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flux-mcp

by geosp
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Python
Hybrid

Generate Image

generate_image

Create images from text prompts, returning JPEG previews plus full-resolution PNG links and IDs for later edits.

Instructions

Generate images from a text description with FLUX 3 Image Klein

Returns a JPEG preview of each image plus the URL and id of the full-resolution PNG. Use the id with edit_image to refine a result.

Input Schema

NameRequiredDescriptionDefault
seedNoSeed for a reproducible result
boxesNoOptional layout: where to place elements. Each box is {"bbox": [y_min, x_min, y_max, x_max] on a 0-1000 grid with (0, 0) at the top left, "desc": what goes there, "text": exact text to render (optional)}. Boxes guide placement and scale, not exact pixels.
countNoNumber of variations
stepsNoSampling steps; leave unset for the model default
widthNoWidth in pixels (rounded to 16)
heightNoHeight in pixels (rounded to 16)
promptYesWhat to draw. A short request is fine.
qualityNo'high' uses the undistilled base model: more detail and variety, about 3 times slower (~45 s). Use only when the user asks for it.fast
enhance_promptNoExpand the prompt into a full scene caption first (recommended)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose the return shape (JPEG preview plus URL and id of the full-resolution PNG), which is valuable, but says nothing about generation latency, cost, failure modes, or how count variations are returned.

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

Conciseness5/5

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

Three short sentences with zero waste; the purpose is front-loaded and the return-value and follow-up details come after. Every sentence earns its place.

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

Completeness4/5

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

For a 9-parameter generation tool with no output schema, the description covers the two things the schema cannot: what comes back and how to chain into edit_image. Latency and cost expectations are the main remaining gap.

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 the schema already documents prompt, quality, seed, boxes, and dimensions thoroughly. The description adds no parameter-level meaning beyond what is structured, so the baseline 3 applies.

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?

States a specific verb and resource (generate images from a text description) and names the model used. It is distinguishable from edit_image because it names that sibling as the refinement path, but it does not explicitly contrast generation-from-scratch with editing.

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 only routing guidance is the closing sentence pointing to edit_image for refinement of a returned id – useful for the follow-up step, but it never states when to choose this tool over its siblings for an initial request or what prerequisites exist.

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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