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flux_models

List available FLUX image generation models with usage notes and key inputs to help users select the right model for text-to-image, variations, inpainting, or edge-guided creation tasks.

Instructions

List supported FLUX models with usage notes and key inputs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler for the 'flux_models' tool within the CallToolRequestSchema request handler. It maps over FLUX_MODELS and returns a JSON string of model metadata including display name, kind, image acceptance, notes, and key inputs.
    if (name === "flux_models") {
      const models = Object.entries(FLUX_MODELS).map(([model, meta]) => ({
        model,
        display: meta.display,
        kind: meta.kind,
        accepts_image: meta.accepts_image,
        notes: meta.notes,
        key_inputs: Object.keys(meta.inputs),
      }));
    
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(models, null, 2),
          },
        ],
      };
    }
  • index.js:206-213 (registration)
    Registration of the 'flux_models' tool in the ListToolsRequestSchema response, defining its name, description, and empty input schema.
    {
      name: "flux_models",
      description: "List supported FLUX models with usage notes and key inputs",
      inputSchema: {
        type: "object",
        properties: {},
      },
    },
  • Helper constant FLUX_MODELS defining metadata for all supported FLUX models, including display names, kinds, notes, input schemas, and image acceptance flags. Used by the flux_models handler to generate the tool response.
    const FLUX_MODELS = {
      "black-forest-labs/flux-1.1-pro-ultra": {
        display: "FLUX1.1 Pro Ultra",
        kind: "text-to-image",
        notes: [
          "Highest quality, up to ~4MP; 'raw' mode for realism.",
          "Use when you need best composition/large output.",
        ],
        inputs: {
          prompt: { required: true, type: "string" },
          raw: { required: false, type: "boolean" },
          aspect_ratio: { required: false, type: "string" },
          seed: { required: false, type: "integer" },
          output_quality: { required: false, type: "number" },
          go_fast: { required: false, type: "boolean" },
        },
        accepts_image: false,
      },
      "black-forest-labs/flux-pro": {
        display: "FLUX1.1 Pro",
        kind: "text-to-image",
        notes: [
          "Fast, reliable, commercial-grade default when Ultra not required.",
        ],
        inputs: {
          prompt: { required: true, type: "string" },
          aspect_ratio: { required: false, type: "string" },
          seed: { required: false, type: "integer" },
        },
        accepts_image: false,
      },
      "black-forest-labs/flux-redux-dev": {
        display: "FLUX.1 Redux [dev]",
        kind: "image-variation",
        notes: [
          "Variations/restyling while preserving key elements; mix image + text.",
        ],
        inputs: {
          image: { required: true, type: "file_or_url" },
          prompt: { required: true, type: "string" },
          strength: { required: false, type: "number" },
          seed: { required: false, type: "integer" },
          num_outputs: { required: false, type: "integer" },
        },
        accepts_image: true,
      },
      "black-forest-labs/flux-fill-pro": {
        display: "FLUX.1 Fill [pro]",
        kind: "inpainting/outpainting",
        notes: ["Professional in/outpainting; provide mask for areas to change."],
        inputs: {
          image: { required: true, type: "file_or_url" },
          mask: { required: false, type: "file_or_url" },
          prompt: { required: true, type: "string" },
          num_inference_steps: { required: false, type: "integer" },
          guidance: { required: false, type: "number" },
          seed: { required: false, type: "integer" },
        },
        accepts_image: true,
      },
      "black-forest-labs/flux-depth-dev": {
        display: "FLUX.1 Depth [dev]",
        kind: "depth-guided editing",
        notes: [
          "Structure-preserving edits/style transfer using depth; supply an image.",
        ],
        inputs: {
          image: { required: true, type: "file_or_url" },
          prompt: { required: true, type: "string" },
          seed: { required: false, type: "integer" },
        },
        accepts_image: true,
      },
      "black-forest-labs/flux-canny-pro": {
        display: "FLUX.1 Canny [pro]",
        kind: "edge-guided generation",
        notes: [
          "Control structure/composition with edges; ideal for sketches/wireframes → detailed images.",
        ],
        inputs: {
          image: { required: true, type: "file_or_url" },
          prompt: { required: true, type: "string" },
          seed: { required: false, type: "integer" },
        },
        accepts_image: true,
      },
    };
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool lists models with 'usage notes and key inputs,' which gives some context about the output format. However, it doesn't describe critical behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, or how the data is structured (e.g., pagination, sorting). For a tool with zero annotation coverage, this leaves significant gaps.

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 a single, efficient sentence that front-loads the core action ('List supported FLUX models') and adds value with 'usage notes and key inputs.' There is no wasted language, and it effectively communicates the essential information in a compact form.

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

Completeness3/5

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

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but could be more complete. It covers the purpose and hints at output content, but without annotations or an output schema, it should ideally specify behavioral aspects like read-only nature or data format. For a low-complexity tool, it's minimally viable but lacks depth in transparency.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, meaning there are no parameters to document. The description doesn't need to add parameter semantics, so it naturally meets the baseline. It appropriately focuses on the tool's purpose without unnecessary parameter details.

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 with a specific verb ('List') and resource ('supported FLUX models'), along with additional context about what information is included ('usage notes and key inputs'). It distinguishes itself from the sibling tool 'flux_generate' by focusing on listing models rather than generating content. However, it doesn't explicitly contrast with the sibling beyond the different action.

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 through 'usage notes and key inputs,' suggesting this tool is for discovering available models and their characteristics. It doesn't provide explicit guidance on when to use this versus 'flux_generate' (e.g., 'use this to find models before generating'), nor does it mention any prerequisites or exclusions. The usage is implied but not clearly articulated.

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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