fal-mcp

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the tool uses fal.ai, lists model options with performance details, and specifies the return value ('Returns paths to saved images and metadata'). It does not cover aspects like rate limits, authentication needs, or error handling, but provides substantial operational context.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a bulleted list of models for quick reference, and ends with return information. Every sentence earns its place without redundancy, making it efficient and easy to scan.

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 the tool's complexity (10 parameters, no annotations, but with an output schema), the description is largely complete. It covers the purpose, model options, and return values, which are critical for usage. The output schema likely details return structures, so the description need not explain those. However, it could benefit from more guidance on parameter interactions or error cases.

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 0%, so the description must compensate. It adds significant meaning by explaining model options and their characteristics (e.g., 'high quality, 28 steps' for fal-ai/flux/dev), which clarifies the 'model' parameter beyond the schema's default. However, it does not address other parameters like 'guidance_scale' or 'num_inference_steps', leaving gaps for a tool with 10 parameters.

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: 'Generate images from a text prompt using fal.ai.' It specifies the verb ('Generate'), resource ('images'), and method ('from a text prompt using fal.ai'), distinguishing it from sibling tools like edit_image or generate_with_lora that modify or enhance images rather than creating from scratch.

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 clear context by listing common models with their characteristics (e.g., 'high quality', 'fast', 'professional'), which helps guide model selection. However, it does not explicitly state when to use this tool versus alternatives like generate_with_lora or generate_with_reference, nor does it mention exclusions or prerequisites.

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