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RamboRogers

FAL Image/Video MCP Server

by RamboRogers

pixverse_text

Generate videos from text prompts using the Pixverse V4.5 model. Specify duration and aspect ratio to create custom video content.

Instructions

Pixverse V4.5 - Advanced text-to-video generation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesText prompt for video generation
durationNo
aspect_ratioNo16:9

Implementation Reference

  • The main handler function for all text-to-video tools, including pixverse_text. It calls fal.subscribe on the model's endpoint (fal-ai/pixverse/v4.5/text-to-video for pixverse_text), processes the video URL with download/data URL handling, and returns formatted content.
    private async handleTextToVideo(args: any, model: any) {
      const { prompt, duration = 5, aspect_ratio = '16:9' } = args;
    
      try {
        // Configure FAL client lazily with query config override
        configureFalClient(this.currentQueryConfig);
        const inputParams: any = { prompt };
        
        if (duration) inputParams.duration = duration;
        if (aspect_ratio) inputParams.aspect_ratio = aspect_ratio;
    
        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,
                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}`);
      }
    }
  • Dynamically generates the input schema for pixverse_text (textToVideo category) with prompt (required), duration, and aspect_ratio parameters.
    } else if (category === 'textToVideo') {
      baseSchema.inputSchema.properties = {
        prompt: { type: 'string', description: 'Text prompt for video generation' },
        duration: { type: 'number', default: 5, minimum: 1, maximum: 30 },
        aspect_ratio: { type: 'string', enum: ['16:9', '9:16', '1:1', '4:3', '3:4'], default: '16:9' },
      };
      baseSchema.inputSchema.required = ['prompt'];
    } else if (category === 'imageToVideo') {
  • src/index.ts:110-118 (registration)
    Registers the pixverse_text tool in the MODEL_REGISTRY.textToVideo array, defining its id, endpoint, name, and description. This enables dynamic tool listing and execution.
    textToVideo: [
      { id: 'veo3', endpoint: 'fal-ai/veo3', name: 'Veo 3', description: 'Google DeepMind\'s latest with speech and audio' },
      { id: 'kling_master_text', endpoint: 'fal-ai/kling-video/v2.1/master/text-to-video', name: 'Kling 2.1 Master', description: 'Premium text-to-video with motion fluidity' },
      { id: 'pixverse_text', endpoint: 'fal-ai/pixverse/v4.5/text-to-video', name: 'Pixverse V4.5', description: 'Advanced text-to-video generation' },
      { id: 'magi', endpoint: 'fal-ai/magi', name: 'Magi', description: 'Creative video generation' },
      { id: 'luma_ray2', endpoint: 'fal-ai/luma-dream-machine/ray-2', name: 'Luma Ray 2', description: 'Latest Luma Dream Machine' },
      { id: 'wan_pro_text', endpoint: 'fal-ai/wan-pro/text-to-video', name: 'Wan Pro', description: 'Professional video effects' },
      { id: 'vidu_text', endpoint: 'fal-ai/vidu/q1/text-to-video', name: 'Vidu Q1', description: 'High-quality text-to-video' }
    ],
  • Tool dispatcher in CallToolRequestSchema handler that routes pixverse_text calls to the handleTextToVideo function.
    } 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 the generated video, convert to data URL if enabled, auto-open, and prepare the output paths.
    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?

With no annotations provided, the description carries full burden for behavioral disclosure. While 'generation' implies a write operation, it doesn't disclose important behavioral traits like authentication requirements, rate limits, processing time, cost implications, or what happens on failure. The version 'V4.5' suggests capabilities but doesn't explain them.

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 extremely concise - just 7 words. It's front-loaded with the model name and core function. However, this brevity comes at the cost of completeness, making it more under-specified than efficiently concise.

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 text-to-video generation tool with 3 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (video URL? file? metadata?), doesn't provide usage examples, and offers no guidance on the complex task of video generation despite the rich sibling tool ecosystem.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 tool description adds no parameter information beyond what's in the schema - it doesn't explain what 'duration' represents (seconds?), what the aspect ratio options mean visually, or provide guidance on prompt engineering for this specific model.

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 purpose as 'Advanced text-to-video generation' with the specific verb 'generation' and resource 'video', distinguishing it from sibling tools like pixverse_image (image generation) and other text-to-video tools. However, it doesn't explicitly differentiate from other text-to-video siblings like kling_master_text or vidu_text beyond the model name 'Pixverse V4.5'.

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 22 sibling tools including multiple text-to-video generators (kling_master_text, vidu_text, ltx_video, etc.), there's no indication of when Pixverse is preferred, what its strengths are, or any prerequisites for use.

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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