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spraay_gpu_run

Run AI model inference on GPU. Supports image generation, video, LLMs, audio transcription, and utilities. Use shortcuts or model IDs. Pay $0.05 USDC per request.

Instructions

Run AI model inference on GPU via Replicate. Supports image generation (flux-pro, sdxl, ideogram), video generation (wan-video, minimax-video), LLMs (llama-70b, llama-8b, mixtral), audio (whisper transcription, musicgen), and utilities (esrgan upscaling, rembg background removal). Use shortcuts like 'flux-pro' or full model IDs like 'owner/model'. Returns output directly for fast models, or a poll URL for longer jobs. Costs $0.05 USDC.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesModel-specific input parameters. Image models: { prompt: '...' }. LLMs: { prompt: '...' }. Whisper: { audio: 'https://...' }. ESRGAN: { image: 'https://...' }.
modelYesModel shortcut (flux-pro, sdxl, llama-70b, whisper, esrgan, etc.) or full Replicate model ID (owner/model-name). Use spraay_gpu_models to list all shortcuts.
versionNoSpecific model version hash (optional — not needed for official models)
webhookNoWebhook URL for async result delivery (optional)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesTrue when the gateway call succeeded; false when it returned an error.
dataNoThe gateway response payload on success. The exact shape depends on the tool (see the tool description and the JSON in the text content block).
errorNoHuman-readable error message, present only when ok is false.
Behavior4/5

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

Annotations already indicate it is not read-only. The description adds cost ($0.05 USDC) and output behavior (direct output or poll URL for long jobs), which are valuable for an agent. However, it does not detail potential side effects or rate limits.

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 concise and well-structured: it opens with the purpose, lists supported tasks, explains shortcut usage, and describes output behavior and cost. Every sentence adds value with no redundancy.

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 (multiple model types), full schema coverage, annotations, and an output schema, the description covers the essential aspects: purpose, parameters via examples, cost, and result delivery. It is complete for an agent to use confidently.

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%, but the description adds concrete examples for input (e.g., { prompt: '...' } for image models), which helps clarify the free-form 'input' object beyond the schema's generic description.

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 'Run AI model inference on GPU via Replicate' and enumerates supported model types (image, video, LLMs, audio, utilities). It distinguishes itself from sibling tools by specifying the Replicate integration and providing a list of shortcuts, making its purpose unambiguous.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives like spraay_compute_image_generation or other compute tools. While it mentions using spraay_gpu_models to list shortcuts, it does not exclude other tools or provide context for when this is the appropriate choice.

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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