Skip to main content
Glama
RamboRogers

FAL Image/Video MCP Server

by RamboRogers

wan_pro_image

Animate static images into videos using motion prompts and customizable parameters like duration and aspect ratio.

Instructions

Wan Pro I2V - Professional image animation

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

  • src/index.ts:119-127 (registration)
    MODEL_REGISTRY.imageToVideo array registration, including the wan_pro_image tool definition with id, endpoint, name, and description.
    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' }
    ]
  • Dynamic input schema definition for image-to-video tools (including wan_pro_image) in generateToolSchema function.
    } 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'];
    }
  • Core handler function for executing image-to-video tools like wan_pro_image. Calls FAL API endpoint, processes video output with downloads and data URLs.
    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}`);
      }
    }
  • Helper function to retrieve model configuration by tool ID, used to dispatch to correct handler.
    // Helper function to get model by ID
    function getModelById(id: string) {
      const allModels = getAllModels();
      return allModels.find(model => model.id === id);
    }
  • Helper function to download video, generate data URL, and auto-open file; called by image-to-video handler.
    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 full burden. It mentions 'Professional image animation' but lacks details on behavioral traits such as processing time, rate limits, authentication needs, output format (e.g., video URL), or error handling. This leaves significant gaps for an agent to understand how the tool behaves beyond its basic function.

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, 'Wan Pro I2V - Professional image animation', which is front-loaded and wastes no words. Every part of it contributes directly to stating the tool's purpose, making it efficient and well-structured.

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 animation tool with 6 parameters and no output schema or annotations, the description is incomplete. It doesn't explain what the tool returns (e.g., a video file or link), performance expectations, or error cases, leaving the agent with insufficient context for reliable use.

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%, with clear descriptions for most parameters (e.g., 'URL of the input image', 'Motion description prompt'). The description doesn't add extra meaning beyond the schema, but the schema itself is well-documented, meeting the baseline for adequate parameter understanding without compensation needed.

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 'Wan Pro I2V - Professional image animation' clearly states the tool's purpose: it animates images using the Wan Pro I2V model. It specifies the action ('animation') and resource ('image'), but doesn't explicitly differentiate from sibling tools like 'wan_pro_text' or other image/video generation tools in the list, which keeps it from a perfect score.

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 for image/video generation (e.g., 'flux_dev', 'pixverse_image', 'vidu_image'), there's no indication of specific contexts, prerequisites, or comparisons to help an agent choose appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/RamboRogers/fal-image-video-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server