generate_image
Create images from text descriptions using AI models. Specify prompts, adjust settings like size and format, and generate visual content for various applications.
Instructions
Generate images from text prompts. Use list_models with category='image' to discover available models.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Text description of the image to generate | |
| model | No | Model ID (e.g., 'fal-ai/flux-pro') or alias (e.g., 'flux_schnell'). Use list_models to see options. | flux_schnell |
| negative_prompt | No | What to avoid in the image | |
| image_size | No | landscape_16_9 | |
| num_images | No | ||
| seed | No | Seed for reproducible generation | |
| enable_safety_checker | No | Enable safety checker to filter inappropriate content | |
| output_format | No | Output image format | png |
Implementation Reference
- The core handler function that implements the logic for the 'generate_image' tool. It resolves the model, prepares parameters for Fal.ai, executes the generation using the queue strategy, processes the results, handles errors, and returns the URLs of generated images.
async def handle_generate_image( arguments: Dict[str, Any], registry: ModelRegistry, queue_strategy: QueueStrategy, ) -> List[TextContent]: """Handle the generate_image tool.""" model_input = arguments.get("model", "flux_schnell") try: model_id = await registry.resolve_model_id(model_input) except ValueError as e: return [ TextContent( type="text", text=f"❌ {e}. Use list_models to see available options.", ) ] fal_args: Dict[str, Any] = { "prompt": arguments["prompt"], "image_size": arguments.get("image_size", "landscape_16_9"), "num_images": arguments.get("num_images", 1), } # Add optional parameters if "negative_prompt" in arguments: fal_args["negative_prompt"] = arguments["negative_prompt"] if "seed" in arguments: fal_args["seed"] = arguments["seed"] if "enable_safety_checker" in arguments: fal_args["enable_safety_checker"] = arguments["enable_safety_checker"] if "output_format" in arguments: fal_args["output_format"] = arguments["output_format"] # Use fast execution (no queue) for image generation try: result = await queue_strategy.execute_fast(model_id, fal_args) except Exception as e: logger.error("Image generation failed: %s", e) return [ TextContent( type="text", text=f"❌ Image generation failed: {e}", ) ] # Check for error in response if "error" in result: error_msg = result.get("error", "Unknown error") logger.error("Image generation failed for %s: %s", model_id, error_msg) return [ TextContent( type="text", text=f"❌ Image generation failed: {error_msg}", ) ] images = result.get("images", []) if not images: logger.warning("Image generation returned no images. Model: %s", model_id) return [ TextContent( type="text", text=f"❌ No images were generated by {model_id}. The prompt may have been filtered.", ) ] # Extract URLs safely try: urls = [img["url"] for img in images] except (KeyError, TypeError) as e: logger.error("Malformed image response from %s: %s", model_id, e) return [ TextContent( type="text", text=f"❌ Image generation completed but response was malformed: {e}", ) ] response = f"🎨 Generated {len(urls)} image(s) with {model_id}:\n\n" for i, url in enumerate(urls, 1): response += f"Image {i}: {url}\n" return [TextContent(type="text", text=response)] - The input schema and Tool definition for the 'generate_image' tool, defining parameters such as prompt (required), model, image_size, num_images, and others with types, enums, defaults, and descriptions.
Tool( name="generate_image", description="Generate images from text prompts. Use list_models with category='image' to discover available models.", inputSchema={ "type": "object", "properties": { "prompt": { "type": "string", "description": "Text description of the image to generate", }, "model": { "type": "string", "default": "flux_schnell", "description": "Model ID (e.g., 'fal-ai/flux-pro') or alias (e.g., 'flux_schnell'). Use list_models to see options.", }, "negative_prompt": { "type": "string", "description": "What to avoid in the image", }, "image_size": { "type": "string", "enum": [ "square", "landscape_4_3", "landscape_16_9", "portrait_3_4", "portrait_9_16", ], "default": "landscape_16_9", }, "num_images": { "type": "integer", "default": 1, "minimum": 1, "maximum": 4, }, "seed": { "type": "integer", "description": "Seed for reproducible generation", }, "enable_safety_checker": { "type": "boolean", "default": True, "description": "Enable safety checker to filter inappropriate content", }, "output_format": { "type": "string", "enum": ["jpeg", "png", "webp"], "default": "png", "description": "Output image format", }, }, "required": ["prompt"], }, ), Tool( - src/fal_mcp_server/server_dual.py:62-79 (registration)Registration of the 'generate_image' handler in the TOOL_HANDLERS dictionary, which is used by the server's call_tool method to dispatch tool executions to the appropriate handler function.
TOOL_HANDLERS = { # Utility tools (no queue needed) "list_models": handle_list_models, "recommend_model": handle_recommend_model, "get_pricing": handle_get_pricing, "get_usage": handle_get_usage, "upload_file": handle_upload_file, # Image tools "generate_image": handle_generate_image, "generate_image_structured": handle_generate_image_structured, "generate_image_from_image": handle_generate_image_from_image, # Video tools "generate_video": handle_generate_video, "generate_video_from_image": handle_generate_video_from_image, "generate_video_from_video": handle_generate_video_from_video, # Audio tools "generate_music": handle_generate_music, } - src/fal_mcp_server/server_dual.py:111-114 (registration)Server decorator registration for list_tools() method that returns ALL_TOOLS, which includes the 'generate_image' tool schema, making it discoverable by MCP clients.
@self.server.list_tools() async def list_tools() -> List[Tool]: """List all available Fal.ai tools""" return ALL_TOOLS