DemonGatanjieuandClaude Fable 5.2
99ee111c81
Privacy mode left the AI blind twice over: the whole prompt was a token, so it could not help with light, lens or style, and model and LoRA file names still went out as written. - The prompt box has a first field, the gist: the picture in words the user writes for the AI to read. seal_answer hands it on as written beside the tokens of the private prompts; either alone can be handed in. - ai_sealed_models.py: while the Gates window's "Model names in a private task" is on (the default), each model of the library is one token ([M1], sealed as its model_id) however an answer writes it: model_id, loader name, file name in a text, card name. A model's own entry drops name, file, folder, links and hash and keeps type, base model, the author's settings and the user's note. expand writes a model token back as the model_id, as the loader's name among a node's values, as the card's name inside the AI's words. The task's menu lists what each token stands for. - The model editor has "Note for the AI" (ai_note, note_for_ai in the tools, searched by search_models): what a model is for, in words the AI may read. - A combo's name is sealed too: unless renamed it is made of its model's name and the start of its prompt. - The privacy skill says how to write around a gist and how to pick and pass on model tokens. Co-Authored-By: Claude Fable 5.2 <noreply@anthropic.com>
580 lines
28 KiB
Python
580 lines
28 KiB
Python
"""The MCP endpoint's (mcp_server.py) reading tools: views of the library (mcp_actions.py acts).
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`call_tool` runs any tool of a registry, these and mcp_actions.py's. search_models and get_model
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are offered through mcp_model_use.py, which adds what loads each model.
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Each tool is a plain function over the same stores the browser's pages use (model catalog
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and sidecars, output gallery, combos, workflow recipes, saved prompts, generated audio, scan
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results, activity log). They never write. Results are small, already-summarised JSON (an AI
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reads them), with ids the detail tools take back: model_id "type:path_idx:relative/path" and
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image "subfolder/file.png" under ComfyUI's output folder. A tool's mistake (bad id, missing
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file) comes back as a tool error the AI can read and correct, never as an exception.
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"""
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import base64
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import datetime
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import html
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import json
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import logging
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import os
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import re
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import folder_paths
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from .activity_log import list_entries
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from .audio_catalog import _gallery_page as _audio_page
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from .gallery_routes import _gallery_listing
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from .image_search import read_png_text
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from .kept_images import _kept_images
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from .material_store import _list_materials, _read_material, get_materials_dir
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from .media_routes import _build_card_thumbnail
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from .metadata import get_metadata
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from .picture_mask import input_facts
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from .mcp_arguments import prepare_arguments, renaming_note
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from .model_catalog import _iter_search_models
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from .model_constants import MEDIA_EXTENSIONS, MODEL_EXTENSIONS, PREVIEW_SUFFIXES
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from .notebooks import _list_notebooks
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from .parameters import _read_parameter_notebooks, get_parameters_dir
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from .path_utils import model_file_within, resolve_folder_subdir, resolve_within
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from .recipe_store import _list_recipes, get_recipes_dir
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from .scan_report import last_scan_path
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from .scan_summary import _summarize
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from . import example_workflows, workflow_route
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MAX_LIMIT = 50
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MAX_SEARCH_PICTURES = 6 # search_images(with_pictures): the first few attached
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MAX_TEXT = 1500
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MAX_PLANS = 12 # 方案 listed for one workflow
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MAX_IMAGE_BYTES = 1_500_000
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IMAGE_TYPES = {".png": "image/png", ".jpg": "image/jpeg", ".jpeg": "image/jpeg", ".webp": "image/webp"}
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SOURCES = {"civitai": "civitai", "local": "inferred_from_file", "": "not_scanned"}
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class ToolError(Exception):
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"""A problem the AI can fix (bad id, nothing there); shown to it as the tool's result."""
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# ---------------------------------------------------------------- helpers
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def _text(value, limit=MAX_TEXT):
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"""Plain text from HTML-ish Civitai descriptions, shortened."""
