generate_image
Create custom images from text descriptions using AI models, with options to adjust dimensions, format, and output location for integration into projects.
Instructions
Generate an image using Together AI
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Text description of the image to generate | |
| model | No | Model to use for generation | black-forest-labs/FLUX.1.1-pro |
| width | No | Image width in pixels | |
| height | No | Image height in pixels | |
| steps | No | Number of inference steps | |
| n | No | Number of images to generate | |
| outputDir | No | Full absolute path where images will be saved (e.g., /Users/username/Projects/myapp/src/assets) | |
| format | No | Output format for the generated images | png |
Implementation Reference
- src/index.ts:141-275 (handler)The CallToolRequestSchema handler implementing the generate_image tool: validates args, calls Together AI API to generate images, processes/resizes with sharp, saves to specified directory, returns file paths.
this.server.setRequestHandler(CallToolRequestSchema, async (request) => { if (request.params.name !== 'generate_image') { throw new McpError( ErrorCode.MethodNotFound, `Unknown tool: ${request.params.name}` ); } if (!request.params.arguments || !isValidGenerateImageArgs(request.params.arguments)) { throw new McpError( ErrorCode.InvalidParams, 'Invalid generate_image arguments' ); } try { const args = request.params.arguments as GenerateImageArgs; // Get requested dimensions const requestWidth = args.width || 1024; const requestHeight = args.height || 768; // Ensure dimensions are at least 256 pixels for the API request const apiWidth = Math.max(256, requestWidth); const apiHeight = Math.max(256, requestHeight); const response = await this.together.images.create({ model: args.model || 'black-forest-labs/FLUX.1.1-pro', prompt: args.prompt, width: apiWidth, height: apiHeight, steps: args.steps || 28, n: args.n || 1, response_format: 'base64', }); // Use provided output directory or default to 'output' const outputDir = args.outputDir ? path.resolve(args.outputDir) : path.join(process.cwd(), 'output'); // Create output directory if it doesn't exist if (!fs.existsSync(outputDir)) { fs.mkdirSync(outputDir, { recursive: true }); } // Process each generated image const results = await Promise.all(response.data.map(async (result: any, index: number) => { const imageData = result.b64_json; let buffer = Buffer.from(imageData, 'base64'); // Only resize if we need to scale down to match requested dimensions if (requestWidth < 256 || requestHeight < 256) { const metadata = await sharp(buffer).metadata(); const originalWidth = metadata.width || 0; const originalHeight = metadata.height || 0; // Calculate target dimensions maintaining aspect ratio const aspectRatio = originalWidth / originalHeight; let targetWidth = requestWidth; let targetHeight = requestHeight; if (requestWidth < 256) { targetWidth = requestWidth; targetHeight = Math.round(requestWidth / aspectRatio); } if (requestHeight < 256) { targetHeight = requestHeight; targetWidth = Math.round(requestHeight * aspectRatio); } // Resize to match requested dimensions buffer = await sharp(buffer) .resize(targetWidth, targetHeight, { fit: 'contain', background: { r: 255, g: 255, b: 255, alpha: 1 } }) .toBuffer(); } // Save image with timestamp and index const timestamp = new Date().getTime(); const format = args.format || 'png'; const filename = `image_${timestamp}_${index}.${format}`; const filepath = path.join(outputDir, filename); let sharpInstance = sharp(buffer); switch (format) { case 'png': await sharpInstance.png().toFile(filepath); break; case 'jpg': await sharpInstance.jpeg({ quality: 90 }).toFile(filepath); break; case 'svg': // For SVG, we'll need to trace the bitmap to create a vector await sharpInstance .png() .toFile(filepath.replace('.svg', '.png')); // Note: Actual SVG conversion would require additional processing // Consider using potrace or similar library for proper SVG conversion console.warn('SVG output is not fully supported yet'); break; } return { ...result, filepath, filename, dimensions: { original: { width: apiWidth, height: apiHeight }, final: await sharp(filepath).metadata().then(m => ({ width: m.width, height: m.height })) } }; })); return { content: [ { type: 'text', text: JSON.stringify(results, null, 2), }, ], }; } catch (error: any) { console.error('Together AI API error:', error); throw new McpError( ErrorCode.InternalError, `Image generation failed: ${error?.message || 'Unknown error'}` ); } }); - src/index.ts:79-137 (registration)Registration of the generate_image tool in the ListToolsRequestSchema response, including name, description, uiSchema, and detailed inputSchema.
name: 'generate_image', description: 'Generate an image using Together AI', uiSchema: { format: { "ui:widget": "select", "ui:options": { label: "Image Format", position: "above-chat" } } }, inputSchema: { type: 'object', properties: { prompt: { type: 'string', description: 'Text description of the image to generate', }, model: { type: 'string', description: 'Model to use for generation', default: 'black-forest-labs/FLUX.1.1-pro', }, width: { type: 'number', description: 'Image width in pixels', default: 1024, }, height: { type: 'number', description: 'Image height in pixels', default: 768, }, steps: { type: 'number', description: 'Number of inference steps', default: 28, }, n: { type: 'number', description: 'Number of images to generate', default: 1, }, outputDir: { type: 'string', description: 'Full absolute path where images will be saved (e.g., /Users/username/Projects/myapp/src/assets)', pattern: '^/', examples: ['/Users/asanstefanski/Private Projekte/democline/src/assets'], }, format: { type: 'string', enum: ['png', 'jpg', 'svg'], description: 'Output format for the generated images', default: 'png', }, }, required: ['prompt'], }, }, - src/index.ts:90-136 (schema)Detailed input schema for generate_image tool parameters including prompt (required), model, dimensions, steps, number of images, output directory, and format.
inputSchema: { type: 'object', properties: { prompt: { type: 'string', description: 'Text description of the image to generate', }, model: { type: 'string', description: 'Model to use for generation', default: 'black-forest-labs/FLUX.1.1-pro', }, width: { type: 'number', description: 'Image width in pixels', default: 1024, }, height: { type: 'number', description: 'Image height in pixels', default: 768, }, steps: { type: 'number', description: 'Number of inference steps', default: 28, }, n: { type: 'number', description: 'Number of images to generate', default: 1, }, outputDir: { type: 'string', description: 'Full absolute path where images will be saved (e.g., /Users/username/Projects/myapp/src/assets)', pattern: '^/', examples: ['/Users/asanstefanski/Private Projekte/democline/src/assets'], }, format: { type: 'string', enum: ['png', 'jpg', 'svg'], description: 'Output format for the generated images', default: 'png', }, }, required: ['prompt'], }, - src/index.ts:20-29 (schema)TypeScript interface defining the expected arguments for generate_image.
interface GenerateImageArgs { prompt: string; model?: string; width?: number; height?: number; steps?: number; n?: number; outputDir?: string; format?: 'png' | 'jpg' | 'svg'; } - src/index.ts:31-44 (helper)Type guard function to validate generate_image arguments matching GenerateImageArgs interface.
const isValidGenerateImageArgs = (args: any): args is GenerateImageArgs => { return ( typeof args === 'object' && args !== null && typeof args.prompt === 'string' && (args.model === undefined || typeof args.model === 'string') && (args.width === undefined || typeof args.width === 'number') && (args.height === undefined || typeof args.height === 'number') && (args.steps === undefined || typeof args.steps === 'number') && (args.n === undefined || typeof args.n === 'number') && (args.outputDir === undefined || typeof args.outputDir === 'string') && (args.format === undefined || ['png', 'jpg', 'svg'].includes(args.format)) ); };