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Submit image generation

generate_image
Destructive

Generate images on fal.ai by submitting a paid request with an exact model_id and validated native inputs. Use for approved AI image creation when you know the precise model and parameters.

Instructions

Confirmed paid model request after current native input-schema validation. Exact model_id/input required; image/video commands do not invent fields or choose a default. Queue submissions return receipt only; synchronous timeout may leave an unknown paid outcome. No retry or polling.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesExact native model inputs. Fetched current model JSON schema validates these before a paid request; no guessed prompt/image/duration adapters.
accountNoExact configured isolated API-key profile label.
confirmNoExplicit approval for the requested paid work, mutation, upload or private file.
model_idYesExact current catalog endpoint ID. Never guess model names or parameter mappings.
store_ioNoLocal default false sends X-Fal-Store-IO:0. true allows provider JSON payload storage; CDN media retention/ACL is separate.
lifecycleNoNative CDN expiry/ACL preference. Omit to use account defaults. null expiration means no expiry; default CDN access may be public. Unknown nicknames may be dropped by provider.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

B3/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and idempotentHint=false, but the description adds real behavioral context the annotations cannot: queue submissions return a receipt only, a synchronous timeout can leave an unknown paid outcome, and there is no retry or polling. That payment-outcome uncertainty is exactly the kind of non-obvious trait an agent needs before invoking.

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

Conciseness3/5

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

The text is compact and free of filler, but it is telegraphic and not front-loaded with the core action; the first clause leads with payment/validation framing instead of what the tool produces. Every clause carries information, yet the phrasing is dense enough to be cryptic on first read.

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

Completeness3/5

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

For a 6-parameter tool with nested objects and no output schema, the description covers payment confirmation, validation, and failure semantics reasonably well. However, it only gestures at the return value ('receipt only') and says nothing about the generated image payload or how results are retrieved, leaving a gap the absent output schema would otherwise force it to fill.

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%, so the schema already documents all six parameters thoroughly. The description reinforces that exact model_id/input are required and that no fields may be invented or defaulted, which is marginally useful but adds little beyond the schema's own wording.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The title gives the verb+resource ('Submit image generation'), but the description itself is oblique: 'Confirmed paid model request after current native input-schema validation' describes the payment/validation framing rather than plainly stating that it generates an image. It never distinguishes itself from siblings like run_model, submit_job, or generate_video, so an agent must infer the boundary from the title alone.

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?

The only usage constraint is the negative 'No retry or polling,' plus the implication that confirm must be set for paid work. There is no statement of when to choose this over run_model, submit_job, or generate_video, and no prerequisites beyond the vague 'confirmed paid request.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.