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value = html.unescape(re.sub(r"<[^>]+>", " ", str(value or "")))
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value = re.sub(r"\s+", " ", value).strip()
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return value if len(value) <= limit else value[:limit] + "…"
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def _when(seconds):
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try:
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return datetime.datetime.fromtimestamp(float(seconds)).isoformat(timespec="seconds")
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except (TypeError, ValueError, OverflowError, OSError):
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return None
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def _limit(value, default=20):
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try:
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return max(1, min(MAX_LIMIT, int(value)))
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except (TypeError, ValueError):
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return default
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def _terms(query):
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return [term for term in str(query or "").lower().split() if term]
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def _matches(terms, *texts):
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haystack = " ".join(str(text or "") for text in texts).lower()
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return all(term in haystack for term in terms)
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def _model_id(folder_type, path_idx, base_dir, file_path):
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return f"{folder_type}:{path_idx}:{os.path.relpath(file_path, base_dir).replace(os.sep, '/')}"
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def _resolve_model(model_id):
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"""(type, path_idx, base_dir, file_path) for a model_id from search_models."""
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parts = str(model_id or "").split(":", 2)
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if len(parts) != 3:
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raise ToolError("model_id looks like 'loras:0:folder/file.safetensors'; take it from search_models.")
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folder_type, path_idx, rel = parts
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rel = rel.replace("\\", "/").strip("/")
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subfolder, filename = os.path.split(rel)
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try:
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path_idx = int(path_idx)
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base_dir, target_dir = resolve_folder_subdir(folder_type, path_idx, "/" + subfolder)
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file_path = model_file_within(target_dir, filename)
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except (ValueError, KeyError, TypeError):
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raise ToolError(f"No models folder for {model_id}.")
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if not filename.lower().endswith(MODEL_EXTENSIONS) or not os.path.isfile(file_path):
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raise ToolError(f"No model file at {model_id}; it may have been moved or renamed.")
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return folder_type, path_idx, base_dir, file_path
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def _resolve_image(image):
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"""The output image at `image` ('subfolder/file.png'), checked to stay inside the output folder."""
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rel = str(image or "").replace("\\", "/").strip("/")
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try:
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path = resolve_within(folder_paths.get_output_directory(), rel)
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except ValueError:
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raise ToolError("image is a path under ComfyUI's output folder, as search_images returns it.")
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if os.path.splitext(path)[1].lower() not in IMAGE_TYPES or not os.path.isfile(path):
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raise ToolError(f"No output image at {image}: it is not (or no longer) in ComfyUI's output folder; "
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"the user may have deleted it. search_images lists the pictures there.")
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return rel, path
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def _resolve_folder_image(image, folder):
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"""(rel, path) of picture `image` ('subfolder/file.png') in ComfyUI's output, input (where
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LoadImage reads) or temp folder (where PreviewImage writes), checked to stay inside it."""
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if folder == "output":
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return _resolve_image(image)
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if folder not in ("input", "temp"):
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raise ToolError("folder is output, input or temp.")
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rel = str(image or "").replace("\\", "/").strip("/")
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try:
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path = resolve_within(getattr(folder_paths, f"get_{folder}_directory")(), rel)
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except ValueError:
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raise ToolError(f"image is a path under ComfyUI's {folder} folder.")
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if os.path.splitext(path)[1].lower() not in IMAGE_TYPES or not os.path.isfile(path):
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raise ToolError(f"No picture at {image} in ComfyUI's {folder} folder.")
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return rel, path
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def _image_content(path):
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"""An MCP image item: the 512 px thumbnail the gallery uses, or None when too big or unreadable."""
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thumb = _build_card_thumbnail(path)
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if not thumb:
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return None
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try:
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with open(thumb, "rb") as source:
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data = source.read(MAX_IMAGE_BYTES + 1)
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except OSError:
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return None
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if len(data) > MAX_IMAGE_BYTES:
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return None
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mime = IMAGE_TYPES.get(os.path.splitext(thumb)[1].lower(), "image/webp")
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return {"type": "image", "data": base64.b64encode(data).decode("ascii"), "mimeType": mime}
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def _kept_lookup():
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return {(item["subfolder"], item["filename"]): {kind: (value or {}).get("name") for kind, value in item.items()
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if kind in ("recipe", "combo", "prompt") and value}
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for item in _kept_images()}
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def _model_brief(folder_type, path_idx, base_dir, file_path, meta):
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rel_dir = os.path.relpath(os.path.dirname(file_path), base_dir).replace(os.sep, "/")
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try:
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size_mb = round(os.path.getsize(file_path) / 1048576, 1)
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except OSError:
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size_mb = None
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return {
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**({"note_for_ai": meta["ai_note"]} if meta.get("ai_note") else {}), # the user's words on what it is for
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"model_id": _model_id(folder_type, path_idx, base_dir, file_path),
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"name": meta.get("custom_name") or meta.get("name") or os.path.basename(file_path),
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"file": os.path.basename(file_path),
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"type": folder_type,
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"folder": "" if rel_dir == "." else rel_dir,
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"base_model": meta.get("baseModel") or "",
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"source": SOURCES.get(meta.get("info_source", ""), "not_scanned"),
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"trigger_words": list(meta.get("trainedWords") or [])[:8],
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"size_mb": size_mb,
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}
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def _each_model():
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"""Every model file once (folders registered twice, like unet and diffusion_models, count once).
