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RamboRogers

FAL Image/Video MCP Server

by RamboRogers

recraft_v3

Generate professional designs and illustrations from text prompts using AI, with customizable image sizes and multiple output options.

Instructions

Recraft V3 - Professional design and illustration

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesText prompt for image generation
image_sizeNolandscape_4_3
num_imagesNo

Implementation Reference

  • src/index.ts:104-104 (registration)
    Registration of the 'recraft_v3' tool in the MODEL_REGISTRY.imageGeneration array, defining its ID, FAL endpoint, name, and description.
    { id: 'recraft_v3', endpoint: 'fal-ai/recraft/v3/text-to-image', name: 'Recraft V3', description: 'Professional design and illustration' },
  • Dynamic input schema generation for imageGeneration tools like recraft_v3 in generateToolSchema function. Defines required 'prompt', optional 'image_size', 'num_images', and model-specific params.
    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') {
  • Core handler for recraft_v3 (imageGeneration category). Parses args, configures FAL client, calls fal.subscribe on 'fal-ai/recraft/v3/text-to-image' endpoint, processes output images with downloads/data URLs, returns JSON result.
    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}`);
      }
    }
  • Helper function getModelById used to retrieve the recraft_v3 model configuration by its ID during tool dispatch.
    function getModelById(id: string) {
      const allModels = getAllModels();
      return allModels.find(model => model.id === id);
    }
  • Call to downloadAndProcessImages helper (defined lines 255-288) which handles post-generation image processing, downloads, data URLs, and auto-open for recraft_v3 outputs.
    const processedImages = await downloadAndProcessImages(imageData.images, model.id);
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. 'Professional design and illustration' implies a creative output but fails to specify whether this is a read-only generation tool, if it modifies existing content, requires authentication, has rate limits, or details the output format. For a tool with no annotation coverage, this is a significant gap in transparency.

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 very concise with a single phrase, which is front-loaded and wastes no words. However, it's arguably too brief for a tool with 3 parameters and no annotations, as it under-specifies rather than being efficiently informative. Still, it avoids redundancy and is structurally clear.

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 complexity (an image generation tool with 3 parameters), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain the tool's behavior, output, or how it differs from siblings, leaving critical gaps for an AI agent to understand and use it effectively. This is inadequate for a tool in this 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 low at 33% (only the 'prompt' parameter has a description). The description adds no information about parameters beyond what the schema provides—it doesn't explain 'image_size' options or 'num_images' constraints. With 0 parameters documented in the description, it doesn't compensate for the schema's gaps, but the baseline is 3 since it doesn't contradict the schema either.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Professional design and illustration' is vague about the specific action—it could mean generating, editing, or analyzing designs. While it hints at visual creation, it lacks a clear verb like 'generate' or 'create' and doesn't distinguish this tool from its many siblings (e.g., other image generation tools like flux_dev or stable_diffusion_35). This leaves the purpose ambiguous but not entirely misleading.

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 guidance is provided on when to use this tool versus alternatives. With 22 sibling tools, including many for image generation (e.g., flux_dev, hunyuan_image), the description offers no context, prerequisites, or exclusions. This absence forces the agent to guess based on the tool name alone, which is insufficient for effective selection.

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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