fal-mcp

generate_with_reference

Create images using a reference image to guide style and content. Specify a prompt and reference image URL to generate visuals with controlled similarity to your reference.

Instructions

Generate images with a style/content reference image.

Uses the reference_image feature of flux-general to guide generation toward a similar style or content as the reference.

Args: reference_image_url: URL of the reference image for style guidance. reference_strength: How strongly to follow the reference (0.0-1.0).

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>",
      "reference_image_url": "<reference_image_url>"
    },
    "name": "generate_with_reference"
  }
}

Input Schema

Name	Required	Default
`prompt`	Yes
`reference_image_url`	Yes
`reference_strength`	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. It mentions the underlying feature ('reference_image feature of flux-general') and the reference_strength range, but doesn't disclose critical behavioral traits like rate limits, authentication needs, output format details (though output schema exists), or potential side effects. For a complex image generation tool with 12 parameters, this is insufficient.

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 three sentences: a clear purpose statement, technical context, and minimal parameter info. It's front-loaded with the core functionality. However, the parameter documentation is sparse given the complexity, and the structure could be improved with bullet points or clearer separation of concepts.

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?

Given the tool's complexity (12 parameters, no annotations, 0% schema coverage), the description is incomplete. While an output schema exists (mitigating need to explain return values), the description fails to address many parameters, provide usage examples, or detail behavioral aspects like error handling or performance characteristics. For a sophisticated image generation tool among siblings, this leaves too much unexplained.

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

Parameters2/5

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 only documents 2 of 12 parameters (reference_image_url and reference_strength), briefly explaining their purpose. Key parameters like prompt, model, dimensions, and inference steps are undocumented in both schema and description, leaving significant gaps in understanding parameter meanings and interactions.

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 style/content reference image' and 'Uses the reference_image feature of flux-general to guide generation toward a similar style or content as the reference.' This specifies the verb (generate), resource (images), and key differentiator (reference image). However, it doesn't explicitly distinguish from siblings like 'generate_image' or 'generate_with_lora' beyond the reference aspect.

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

Usage Guidelines3/5

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

The description implies usage context: when you want to generate images guided by a reference image for style or content. It doesn't provide explicit guidance on when to use this vs. alternatives like 'generate_image' (no reference) or 'generate_with_lora' (different guidance method), nor does it mention exclusions or prerequisites. The context is clear but lacks comparative guidance.

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

Deploy Server