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RamboRogers

FAL Image/Video MCP Server

by RamboRogers

kling_master_image

Convert images to videos using Kling 2.1 Master I2V technology. Specify motion prompts and adjust duration, aspect ratio, and generation parameters to create animated content from static images.

Instructions

Kling 2.1 Master I2V - Premium image-to-video conversion

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

  • Handler method for all image-to-video tools, including kling_master_image. Calls fal.subscribe on the model's endpoint with provided parameters and processes the video output.
    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}`);
      }
    }
  • Dynamic input schema generation for image-to-video tools like kling_master_image in generateToolSchema.
    } 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:121-121 (registration)
    Tool registration entry in MODEL_REGISTRY.imageToVideo array, defining id, endpoint, name, and description.
    { 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' },
  • Helper function to retrieve model configuration by ID, used in tool dispatch for kling_master_image.
    function getModelById(id: string) {
      const allModels = getAllModels();
      return allModels.find(model => model.id === id);
    }
  • Helper function to download and process video output, used in the kling_master_image 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 'Premium' but doesn't clarify what that means (quality, cost, rate limits, authentication needs). It doesn't describe output format, processing time, or any behavioral constraints beyond the basic conversion statement.

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 7 words. It's front-loaded with the core functionality and includes the model version. Every word earns its place with no wasted text or redundancy.

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 6-parameter tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns (video URL? processing status?), doesn't mention error conditions, and provides minimal context about the conversion process despite the complexity implied by multiple configuration parameters.

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 83% (high), so the baseline is 3. The description adds no parameter-specific information beyond what's already in the schema. It doesn't explain relationships between parameters or provide usage examples for the motion prompt, negative prompt, or cfg_scale.

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 clearly states the tool's function as 'Premium image-to-video conversion' using Kling 2.1 Master I2V technology. It specifies the verb (conversion) and resource (image to video), though it doesn't explicitly differentiate from sibling tools like kling_master_text or other video generation tools in the list.

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 multiple sibling tools for image/video generation (e.g., flux_dev, hunyuan_image, pixverse_image, veo3), there's no indication of what makes this tool unique or when it should be preferred 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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