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Submit fal.ai Request to Queue

fal_submit_request

Submit a generative AI job to fal.ai's async queue and get a request ID for tracking progress. Use for slow models like video generation or training jobs.

Instructions

Submit a job to fal.ai's async queue and return immediately with a request_id. Use this for slow models (video generation, training jobs) or whenever you don't want to block waiting for a result.

After submitting, use fal_check_status to poll progress and fal_get_result once status is COMPLETED. This does NOT wait for or return the final result — see fal_run_model if you want a single blocking call instead.

Args:

  • model_id (string): The fal.ai model id, e.g. "fal-ai/minimax/video-01"

  • arguments (object): Model-specific input, e.g. { "prompt": "a cat astronaut" }

  • webhook_url (string, optional): URL fal should POST the result to on completion

  • response_format ('markdown' | 'json'): Output format (default: markdown)

Returns: For JSON format: { "request_id": string, "status": string, "status_url": string, "response_url": string, "cancel_url": string, "queue_position": number } For markdown format: the same fields, human-readable, plus next-step guidance.

Examples:

  • Use when: "Generate a video of a rocket launch" -> model_id="fal-ai/minimax/video-01", arguments={"prompt": "a rocket launch"}

  • Use when: "Kick off this training job and let me know status later" -> submit, then poll with fal_check_status

  • Don't use when: the model is fast and you want the result immediately — use fal_run_model instead

Error Handling:

  • Returns "Authentication failed" if FAL_KEY is missing or invalid

  • Returns "Invalid input (422)" if arguments don't match the model's schema — check fal_get_model_schema

  • Returns "Not found (404)" if the model_id doesn't exist

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesThe fal.ai model/endpoint id, e.g. "fal-ai/flux/dev", "fal-ai/flux-pro/kontext", or "fal-ai/minimax/video-01". Find valid ids with fal_list_models or at https://fal.ai/models.
argumentsYesThe model's input arguments as a JSON object, matching that model's own schema (e.g. { "prompt": "a cat astronaut", "image_size": "square_hd" }). Call fal_get_model_schema first if you're unsure what fields a model accepts.
webhook_urlNoOptional. If set, fal POSTs the result here when done instead of requiring polling.
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readable (default: markdown)markdown
Behavior5/5

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

The description discloses key behaviors: it submits to a queue and returns immediately without waiting for the result. It also covers error handling (authentication, invalid input, not found) and which parameters are optional. Annotations already indicate readOnlyHint: false and destructiveHint: false, and the description aligns with and enriches them with concrete error scenarios.

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 fairly long but well-structured with clear sections (Args, Returns, Examples, Error Handling). It front-loads the essential purpose and usage distinction. While every sentence is informative, the length could be slightly trimmed without losing value. Still, the organization aids comprehension.

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 (4 parameters, async workflow, sibling tools), the description is complete: it covers input, output format, error handling, and usage workflow. Even without an output schema, it documents return fields for both response formats. Examples and error cases ensure an agent can use it 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%, so baseline is 3. However, the description adds value beyond the schema by providing examples (e.g., model_id 'fal-ai/minimax/video-01' with arguments 'prompt'), explaining the purpose of webhook_url, and specifying default for response_format. It also ties error handling to parameters (e.g., invalid input requires checking fal_get_model_schema). This additional context justifies a 4.

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 begins with a clear verb+resource: 'Submit a job to fal.ai's async queue and return immediately with a request_id.' It explicitly distinguishes itself from fal_run_model (blocking) and mentions use cases for slow models like video generation or training jobs. This makes its purpose unambiguous and differentiated from sibling 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?

The description provides explicit when-to-use (slow models, non-blocking) and when-not-to-use (fast models, use fal_run_model) guidance. It also instructs on the subsequent steps: use fal_check_status and fal_get_result after submission. This is comprehensive and actionable for an AI agent.

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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