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FLUX MCP Server

FLUX MCP Server

by tehw0lf
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_preview

Retrieve a preview thumbnail for a generated image after generation completes. Pass an image ID or omit it to get the last image, returning an inline thumbnail and the full-size path.

Instructions

Retrieve a preview (thumbnail) of a generated image. Use this after generate_image completes — especially useful for FLUX.2-dev background generations where the MCP client timed out. Pass the image_id from generate_image output, or omit to get the last generated image. Returns the thumbnail inline and the full-size image path.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_preview"
  }
}

Input Schema

NameRequiredDescriptionDefault
image_idNoImage ID returned by generate_image (e.g. '20250126_143052_42'). Omit to get the last generated image.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A4.7/5.0
Behavior5/5

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It states that it returns the thumbnail inline and the full-size image path, and explains the optional behavior of omitting image_id to get the last generated image. This gives a clear picture of what to expect from the tool.

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?

The description is concise, front-loaded with the primary purpose, and all three sentences add valuable information without redundancy. It avoids unnecessary detail while covering purpose, usage, and return behavior.

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

Completeness5/5

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

For a simple tool with one optional parameter and no output schema, the description is complete. It covers purpose, when to use, parameter behavior, and return information, leaving no significant gaps for the agent to infer.

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 fully documents the image_id parameter. The description repeats this information but does not add additional semantic meaning beyond what the schema provides. The baseline score of 3 is appropriate.

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 the tool retrieves a preview thumbnail of a generated image, using the specific verb 'retrieve' and a clear resource. It is distinguishable from siblings like generate_image, unload_model, get_status, and set_timeout, which serve different purposes.

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

Usage Guidelines5/5

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

The description explicitly says to use this after generate_image completes, and it highlights a specific use case for FLUX.2-dev background generations where the MCP client timed out. This provides clear when-to-use guidance and implicitly excludes other contexts, with no competing sibling tool offering the same functionality.

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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