Skip to main content
Glama
RamboRogers

FAL Image/Video MCP Server

by RamboRogers

list_available_models

Discover available AI models for generating images and videos, filtered by category to match your creative needs.

Instructions

List all available models in the current registry with their capabilities

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoFilter models by categoryall

Implementation Reference

  • The main handler function for the 'list_available_models' tool. It filters models from the MODEL_REGISTRY based on the provided category and returns a formatted list with details.
    private async handleListModels(args: any) {
      const { category = 'all' } = args;
    
      let modelsToList: any[] = [];
      
      if (category === 'all') {
        modelsToList = getAllModels();
      } else if (category === 'imageGeneration') {
        modelsToList = MODEL_REGISTRY.imageGeneration;
      } else if (category === 'imageGeneration') {
        modelsToList = MODEL_REGISTRY.imageGeneration;
      } else if (category === 'textToVideo') {
        modelsToList = MODEL_REGISTRY.textToVideo;
      } else if (category === 'imageToVideo') {
        modelsToList = MODEL_REGISTRY.imageToVideo;
      }
    
      const modelList = modelsToList.map(model => ({
        id: model.id,
        name: model.name,
        description: model.description,
        endpoint: model.endpoint,
        category: MODEL_REGISTRY.imageGeneration.includes(model as any) ? 'imageGeneration' :
                  MODEL_REGISTRY.textToVideo.includes(model as any) ? 'textToVideo' :
                  MODEL_REGISTRY.imageToVideo.includes(model as any) ? 'imageToVideo' : 'unknown'
      }));
    
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify({
              total_models: modelList.length,
              category_filter: category,
              models: modelList,
              note: "Use 'execute_custom_model' to run any FAL endpoint not in this registry"
            }, null, 2),
          },
        ],
      };
    }
  • Defines the input schema for the 'list_available_models' tool, specifying an optional 'category' parameter to filter models.
    tools.push({
      name: 'list_available_models',
      description: 'List all available models in the current registry with their capabilities',
      inputSchema: {
        type: 'object',
        properties: {
          category: {
            type: 'string',
            enum: ['all', 'imageGeneration', 'textToVideo', 'imageToVideo'],
            default: 'all',
            description: 'Filter models by category'
          }
        },
        required: []
      }
    });
  • src/index.ts:460-465 (registration)
    Tool dispatch/registration in the main CallToolRequestSchema handler, routing 'list_available_models' calls to the handleListModels function.
    // Handle special tools first
    if (name === 'list_available_models') {
      return await this.handleListModels(args);
    } else if (name === 'execute_custom_model') {
      return await this.handleCustomModel(args);
    }
  • Helper function that aggregates all models from the categorized MODEL_REGISTRY for easy access.
    function getAllModels() {
      return [
        ...MODEL_REGISTRY.imageGeneration,
        ...MODEL_REGISTRY.textToVideo,
        ...MODEL_REGISTRY.imageToVideo
      ];
    }
  • The MODEL_REGISTRY object containing all available models, categorized into imageGeneration, textToVideo, and imageToVideo. This is the data source for the tool.
    const MODEL_REGISTRY = {
      imageGeneration: [
        { id: 'imagen4', endpoint: 'fal-ai/imagen4/preview', name: 'Imagen 4', description: 'Google\'s latest text-to-image model' },
        { id: 'flux_kontext', endpoint: 'fal-ai/flux-pro/kontext/text-to-image', name: 'FLUX Kontext Pro', description: 'State-of-the-art prompt adherence and typography' },
        { id: 'ideogram_v3', endpoint: 'fal-ai/ideogram/v3', name: 'Ideogram V3', description: 'Advanced typography and realistic outputs' },
        { id: 'recraft_v3', endpoint: 'fal-ai/recraft/v3/text-to-image', name: 'Recraft V3', description: 'Professional design and illustration' },
        { id: 'stable_diffusion_35', endpoint: 'fal-ai/stable-diffusion-v35-large', name: 'Stable Diffusion 3.5 Large', description: 'Improved image quality and performance' },
        { id: 'flux_dev', endpoint: 'fal-ai/flux/dev', name: 'FLUX Dev', description: 'High-quality 12B parameter model' },
        { id: 'hidream', endpoint: 'fal-ai/hidream-i1-full', name: 'HiDream I1', description: 'High-resolution image generation' },
        { id: 'janus', endpoint: 'fal-ai/janus', name: 'Janus', description: 'Multimodal understanding and generation' }
      ],
      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' }
      ],
      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' }
      ]
    };
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. It states the action but lacks behavioral details such as whether this is a read-only operation, if it requires authentication, rate limits, pagination, or what the output format looks like (e.g., list of model names with capabilities). This is inadequate for a tool with potential complexity.

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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by specifying the action, scope, and key feature (capabilities), making it highly concise 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'capabilities' entail, how models are returned, or any prerequisites. For a tool in a registry with many siblings, more context is needed to guide effective 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?

The input schema has 100% description coverage, with the 'category' parameter well-documented via enum and description. The description adds no additional parameter semantics beyond implying filtering by capabilities, which aligns with the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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 verb ('List') and resource ('all available models in the current registry'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'execute_custom_model' or the various model-specific tools, which would require a 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 many sibling tools for specific models (e.g., 'flux_dev', 'imagen4'), there's no mention of whether this is for discovery, comparison, or selection purposes, leaving the agent without contextual direction.

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