answers 400 JOB_NOT_RETRYABLE — some late phases hold partially-committed state and deliberately have no blanket retry. DEFAULTS TO A COST PREVIEW — see the dryRun argument. Returns { jobId } — poll get_job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYesId of a job whose status is "error" (from get_job).
dryRunNoDEFAULTS 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.
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.

TDQS

A4.8/5.0
Behavior5/5

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

The description thoroughly discloses behavior: FREE vs PAID based on recoverability, reuses paid results for succeeded work, defaults to a cost preview, returns { jobId } and mentions polling get_job. It also reveals the non-retryable error (400 JOB_NOT_RETRYABLE) and partially-committed state rationale. Annotations only provide readOnlyHint=false, destructiveHint=false, openWorldHint=true, idempotentHint=false; the description adds extensive behavioral depth beyond these, including cost semantics and execution flow. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average but every sentence carries information. It is logically structured: cost model, purpose, reuse behavior, related tool, error condition, dryRun default, return type. Key facts are front-loaded (defines what it does and when it's cheap). It avoids fluff but is dense; a 4 reflects strong organization with slight density.

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?

Given the tool's complexity (cost variability, dryRun flow, idempotency, error handling), the description is remarkably complete. It covers when it's free/paid, what happens to succeeded work, how to get cost info via get_job, the non-retryable error case, the default dryRun behavior with user confirmation, and the return type. No output schema exists, but the return is described as { jobId } and dryRun returns a cost structure. No significant gaps for an agent to call correctly.

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%, each parameter has a description. The tool description adds valuable context: dryRun default true and its purpose (cost preview, requiring user confirmation), and idempotencyKey guidance for exactly-once dispatch on retryable errors. These enrich the schema descriptions without redundancy. Since the schema already covers the basics, a 4 is warranted for the additional operational guidance.

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 opens with a clear verb and resource: 'Re-runs the FAILED step of a job whose status is "error"'. It explains the tool reuses the existing job's prompt, model, and settings, distinguishing it from creating a new job. This makes the purpose specific and unambiguous, and it differentiates from siblings like cancel_job and create_* tools.

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

Usage Guidelines5/5

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

Explicitly states when to use: when a job is in error status. It names the alternative (creating the whole thing again) and explains why retry is preferred (cheap way out). It also instructs on the dryRun workflow: show cost quote, get explicit yes, then re-call with dryRun:false. Mentions get_job for retryMode/cost verification and idempotencyKey reuse for retry on specific errors. This is comprehensive guidance.

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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