FAL Image/Video MCP Server

Instructions

Implementation Reference

• src/index.ts:495-563 (handler)

  Core handler function for executing flux_dev tool. Dispatches to fal-ai/flux/dev endpoint, handles flux-specific parameters (num_inference_steps, guidance_scale), processes image outputs with downloads and data URLs.
  private async handleImageGeneration(args: any, model: any) { const { prompt, image_size = 'landscape_4_3', num_inference_steps = 25, guidance_scale = 3.5, num_images = 1, negative_prompt, safety_tolerance, raw, } = args; try { // Configure FAL client lazily with query config override configureFalClient(this.currentQueryConfig); const inputParams: any = { prompt }; // Add common parameters if (image_size) inputParams.image_size = image_size; if (num_images > 1) inputParams.num_images = num_images; // Add model-specific parameters based on model capabilities if (model.id.includes('flux') || model.id.includes('stable_diffusion')) { if (num_inference_steps) inputParams.num_inference_steps = num_inference_steps; if (guidance_scale) inputParams.guidance_scale = guidance_scale; } if ((model.id.includes('stable_diffusion') || model.id === 'ideogram_v3') && negative_prompt) { inputParams.negative_prompt = negative_prompt; } if (model.id.includes('flux_pro') && safety_tolerance) { inputParams.safety_tolerance = safety_tolerance; } if (model.id === 'flux_pro_ultra' && raw !== undefined) { inputParams.raw = raw; } const result = await fal.subscribe(model.endpoint, { input: inputParams }); const imageData = result.data as FalImageResult; const processedImages = await downloadAndProcessImages(imageData.images, model.id); return { content: [ { type: 'text', text: JSON.stringify({ model: model.name, id: model.id, endpoint: model.endpoint, prompt, images: processedImages, metadata: inputParams, download_path: DOWNLOAD_PATH, data_url_settings: { enabled: ENABLE_DATA_URLS, max_size_mb: Math.round(MAX_DATA_URL_SIZE / 1024 / 1024), }, autoopen_settings: { enabled: AUTOOPEN, note: AUTOOPEN ? "Files automatically opened with default application" : "Auto-open disabled" }, }, null, 2), }, ], }; } catch (error) { throw new Error(`${model.name} generation failed: ${error}`); } }
• src/index.ts:357-373 (schema)

  Dynamically generates the input schema for flux_dev (imageGeneration model), including flux-specific properties like num_inference_steps and guidance_scale.
  if (category === 'imageGeneration') { baseSchema.inputSchema.properties = { prompt: { type: 'string', description: 'Text prompt for image generation' }, image_size: { type: 'string', enum: ['square_hd', 'square', 'portrait_4_3', 'portrait_16_9', 'landscape_4_3', 'landscape_16_9'], default: 'landscape_4_3' }, num_images: { type: 'number', default: 1, minimum: 1, maximum: 4 }, }; baseSchema.inputSchema.required = ['prompt']; // Add model-specific parameters if (model.id.includes('flux') || model.id.includes('stable_diffusion')) { baseSchema.inputSchema.properties.num_inference_steps = { type: 'number', default: 25, minimum: 1, maximum: 50 }; baseSchema.inputSchema.properties.guidance_scale = { type: 'number', default: 3.5, minimum: 1, maximum: 20 }; } if (model.id.includes('stable_diffusion') || model.id === 'ideogram_v3') { baseSchema.inputSchema.properties.negative_prompt = { type: 'string', description: 'Negative prompt' }; } } else if (category === 'textToVideo') {
• src/index.ts:106-106 (registration)

  Registers the flux_dev tool in MODEL_REGISTRY.imageGeneration, providing id, endpoint, name, and description used for tool listing and dispatch.
  { id: 'flux_dev', endpoint: 'fal-ai/flux/dev', name: 'FLUX Dev', description: 'High-quality 12B parameter model' },
• src/index.ts:140-143 (helper)

  Helper to lookup model configuration (including flux_dev) by tool name for handler dispatch.
  function getModelById(id: string) { const allModels = getAllModels(); return allModels.find(model => model.id === id); }
• src/index.ts:400-402 (registration)

  Dynamically registers flux_dev (and other image gen models) as MCP tools in the listTools handler by generating schemas from registry.
  for (const model of MODEL_REGISTRY.imageGeneration) { tools.push(this.generateToolSchema(model, 'imageGeneration')); }