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generateImageFromReference

Create a new image by providing a text prompt and a reference image URL. Use an existing image as input to generate variations or transformations based on your description.

Instructions

Generate a new image using an existing image as reference. User-configured settings in MCP config will be used as defaults unless specifically overridden.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe text description of what to generate based on the reference image (e.g., "create a cartoon version", "make it look like a painting")
imageUrlYesPublic HTTP(S) URL(s) of reference images. Accepts a string or an array for multi-reference. Local file paths, file uploads, or base64/data URLs are not supported.
modelNoModel name to use for generation (default: user config or "kontext"). Available: "kontext", "nanobanana", "seedream"
seedNoSeed for reproducible results (default: random)
widthNoWidth of the generated image (default: 1024)
heightNoHeight of the generated image (default: 1024)
enhanceNoWhether to enhance the prompt using an LLM before generating (default: true)
safeNoWhether to apply content filtering (default: false)
outputPathNoDirectory path where to save the image (default: user config or "./mcpollinations-output")
fileNameNoName of the file to save (without extension, default: generated from prompt)
formatNoImage format to save as (png, jpeg, jpg, webp - default: png)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions user-configured defaults but omits critical details like whether the operation is destructive (overwrites files), auth requirements, rate limits, or output format. The description does not contradict annotations (none exist).

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 two sentences long, front-loaded with the core purpose. It efficiently conveys the key action. However, it could be more structured (e.g., listing key behaviors).

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 has 11 parameters, no output schema, and no annotations, the description is too brief. It fails to explain the output (e.g., saved file, returned URL), how the reference image is used, or what happens with multiple references. Missing behavioral and result context.

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?

Schema description coverage is 100%, so baseline is 3. The description adds minimal context about defaults from user config, but the schema already documents default values. The description does not clarify interplay between parameters or edge cases.

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 it generates a new image using an existing image as reference, which is a specific verb+resource. This distinguishes it from siblings like generateImage (no reference) and editImage (modify existing).

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?

No explicit guidance on when to use this tool versus alternatives. The description does not mention when-not-to-use or suggest sibling tools for different scenarios.

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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