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ComfyUI also registers custom_nodes, whose node packs carry files of their own: not the library."""
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seen = set()
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for folder_type, path_idx, base_dir, file_path in _iter_search_models():
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if folder_type == "custom_nodes" or not file_path.lower().endswith(MODEL_EXTENSIONS):
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continue
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real = os.path.normcase(os.path.realpath(file_path))
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if real in seen:
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continue
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seen.add(real)
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yield folder_type, path_idx, base_dir, file_path
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# ---------------------------------------------------------------- models and scans
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def search_models(query="", type="", base_model="", source="", limit=20):
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terms, wanted_base, found = _terms(query), str(base_model or "").lower(), []
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total = 0
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type = str(type or "").lower()
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for folder_type, path_idx, base_dir, file_path in _each_model():
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if type and type not in (folder_type, folder_type.rstrip("s")): # "checkpoint" finds the checkpoints too
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continue
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meta = get_metadata(file_path)
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if wanted_base and wanted_base not in str(meta.get("baseModel") or "").lower():
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continue
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if source and SOURCES.get(meta.get("info_source", ""), "not_scanned") != source:
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continue
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if not _matches(terms, os.path.relpath(file_path, base_dir), meta.get("name"), meta.get("custom_name"),
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meta.get("baseModel"), meta.get("ai_note"), " ".join((meta.get("trainedWords") or []) + (meta.get("headerWords") or []))):
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continue
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total += 1
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if len(found) < _limit(limit):
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found.append(_model_brief(folder_type, path_idx, base_dir, file_path, meta))
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return {"total_matches": total, "models": found}
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def get_model(model_id, include_cover=False):
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folder_type, path_idx, base_dir, file_path = _resolve_model(model_id)
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meta = get_metadata(file_path)
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detail = _model_brief(folder_type, path_idx, base_dir, file_path, meta)
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detail.update({
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"trigger_words": list(meta.get("trainedWords") or []),
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"civitai_name": meta.get("name") or "",
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"version": meta.get("version_name") or "",
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"civitai_url": meta.get("civitai_url") or "",
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"download_link": meta.get("source_url") or "",
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"user_notes": meta.get("custom_notes") or "",
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"fields_set_by_user": list(meta.get("user_fields") or []),
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"description": _text(meta.get("description")),
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"sha256": meta.get("hash") or "",
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})
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if meta.get("info_source") == "local":
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detail["why_not_on_civitai"] = meta.get("unmatched_reason") or "unknown"
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extra = []
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if include_cover:
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base = os.path.splitext(file_path)[0]
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cover = next((base + suffix for suffix in PREVIEW_SUFFIXES + MEDIA_EXTENSIONS
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if os.path.splitext(suffix)[1].lower() in IMAGE_TYPES and os.path.isfile(base + suffix)), None)
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item = _image_content(cover) if cover else None
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detail["cover"] = "attached" if item else "none"
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extra = [item] if item else []
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return detail, extra
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def library_overview():
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by_type, by_base = {}, {}
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for folder_type, _idx, _base, file_path in _each_model():
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by_type[folder_type] = by_type.get(folder_type, 0) + 1
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base = get_metadata(file_path).get("baseModel") or "unknown"
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by_base[base] = by_base.get(base, 0) + 1
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summary = _summarize()
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return {
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"models_by_type": dict(sorted(by_type.items(), key=lambda item: -item[1])),
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"models_by_base_model": dict(sorted(by_base.items(), key=lambda item: -item[1])[:20]),
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"scan": {key: summary[key] for key in ("total", "matched", "unmatched", "new", "pending")},
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"combos": len(_list_notebooks()),
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"workflow_recipes": len(_list_recipes(get_recipes_dir())),
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"saved_prompts": sum(1 for item in _list_materials(get_materials_dir()) if item.get("kind") == "prompt_plan"),
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}
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def scan_report():
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summary = _summarize()
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try:
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with open(last_scan_path(), encoding="utf-8") as source:
