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Search fal.ai Model Catalog

fal_list_models
Read-onlyIdempotent

Search and browse fal.ai's catalog of 600+ models to find the correct model ID for any task. Filter by category or keyword to discover the right generative AI model.

Instructions

Search or browse fal.ai's catalog of 600+ hosted models to find the right model id for a task. Does NOT run any model — this is discovery only.

Args:

  • query (string, optional): Keyword search, e.g. "flux", "upscale", "text to speech"

  • category (string, optional): Category filter, e.g. "text-to-image", "image-to-video"

  • limit (number): Maximum results to return, 1-100 (default: 20)

  • cursor (string, optional): Pagination cursor from a previous response's next_cursor

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

Returns: For JSON format: { "count": number, "models": [ { "endpoint_id": string, "title": string, "category": string, "short_description": string } ], "has_more": boolean, "next_cursor": string | null } For markdown format: a readable list of matching models with their ids.

Examples:

  • Use when: "What model should I use to generate a video?" -> query="video" or category="text-to-video"

  • Use when: "Find a background removal model" -> query="background removal"

  • Don't use when: you already know the exact model_id — go straight to fal_run_model or fal_submit_request

Error Handling:

  • Returns "No models found matching ''" if the search returns empty

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results to return (default: 20)
queryNoOptional keyword search, e.g. "flux", "video", "upscale", "text to speech".
cursorNoPagination cursor from a previous call's next_cursor field
categoryNoOptional category filter, e.g. "text-to-image", "image-to-video", "text-to-speech".
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readable (default: markdown)markdown
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety. The description adds behavioral details like error messages ('No models found', 'Authentication failed') and pagination behavior, which go beyond 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?

The description is well-organized with clear sections (Args, Returns, Examples, Error Handling). Each sentence is purposeful and adds information, making it efficient and easy to parse.

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?

The tool has 5 parameters and no output schema, but the description fully explains the return format for both JSON and markdown, pagination with next_cursor, and error handling. It covers all necessary context for correct usage.

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. The description adds value by providing example values for query and category, clarifying defaults for limit and response_format, and explaining the cursor for pagination. This extra 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 clearly states the tool's purpose: search or browse fal.ai's catalog of 600+ hosted models to find the right model ID. It explicitly distinguishes itself from siblings by saying 'Does NOT run any model — this is discovery only.' The verb+resource is specific and unique.

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?

Provides explicit when-to-use examples (e.g., query='video' for video generation) and when-not-to-use (if model_id is known, use fal_run_model or fal_submit_request). This directly guides the agent on alternatives, earning top marks.

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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