AI Video Generator MCP Server

Instructions

Implementation Reference

• src/index.ts:207-257 (handler)

  Handler for the 'check-video-status' tool. Determines the model, validates it, calls falClient.queue.status with the requestId, processes the response to extract status, logs, and position, and returns a JSON-formatted text content response. Handles errors appropriately.
  } else if (name === "check-video-status") { try { // Determine which model to use const modelName = args.model || "luma"; const modelUrl = MODELS[modelName as keyof typeof MODELS]; if (!modelUrl) { return { isError: true, content: [ { type: "text", text: `Invalid model: ${modelName}. Supported models are: luma, kling` } ] }; } const status: any = await falClient.queue.status(modelUrl, { requestId: args.request_id, logs: true }); // Safely extract properties const statusLogs = Array.isArray(status.logs) ? status.logs : []; const position = status.position || status.queue_position || 0; return { content: [ { type: "text", text: JSON.stringify({ model: modelName, status: status.status, logs: statusLogs, position: position }, null, 2) } ] }; } catch (error: any) { return { isError: true, content: [ { type: "text", text: `Error checking video status: ${error instanceof Error ? error.message : String(error)}` } ] }; }
• src/index.ts:94-113 (schema)

  Registration and schema for 'check-video-status' tool in the ListToolsRequestSchema handler. Defines name, description, and input schema requiring 'request_id' and optional 'model'.
  { name: "check-video-status", description: "Check the status of a video generation request", inputSchema: { type: "object", properties: { request_id: { type: "string", description: "The request ID to check" }, model: { type: "string", enum: ["luma", "kling"], default: "luma", description: "AI model used for the request (luma=Ray2, kling=Kling)" } }, required: ["request_id"] } }
• src/index.ts:40-116 (registration)

  The ListToolsRequestSchema handler where the 'check-video-status' tool is registered by including it in the tools array.
  server.setRequestHandler(ListToolsRequestSchema, async () => { return { tools: [ { name: "generate-video", description: "Generate a video from text prompt and/or images using AI models (Luma or Kling)", inputSchema: { type: "object", properties: { prompt: { type: "string", description: "Text description of the desired video content" }, image_url: { type: "string", description: "Initial image to start the video from (URL or base64 data URI)" }, end_image_url: { type: "string", description: "Final image to end the video with (URL or base64 data URI)" }, aspect_ratio: { type: "string", enum: ["16:9", "9:16", "4:3", "3:4", "21:9", "9:21"], default: "16:9", description: "Aspect ratio of the video" }, resolution: { type: "string", enum: ["540p", "720p", "1080p"], default: "540p", description: "Resolution of the video (higher resolutions use more credits)" }, duration: { type: "string", enum: ["5s", "9s"], default: "5s", description: "Duration of the video (9s costs 2x more)" }, loop: { type: "boolean", default: false, description: "Whether the video should loop (blend end with beginning)" }, model: { type: "string", enum: ["luma", "kling"], default: "luma", description: "AI model to use (luma=Ray2, kling=Kling)" } }, required: ["prompt"] } }, { name: "check-video-status", description: "Check the status of a video generation request", inputSchema: { type: "object", properties: { request_id: { type: "string", description: "The request ID to check" }, model: { type: "string", enum: ["luma", "kling"], default: "luma", description: "AI model used for the request (luma=Ray2, kling=Kling)" } }, required: ["request_id"] } } ] }; });