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last = json.load(source)
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except (OSError, ValueError):
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last = None
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report = {
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"counts": {key: summary[key] for key in ("total", "matched", "unmatched", "new", "pending")},
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"meaning": "matched: found on Civitai; unmatched: base model inferred from the file (reason says why); "
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"new: never scanned; pending: unmatched only because Civitai was not asked (offline or no connection).",
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"unmatched_models": summary["unmatched_models"][:MAX_LIMIT],
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"not_scanned_models": summary["new_models"][:MAX_LIMIT],
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}
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if isinstance(last, dict) and last.get("counts"):
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report["last_scan"] = {"finished": _when(last.get("finished")), "kind": last.get("kind"),
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"counts": last.get("counts"), "civitai_unreachable": bool(last.get("civitai_down")),
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"models": (last.get("files") or [])[:30]}
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return report
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# ---------------------------------------------------------------- outputs
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def search_images(query="", limit=20, page=1, with_pictures=False):
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images = _gallery_listing(folder_paths.get_output_directory(), False, str(query or "").strip())
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limit = _limit(limit)
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try:
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page = max(1, int(page))
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except (TypeError, ValueError):
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page = 1
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kept = _kept_lookup()
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chosen = images[(page - 1) * limit:page * limit]
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found = {
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"total_matches": len(images), "page": page,
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"images": [{"image": "/".join(part for part in (item["subfolder"], item["filename"]) if part),
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"created": _when(item.get("mtime")),
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**({"kept_as": kept[(item["subfolder"], item["filename"])]}
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if (item["subfolder"], item["filename"]) in kept else {})}
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for item in chosen],
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}
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if not with_pictures:
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return found
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pictures = [] # the first few themselves, in the order listed, so an AI that sees pictures need not ask for each
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for entry in found["images"][:MAX_SEARCH_PICTURES]:
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item = _image_content(_resolve_image(entry["image"])[1])
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entry["attached"] = bool(item)
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if item:
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pictures.append(item)
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return found, pictures
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def get_image_info(image, include_image=False, folder="output"):
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if folder != "output":
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rel, path = _resolve_folder_image(image, folder)
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info = {"image": rel, "folder": folder, "created": _when(os.path.getmtime(path)), **input_facts(path)}
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extra = [item for item in [_image_content(path)] if include_image and item]
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if include_image:
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info["image_attached"] = bool(extra)
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return info, extra
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rel, path = _resolve_image(image)
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info = {"image": rel, "created": _when(os.path.getmtime(path))}
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chunks = read_png_text(path) if path.lower().endswith(".png") else {}
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try:
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prompt = json.loads(chunks.get("prompt") or "null")
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except ValueError:
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prompt = None
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if isinstance(prompt, dict):
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nodes, models = [], set()
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for node_id, node in list(prompt.items())[:80]:
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if not isinstance(node, dict):
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continue
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inputs = {}
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for key, value in (node.get("inputs") or {}).items():
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if isinstance(value, str):
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inputs[key] = _text(value, 2000) if len(value) > 2000 else value
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if value.lower().endswith(MODEL_EXTENSIONS):
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models.add(value.replace("\\", "/"))
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elif isinstance(value, (int, float, bool)):
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inputs[key] = value
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nodes.append({"id": node_id, "type": node.get("class_type"), "inputs": inputs})
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info.update(models_used=sorted(models), nodes=nodes)
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if chunks.get("workflow"):
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info["to_change_it"] = f"open_workflow(workflow='{rel}') opens the workflow that made it, as it was"
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elif chunks.get("parameters"):
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info["parameters"] = _text(chunks["parameters"], 4000)
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else:
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info["note"] = "No generation settings are stored in this image."
