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RamboRogers

FAL Image/Video MCP Server

by RamboRogers

janus

Generate images from text prompts using multimodal AI, with options to customize size and quantity for various formats.

Instructions

Janus - Multimodal understanding and generation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesText prompt for image generation
image_sizeNolandscape_4_3
num_imagesNo

Implementation Reference

  • Core handler function for the 'janus' tool (and other image generation models). Configures FAL client, prepares input parameters, calls fal.subscribe on the 'fal-ai/janus' endpoint, processes output images with downloads/data URLs, and formats the response.
    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}`);
      }
    }
  • Input schema definition for image generation tools including 'janus': requires 'prompt', optional 'image_size', 'num_images', model-specific params like 'num_inference_steps', '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:108-108 (registration)
    Model registry entry that registers the 'janus' tool with its endpoint, name, and description in the imageGeneration category.
    { id: 'janus', endpoint: 'fal-ai/janus', name: 'Janus', description: 'Multimodal understanding and generation' }
  • src/index.ts:400-402 (registration)
    Dynamic registration of 'janus' tool schema in the ListTools response by iterating over imageGeneration models.
    for (const model of MODEL_REGISTRY.imageGeneration) {
      tools.push(this.generateToolSchema(model, 'imageGeneration'));
    }
  • Helper function to retrieve the 'janus' model configuration by its tool name/id during tool dispatch.
    function getModelById(id: string) {
      const allModels = getAllModels();
      return allModels.find(model => model.id === id);
    }
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral information. 'Multimodal understanding and generation' suggests both analysis and creation capabilities, but doesn't specify what gets generated (images? text? both?), quality characteristics, rate limits, authentication needs, or output format. The input schema hints at image generation through parameters like 'image_size', but the description doesn't explicitly confirm this.

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 extremely concise at just 5 words. While this may be too brief for adequate tool understanding, it contains no redundant information and is efficiently structured. Every word carries conceptual weight without waste.

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?

For a tool with 3 parameters, no annotations, no output schema, and 22 sibling tools, the description is insufficiently complete. It doesn't explain what the tool actually produces, how it differs from similar tools, or provide necessary context for proper usage. The agent would struggle to select this tool appropriately among the many alternatives.

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 only 33% (only the 'prompt' parameter has a description). The description adds no parameter-specific information beyond what's in the schema. However, the schema itself provides reasonable documentation with enums for 'image_size' and constraints for 'num_images', establishing a baseline understanding of the three parameters.

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 states 'Multimodal understanding and generation' which indicates a general capability but is vague about specific functions. It doesn't clearly distinguish this tool from the many sibling image/text generation tools (like flux_dev, hunyuan_image, etc.) or specify what type of multimodal processing it performs. The name 'janus' provides no additional clarity.

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 about when to use this tool versus the 22 sibling tools listed. The description doesn't mention any specific contexts, prerequisites, or alternatives. The agent must infer usage from the input schema alone, which suggests image generation but doesn't clarify differentiation.

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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