fal-mcp

generate_with_lora

Generate images by applying LoRA models to customize AI-generated visuals. Specify a prompt and LoRA URL to create tailored images with controlled style effects.

Instructions

Generate images with a LoRA model applied.

Args: lora_url: URL to the LoRA safetensors file (e.g. from HuggingFace). lora_scale: Strength of the LoRA effect (0.0-2.0, default 1.0).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>",
      "lora_url": "<lora_url>"
    },
    "name": "generate_with_lora"
  }
}

Input Schema

Name	Required	Default
`prompt`	Yes
`lora_url`	Yes
`lora_scale`	No
`model`	No	fal-ai/flux-general
`width`	No
`height`	No
`num_inference_steps`	No
`guidance_scale`	No
`seed`	No
`num_images`	No
`output_format`	No	png
`filename`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Feb 20, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. While 'Generate images' implies a creation operation, the description doesn't mention important behavioral aspects like: whether this is a read-only or mutating operation, authentication requirements, rate limits, cost implications, or what happens to generated images. The description only covers the LoRA-specific parameters without broader behavioral context.

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 appropriately concise with a clear main purpose statement followed by parameter explanations. The two-sentence structure is efficient, though the parameter explanations are incomplete given the total parameter count. No wasted words, but the brevity comes at the cost of completeness for this complex tool.

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

Completeness2/5

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

For a complex image generation tool with 12 parameters, no annotations, and rich sibling tools, the description is significantly incomplete. It covers only the LoRA-specific aspects while ignoring the core 'prompt' parameter, generation settings, output specifications, and how this tool differs from other generation tools on the server. The existence of an output schema helps with return values, but the description doesn't provide enough context for proper tool selection and usage.

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?

The description provides semantic context for only 2 of 12 parameters (lora_url and lora_scale) with schema description coverage at 0%. While the description adds value by explaining what these LoRA-specific parameters mean, it completely ignores the other 10 parameters including the required 'prompt' parameter and important defaults like model, dimensions, and generation settings. This leaves significant gaps in parameter understanding.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Generate images with a LoRA model applied.' This specifies both the action (generate images) and the key resource/technique (LoRA model). However, it doesn't explicitly differentiate from sibling tools like 'generate_image' or 'generate_with_reference' which also generate images, leaving some ambiguity about when to choose this specific tool.

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 description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'generate_image', 'generate_with_reference', and 'raw_generate' that all appear to generate images, there's no indication of when LoRA-based generation is appropriate versus other methods. The description lacks any 'when-to-use' or 'when-not-to-use' context.

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

Deploy Server