list_models

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

No annotations are provided, so the description carries the full burden. It mentions that 'task' uses 'Fal.ai's semantic search and prioritizes featured models,' which adds useful behavioral context. However, it doesn't cover other aspects like rate limits, authentication needs, or pagination behavior, leaving gaps for a tool with no annotations.

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

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

The description is two sentences, front-loaded with the core purpose, followed by specific parameter usage. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension by an AI agent.

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

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

Given no annotations and no output schema, the description does a good job covering the tool's purpose and parameter usage. However, it lacks details on return values or error handling, which could be important for a listing tool. It's mostly complete but has minor gaps in behavioral context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 parameters well. The description adds value by explaining the semantic difference between 'task' and 'search' parameters, providing examples and clarifying that 'task' is for 'intelligent task-based ranking' while 'search' is for 'simple name/description filtering.' This enhances understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Discover available Fal.ai models for image, video, and audio generation.' It uses a specific verb ('Discover') and identifies the resource ('Fal.ai models'), distinguishing it from siblings like 'recommend_model' or 'generate_image' which perform different operations.

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

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

The description provides explicit guidance on when to use parameters: 'Use 'task' parameter for intelligent task-based ranking (e.g., 'portrait photography'), or 'search' for simple name/description filtering.' It contrasts the 'task' and 'search' parameters, helping the agent choose between them based on the user's needs.

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