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kept = _kept_lookup().get(tuple(rel.rsplit("/", 1)) if "/" in rel else ("", rel))
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if kept:
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info["kept_as"] = kept
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extra = []
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if include_image:
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item = _image_content(path)
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info["image_attached"] = bool(item)
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extra = [item] if item else []
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return info, extra
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def search_audio(query="", limit=20):
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items, total, _more = _audio_page(folder_paths.get_output_directory(), 1, _limit(limit), str(query or "").lower().strip())
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return {"total_matches": total, "audio": [{
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"file": "/".join(part for part in (item["subfolder"], item["filename"]) if part),
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"created": _when(item.get("mtime")),
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"speech": (item.get("generation") or {}).get("speech") or "",
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"voice_sample": (item.get("generation") or {}).get("sample") or "",
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"seed": (item.get("generation") or {}).get("seed"),
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} for item in items]}
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# ---------------------------------------------------------------- what the user kept
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def _model_name(model):
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if isinstance(model, dict):
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return model.get("filename") or model.get("name") or ""
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return str(model or "")
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def combo_view(note):
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"""A combo (one of _list_notebooks) as the AI and the materials picker see it, with its LoRAs and texts."""
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data = note.get("data") or {}
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if data.get("kind") == "nodes": # its own node structure: model and text slots by their names
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structure = data.get("structure") or {}
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values = data.get("values") or {}
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slots = structure.get("slots") or []
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models = [{"slot": s.get("label") or s.get("widget"), "folder": s.get("folder") or "",
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"file": os.path.basename(str(values.get(s["id"]) or "").replace("\\", "/"))}
|
|
for s in slots if s.get("kind") == "model"]
|
|
texts = [{"slot": s.get("label") or s.get("widget"), "text": _text(values.get(s["id"]), 400)}
|
|
for s in slots if s.get("kind") == "text"]
|
|
combo = {"name": note.get("name"), "kind": "node structure", "models": models, "texts": texts,
|
|
"nodes": [n.get("name") or n.get("type") for n in structure.get("nodes") or []],
|
|
"main_model": models[0]["file"] if models else "", "prompt": texts[0]["text"] if texts else ""}
|
|
return combo, [model["file"] for model in models[1:]] + [text["text"] for text in texts[1:]]
|
|
loras = [_model_name(lora) for lora in data.get("loras") or []]
|
|
return {"name": note.get("name"), "base_model": data.get("baseModel") or "", "main_model": _model_name(data.get("mainModel")),
|
|
"loras": loras, "prompt": _text(data.get("promptEn"), 800)}, loras
|
|
|
|
|
|
def list_combos(query="", limit=20, *, exact_name=False):
|
|
terms, found = _terms(query), []
|
|
for note in _list_notebooks():
|
|
combo, loras = combo_view(note)
|
|
if exact_name and combo["name"] != query:
|
|
continue
|
|
if not exact_name and not _matches(terms, combo["name"], combo["main_model"], combo["prompt"], " ".join(loras)):
|
|
continue
|
|
found.append(combo)
|
|
return {"total_matches": len(found), "combos": found[:_limit(limit)]}
|
|
|
|
|
|
def plans():
|
|
"""Each saved workflow's 方案: the parameter sets saved under it with values unlike its own (saving a workflow
|
|
keeps one equal to them, which the Workflows page leaves out too): {workflow file name: [(set file name, set name)]}."""
