{
  "tools": [
    {
      "id": "youtube_video_collector",
      "name": "ADAM Video Dataset Collector",
      "description": "Previews and downloads supplied YouTube videos or playlists, normalizes MP4 files, extracts traceable frames, and writes provenance and credits.",
      "category": "Dataset",
      "entry_function": "collect_youtube_dataset",
      "arguments": ["dataset_name", "urls", "output_root", "max_videos", "max_duration_seconds", "max_total_duration_seconds", "max_total_size_mb", "preferred_resolution", "download_audio", "skip_beginning_seconds", "skip_ending_seconds", "mode", "frames_per_second", "max_accepted_frames", "remove_blurry_frames", "remove_black_frames", "remove_near_duplicates", "duplicate_threshold", "keep_mp4", "separate_source_folders", "mix_accepted_frames", "generate_captions", "generate_credits", "save_exact_timestamps", "dry_run", "permission_status", "retry_limit"],
      "required_arguments": ["dataset_name", "urls"],
      "capabilities": ["manual_urls", "playlist_limit", "metadata_preview", "mp4_normalization", "frame_provenance", "progress", "pause", "cancel"],
      "requires_confirmation": true,
      "enabled": true,
      "demo": false,
      "backend": {
        "type": "python",
        "module": "adam.tools.youtube_video_collector",
        "function": "collect_youtube_dataset"
      }
    },
    {
      "id": "dataset_collector",
      "name": "Dataset Collector",
      "description": "Collects image references and produces a reviewable dataset manifest.",
      "category": "Dataset",
      "entry_function": "collect_dataset",
      "arguments": ["subject", "image_count", "collection_mode", "project_name", "output_dir"],
      "required_arguments": ["subject", "image_count", "project_name"],
      "capabilities": ["fresh_collection", "named_output"],
      "requires_confirmation": true,
      "enabled": true,
      "demo": false,
      "backend": {
        "type": "python",
        "module": "adam.tools.real_dataset_collector",
        "function": "collect_dataset"
      }
    },
    {
      "id": "dataset_preparer",
      "name": "Dataset Preparation",
      "description": "Validates, filters, deduplicates, and prepares collected images.",
      "category": "Dataset",
      "entry_function": "prepare_dataset",
      "arguments": ["project_name"],
      "requires_confirmation": false,
      "enabled": true,
      "demo": true,
      "backend": {
        "type": "python",
        "module": "adam.tools.demo_backends",
        "function": "prepare_dataset"
      }
    },
    {
      "id": "caption_generator",
      "name": "Caption Generator",
      "description": "Creates editable training captions from a prepared dataset.",
      "category": "Dataset",
      "entry_function": "generate_captions",
      "arguments": ["subject", "project_name"],
      "requires_confirmation": false,
      "enabled": true,
      "demo": true,
      "backend": {
        "type": "python",
        "module": "adam.tools.demo_backends",
        "function": "generate_captions"
      }
    },
    {
      "id": "lora_trainer",
      "name": "LoRA Trainer",
      "description": "Launches and monitors a registered SD/SDXL LoRA training backend.",
      "category": "Training",
      "entry_function": "train_lora",
      "arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "base_model", "resume_from", "preview_enabled", "preview_every", "preview_prompt", "preview_seed"],
      "required_arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "base_model"],
      "capabilities": ["fresh_training", "resume_training", "progress", "pause", "cancel"],
      "requires_confirmation": true,
      "enabled": true,
      "demo": false,
      "backend": {
        "type": "python",
        "module": "adam.tools.lora_adapter",
        "function": "train_lora"
      }
    },
    {
      "id": "preview_generator",
      "name": "Preview Generator",
      "description": "Produces review previews from the latest registered model output.",
      "category": "Output",
      "entry_function": "generate_previews",
      "arguments": [
        "subject",
        "project_name",
        "preview_count",
        "model_name",
        "checkpoint",
        "prompt",
        "seed"
      ],
      "requires_confirmation": false,
      "enabled": true,
      "demo": true,
      "backend": {
        "type": "python",
        "module": "adam.tools.demo_backends",
        "function": "generate_previews"
      }
    },
    {
      "id": "ddpm_generator",
      "name": "DDPM Generator",
      "description": "Generates reproducible image batches from completed models in the connected DDPM project.",
      "category": "Output",
      "entry_function": "generate_ddpm_images",
      "arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio", "reference_image", "reference_strength", "width", "height", "preview_interval"],
      "required_arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio"],
      "capabilities": ["image_generation", "seed", "sampler", "batch", "aspect_ratio", "reference_image", "live_preview", "progress", "cancel"],
      "model_trainers": ["ddpm"],
      "generation_options": {
        "samplers": ["DDIM", "DDPM"],
        "aspect_ratios": ["1:1 (Square)", "16:9 (Widescreen)", "9:16 (Portrait)", "4:3 (Classic)", "3:4 (Portrait Classic)", "3:2 (Photo)", "2:3 (Portrait Photo)"],
        "step_min": 5,
        "step_max": 500,
        "step_default": 50
      },
      "requires_confirmation": false,
      "enabled": true,
      "demo": false,
      "backend": {
        "type": "python",
        "module": "adam.tools.ddpm_generator",
        "function": "generate_ddpm_images"
      }
    },
    {
      "id": "flow_generator",
      "name": "Flow Matching Generator",
      "description": "Generates reproducible image batches from completed models in the connected Flow Matching project.",
      "category": "Output",
