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RamboRogers

FAL Image/Video MCP Server

by RamboRogers

hunyuan_image

Convert images into videos by describing motion prompts, controlling duration, aspect ratio, and content preferences through the FAL Image/Video MCP Server.

Instructions

Hunyuan I2V - Open-source image-to-video

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_urlYesURL of the input image
promptYesMotion description prompt
durationNoVideo duration in seconds5
aspect_ratioNo16:9
negative_promptNoWhat to avoid in the video
cfg_scaleNoHow closely to follow the prompt

Implementation Reference

  • Core handler function for the 'hunyuan_image' tool (shared with other image-to-video models). It processes inputs, calls the FAL endpoint 'fal-ai/hunyuan-video-image-to-video', handles video output, downloads, data URLs, and auto-opens files.
    private async handleImageToVideo(args: any, model: any) {
      const { 
        image_url, 
        prompt, 
        duration = '5', 
        aspect_ratio = '16:9',
        negative_prompt,
        cfg_scale
      } = args;
    
      try {
        // Configure FAL client lazily with query config override
        configureFalClient(this.currentQueryConfig);
        const inputParams: any = { image_url, prompt };
        
        // Add optional parameters
        if (duration) inputParams.duration = duration;
        if (aspect_ratio) inputParams.aspect_ratio = aspect_ratio;
        if (negative_prompt) inputParams.negative_prompt = negative_prompt;
        if (cfg_scale !== undefined) inputParams.cfg_scale = cfg_scale;
    
        const result = await fal.subscribe(model.endpoint, { input: inputParams });
        const videoData = result.data as FalVideoResult;
        const videoProcessed = await downloadAndProcessVideo(videoData.video.url, model.id);
    
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify({
                model: model.name,
                id: model.id,
                endpoint: model.endpoint,
                input_image: image_url,
                prompt,
                video: {
                  url: videoData.video.url,
                  localPath: videoProcessed.localPath,
                  ...(videoProcessed.dataUrl && { dataUrl: videoProcessed.dataUrl }),
                  width: videoData.video.width,
                  height: videoData.video.height,
                },
                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 'hunyuan_image' tool, generated dynamically in generateToolSchema for imageToVideo category.
    } else if (category === 'imageToVideo') {
      baseSchema.inputSchema.properties = {
        image_url: { type: 'string', description: 'URL of the input image' },
        prompt: { type: 'string', description: 'Motion description prompt' },
        duration: { type: 'string', enum: ['5', '10'], default: '5', description: 'Video duration in seconds' },
        aspect_ratio: { type: 'string', enum: ['16:9', '9:16', '1:1'], default: '16:9' },
        negative_prompt: { type: 'string', description: 'What to avoid in the video' },
        cfg_scale: { type: 'number', default: 0.5, minimum: 0, maximum: 1, description: 'How closely to follow the prompt' }
      };
      baseSchema.inputSchema.required = ['image_url', 'prompt'];
    }
  • src/index.ts:119-127 (registration)
    MODEL_REGISTRY definition registers the 'hunyuan_image' tool with its endpoint and metadata, used for dynamic tool listing and dispatch.
    imageToVideo: [
      { id: 'ltx_video', endpoint: 'fal-ai/ltx-video-13b-distilled/image-to-video', name: 'LTX Video', description: 'Fast and high-quality image-to-video conversion' },
      { id: 'kling_master_image', endpoint: 'fal-ai/kling-video/v2.1/master/image-to-video', name: 'Kling 2.1 Master I2V', description: 'Premium image-to-video conversion' },
      { id: 'pixverse_image', endpoint: 'fal-ai/pixverse/v4.5/image-to-video', name: 'Pixverse V4.5 I2V', description: 'Advanced image-to-video' },
      { id: 'wan_pro_image', endpoint: 'fal-ai/wan-pro/image-to-video', name: 'Wan Pro I2V', description: 'Professional image animation' },
      { id: 'hunyuan_image', endpoint: 'fal-ai/hunyuan-video-image-to-video', name: 'Hunyuan I2V', description: 'Open-source image-to-video' },
      { id: 'vidu_image', endpoint: 'fal-ai/vidu/image-to-video', name: 'Vidu I2V', description: 'High-quality image animation' },
      { id: 'luma_ray2_image', endpoint: 'fal-ai/luma-dream-machine/ray-2/image-to-video', name: 'Luma Ray 2 I2V', description: 'Latest Luma image-to-video' }
    ]
  • src/index.ts:476-482 (registration)
    Dispatch logic in CallToolRequestSchema handler routes calls to 'hunyuan_image' to the image-to-video handler based on registry lookup.
    if (MODEL_REGISTRY.imageGeneration.find(m => m.id === name)) {
      return await this.handleImageGeneration(args, model);
    } else if (MODEL_REGISTRY.textToVideo.find(m => m.id === name)) {
      return await this.handleTextToVideo(args, model);
    } else if (MODEL_REGISTRY.imageToVideo.find(m => m.id === name)) {
      return await this.handleImageToVideo(args, model);
    }
  • Helper function used by the handler to download, process, and prepare video outputs including local paths and data URLs.
    async function downloadAndProcessVideo(videoUrl: string, modelName: string): Promise<any> {
      const filename = generateFilename('video', modelName);
      const localPath = await downloadFile(videoUrl, filename);
      const dataUrl = await urlToDataUrl(videoUrl);
      
      // Auto-open the downloaded video if available
      if (localPath) {
        await autoOpenFile(localPath);
      }
      
      const result: any = {};
      
      // Only include localPath if download was successful
      if (localPath) {
        result.localPath = localPath;
      }
      
      // Only include dataUrl if it was successfully generated
      if (dataUrl) {
        result.dataUrl = dataUrl;
      }
      
      return result;
    }
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 'Open-source' but does not cover critical traits such as rate limits, authentication needs, processing time, or output format (e.g., video file type). For a tool with 6 parameters and no output schema, this lack of behavioral context is a significant gap.

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 with a single phrase, 'Hunyuan I2V - Open-source image-to-video', which is front-loaded and wastes no words. It efficiently conveys the core purpose without unnecessary elaboration, making it easy to parse quickly.

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 of an image-to-video tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It lacks information on behavioral aspects, usage guidelines, and output details, leaving the agent with insufficient context to effectively invoke the tool without guesswork.

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 high at 83%, providing good documentation for parameters like 'image_url' and 'prompt'. The description adds minimal value beyond the schema, only implying image-to-video conversion without detailing parameter interactions or usage examples. This meets the baseline for high schema coverage but does not enhance understanding.

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 'Hunyuan I2V - Open-source image-to-video' clearly states the tool's purpose: converting images to videos using the Hunyuan model. It specifies the verb ('image-to-video') and resource ('Hunyuan'), but does not differentiate from sibling tools like 'flux_kontext' or 'pixverse_image', which may also handle image-to-video tasks, leaving room for ambiguity in selection.

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?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools (e.g., 'flux_kontext', 'pixverse_image', 'vidu_image') that might offer similar image-to-video capabilities, there is no indication of specific contexts, strengths, or limitations that would help an agent choose this tool over others.

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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