|
|
own = {recipe["filename"]: (recipe["data"].get("parameter_signature") or {}).get("value") for recipe in _list_recipes(get_recipes_dir())}
|
|
out = {}
|
|
for notebook in _read_parameter_notebooks(get_parameters_dir()):
|
|
workflow = notebook["data"].get("recipe_filename")
|
|
if workflow in own and not (own[workflow] and (notebook["data"].get("parameter_signature") or {}).get("value") == own[workflow]):
|
|
out.setdefault(workflow, []).append((notebook["filename"], notebook["name"]))
|
|
return out
|
|
|
|
|
|
def list_workflows(query="", limit=20, *, exact_name=False):
|
|
terms, found, saved_plans = _terms(query), [], plans()
|
|
for recipe in _list_recipes(get_recipes_dir(), with_workflow=True):
|
|
data = recipe.get("data") or {}
|
|
name = data.get("name") or recipe["filename"]
|
|
params = json.dumps(data.get("params") or {}, ensure_ascii=False)
|
|
own = [f"{name} / {plan}" for _file, plan in saved_plans.get(recipe["filename"], [])][:MAX_PLANS]
|
|
if exact_name and name != query:
|
|
continue
|
|
if not exact_name and not _matches(terms, name, " ".join(data.get("tags") or []), data.get("notes"), params, " ".join(own)):
|
|
continue
|
|
found.append({"name": name, "route": workflow_route.line(data.get("workflow")), "tags": data.get("tags") or [],
|
|
"notes": _text(data.get("notes"), 500), "saved": _when((data.get("timestamp") or 0) / 1000),
|
|
"settings": params if len(params) <= MAX_TEXT else params[:MAX_TEXT] + "…",
|
|
**({"plans": own} if own else {})}) # run_workflow(workflow=one of them) runs it with that set
|
|
fits = (lambda item: item["name"] == query) if exact_name else (lambda item: _matches(terms, item["name"], item["title"], item["notes"]))
|
|
return example_workflows.answer(found, fits, _limit(limit))
|
|
|
|
|
|
def saved_prompts():
|
|
"""Every saved prompt plan: {name, tags, positive, negative}."""
|
|
directory = get_materials_dir()
|
|
for summary in _list_materials(directory):
|
|
if summary.get("kind") != "prompt_plan" or summary.get("moved_to_recipe"):
|
|
continue
|
|
try:
|
|
plan = _read_material(os.path.join(directory, summary["filename"])).get("plan") or {}
|
|
except (OSError, ValueError, KeyError):
|
|
continue
|
|
yield {"name": summary.get("name"), "tags": summary.get("tags") or [],
|
|
"positive": _text(plan.get("positive"), 1200), "negative": _text(plan.get("negative"), 600)}
|
|
|
|
|
|
def list_saved_prompts(query="", limit=20):
|
|
terms = _terms(query)
|
|
found = [prompt for prompt in saved_prompts()
|
|
if _matches(terms, prompt["name"], " ".join(prompt["tags"]), prompt["positive"], prompt["negative"])]
|
|
return {"total_matches": len(found), "prompts": found[:_limit(limit)]}
|
|
|
|
|
|
def recent_activity(limit=10, source=""):
|
|
entries = list_entries(_limit(limit), source if source in ("canvas", "file") else None)
|
|
return {"entries": [{"time": _when(entry.get("time")), "source": entry.get("source"), "action": entry.get("action"),
|
|
"target": entry.get("target") or "", "detail": entry.get("detail") or {}} for entry in entries]}
|
|
|
|
|
|
# ---------------------------------------------------------------- registry
|
|
|
|
def _schema(properties=None, required=()):
|
|
schema = {"type": "object", "properties": properties or {}, "additionalProperties": False}
|
|
if required:
|
|
schema["required"] = list(required)
|
|
return schema
|
|
|
|
|
|
_QUERY = {"type": "string", "description": "Words that must all appear; empty lists all."}
|
|
_LIMIT = {"type": "integer", "minimum": 1, "maximum": MAX_LIMIT, "default": 20}
|
|
|
|
|
|
def _tool(name, title, description, run, properties=None, required=()):
|
|
return name, {"run": run, "spec": {
|
|
"name": name, "title": title, "description": description,
|
|
"inputSchema": _schema(properties, required),
|
|
"annotations": {"title": title, "readOnlyHint": True, "destructiveHint": False,
|
|
"idempotentHint": True, "openWorldHint": False},
|
|
}}
|
|
|
|
|
|
TOOLS = dict([
|
|
_tool("library_overview", "Library overview",
|
|
"How many models of each type and base model the user has, how many are scanned, and how many "
|
|
"combos, workflow recipes and saved prompts they kept. A good first call.", library_overview),
|
|
_tool("scan_report", "Scan status",