      "entry_function": "generate_flow_images",
      "arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio", "preview_interval"],
      "required_arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio"],
      "capabilities": ["image_generation", "seed", "ode_method", "batch", "aspect_ratio", "live_preview", "progress", "cancel"],
      "model_trainers": ["flow"],
      "generation_options": {
        "samplers": ["Heun", "Euler"],
        "aspect_ratios": ["1:1 (Square)", "4:3 (Landscape)", "3:4 (Portrait)", "3:2 (Landscape)", "2:3 (Portrait)", "16:9 (Widescreen)", "9:16 (Vertical)"],
        "step_min": 1,
        "step_max": 200,
        "step_default": 20
      },
      "requires_confirmation": false,
      "enabled": true,
      "demo": false,
      "backend": {
        "type": "python",
        "module": "adam.tools.flow_generator",
        "function": "generate_flow_images"
      }
    },
    {
      "id": "lora_generator",
      "name": "LoRA Generator",
      "description": "Generates prompted SDXL image batches with completed LoRAs from the connected LoRA Trainer project.",
      "category": "Output",
      "entry_function": "generate_lora_images",
      "arguments": ["model_name", "model_path", "prompt", "negative_prompt", "base_model_path", "image_count", "steps", "seed", "sampler", "aspect_ratio", "width", "height", "cfg_scale", "lora_strength", "reference_image", "denoise_strength", "prompt_weighting", "preview_interval"],
      "required_arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio"],
      "capabilities": ["image_generation", "text_prompt", "seed", "sampler", "batch", "aspect_ratio", "reference_image", "live_preview", "progress", "cancel"],
      "model_trainers": ["lora"],
      "generation_options": {
        "samplers": ["DPM++ 2M", "DPM++ SDE", "Euler", "Euler a", "DDIM"],
        "aspect_ratios": ["1:1 (Square)", "4:3 (Landscape)", "3:4 (Portrait)", "3:2 (Landscape)", "2:3 (Portrait)", "16:9 (Widescreen)", "9:16 (Vertical)"],
        "step_min": 1,
        "step_max": 150,
        "step_default": 30
      },
      "requires_confirmation": false,
      "enabled": true,
      "demo": false,
      "backend": {
        "type": "python",
        "module": "adam.tools.lora_generator",
        "function": "generate_lora_images"
      }
    },
    {
      "id": "showcase_video_renderer",
      "name": "Showcase Video Renderer",
      "description": "Composes images generated by the current job into ADAM's finished showcase MP4 interface.",
      "category": "Output",
      "entry_function": "render_showcase_video",
      "arguments": ["title", "display_seconds", "resolution", "models"],
      "required_arguments": ["title", "display_seconds", "resolution", "models"],
      "capabilities": ["video_generation", "progress", "cancel"],
      "requires_confirmation": false,
      "enabled": true,
      "demo": false,
      "backend": {
        "type": "python",
        "module": "adam.showcase",
        "function": "render_showcase_video"
      }
    },
    {
      "id": "completion_notifier",
      "name": "Completion Notification",
      "description": "Records pipeline completion and makes the output easy to open.",
      "category": "System",
      "entry_function": "notify_complete",
      "arguments": ["project_name"],
      "requires_confirmation": false,
      "enabled": true,
      "demo": true,
      "backend": {
        "type": "python",
        "module": "adam.tools.demo_backends",
        "function": "notify_complete"
      }
    },
    {
      "id": "system_monitor",
      "name": "System Monitor",
      "description": "Reports CPU, RAM, GPU, VRAM, temperature, and active processes.",
      "category": "System",
      "entry_function": "inspect_system",
      "arguments": ["project_name"],
      "requires_confirmation": false,
      "enabled": true,
      "demo": false,
      "backend": {
        "type": "python",
        "module": "adam.tools.demo_backends",
        "function": "inspect_system"
      }
    },
    {
      "id": "ddpm_trainer",
      "name": "DDPM Trainer",
      "description": "Launches and monitors the connected DDPM trainer with safe, explicit run settings.",
      "category": "Training",
      "entry_function": "train_ddpm",
      "arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "resume_from", "resolution", "batch_size", "learning_rate", "gradient_accumulation_steps", "dataloader_num_workers", "mixed_precision", "save_every", "preview_steps", "training_intensity", "preview_enabled", "preview_every", "preview_prompt", "preview_seed"],
      "required_arguments": ["dataset_dir", "model_name", "epochs", "output_dir"],
      "capabilities": ["fresh_training", "resume_training", "progress", "pause", "cancel"],
      "requires_confirmation": true,
      "enabled": true,
      "demo": false,
      "backend": {
        "type": "python",
        "module": "adam.tools.ddpm_adapter",
        "function": "train_ddpm"
      }
    },
    {
      "id": "flow_trainer",
      "name": "Flow Matching Trainer",
      "description": "Launches and monitors the connected Rectified Flow image trainer.",
      "category": "Training",
      "entry_function": "train_flow",
      "arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "resume_from", "resolution", "batch_size", "learning_rate", "gradient_accumulation", "workers", "mixed_precision", "save_every", "preview_every", "preview_steps", "gradient_checkpointing", "preview_enabled", "preview_prompt", "preview_seed"],
      "required_arguments": ["dataset_dir", "model_name", "epochs", "output_dir"],
      "capabilities": ["fresh_training", "resume_training", "progress", "cancel"],
      "requires_confirmation": true,
      "enabled": true,
      "demo": false,
      "backend": {
        "type": "python",
        "module": "adam.tools.flow_adapter",
        "function": "train_flow"
      }
    }
  ]
}