|
|
"Which models are matched on Civitai, which are not and why, which were never scanned, and what the last scan did.",
|
|
scan_report),
|
|
_tool("search_images", "Search output images",
|
|
"Output images, newest first, by words in their prompt, model or settings (a LoRA name, 'seed:123'). "
|
|
"with_pictures attaches the first 6 (512 px).",
|
|
search_images, {"query": _QUERY, "limit": _LIMIT, "page": {"type": "integer", "minimum": 1, "default": 1},
|
|
"with_pictures": {"type": "boolean", "default": False}}),
|
|
_tool("get_image_info", "Image generation settings",
|
|
"How an output image was made (prompts, models, seed, sampler…) and whether the user kept it. "
|
|
"folder=input: a picture LoadImage reads; folder=temp: a run's preview; both with their size and MASK "
|
|
"area. include_image attaches a 512 px copy; zoom_image shows a part larger.",
|
|
get_image_info, {"image": {"type": "string", "description": "From search_images; for folder=input, the name LoadImage takes."},
|
|
"include_image": {"type": "boolean", "default": False},
|
|
"folder": {"type": "string", "enum": ["output", "input", "temp"], "default": "output"}}, ["image"]),
|
|
_tool("list_combos", "Combos",
|
|
"The user's combos (搭配): a main model, LoRAs and a prompt kept together, or (kind \"node "
|
|
"structure\") model nodes and text nodes saved from the canvas with their links, listed by slot.", list_combos,
|
|
{"query": _QUERY, "limit": _LIMIT}),
|
|
_tool("list_workflows", "Workflow recipes",
|
|
"The user's saved workflow recipes: route, tags, notes, plans; settings when few match. `examples`: official ones by name, opened and run like saved ones.", list_workflows,
|
|
{"query": _QUERY, "limit": _LIMIT}),
|
|
_tool("list_saved_prompts", "Saved prompts",
|
|
"Prompts the user saved in the Prompt Studio, with positive and negative text.", list_saved_prompts,
|
|
{"query": _QUERY, "limit": _LIMIT}),
|
|
_tool("search_audio", "Generated audio",
|
|
"Speech the user generated (Anomalous TTS), newest first, with its text, voice and seed.", search_audio,
|
|
{"query": _QUERY, "limit": _LIMIT}),
|
|
_tool("recent_activity", "Activity log",
|
|
"What Anomalous changed recently, newest first: canvas edits it made and files it saved, scanned or deleted.",
|
|
recent_activity, {"limit": {**_LIMIT, "default": 10}, "source": {"type": "string", "enum": ["canvas", "file"]}}),
|
|
])
|
|
|
|
|
|
def tool_text(data):
|
|
"""A result as the AI reads it: JSON without spaces. Apps keep a long one out of the chat and
|
|
have the AI open it as a file (Antigravity past about 4,000 characters), so every character counts."""
|
|
return json.dumps(data, ensure_ascii=False, separators=(",", ":"))
|
|
|
|
|
|
def call_tool(tools, name, arguments, ctx=None):
|
|
"""An MCP tools/call result for tool `name` of `tools` (run in a worker thread). Tools marked
|
|
`context` get `ctx` ({base_url} of this server) first."""
|
|
tool = tools[name]
|
|
spec = tool["spec"]["inputSchema"]
|
|
arguments, renamed = prepare_arguments(spec, arguments)
|
|
unknown = set(arguments) - set(spec["properties"])
|
|
missing = [key for key in spec.get("required", []) if key not in arguments]
|
|
if unknown or missing:
|
|
wrong = "; ".join(part for part in (unknown and f"Unknown arguments {sorted(unknown)}", missing and f"missing {missing}") if part)
|
|
takes = ", ".join(key + (" (needed)" if key in spec.get("required", []) else "") for key in spec["properties"] if key != "task")
|
|
return _failure(f"{wrong}. {name} takes: {takes or 'nothing'}.")
|
|
try:
|
|
value = tool["run"](ctx, **arguments) if tool.get("context") else tool["run"](**arguments)
|
|
except ToolError as error:
|
|
return _failure(str(error))
|
|
except Exception as error: # noqa: BLE001 - an AI app gets a readable failure, never a dropped request
|
|
logging.getLogger(__name__).warning("Anomalous MCP tool %s failed", name, exc_info=True)
|
|
return _failure(f"{name} failed: {error}")
|
|
data, extra = value if isinstance(value, tuple) else (value, [])
|
|
if renamed and isinstance(data, dict):
|
|
data = {**data, "arguments_note": renaming_note(name, renamed)}
|
|
return {"content": [{"type": "text", "text": tool_text(data)}, *extra],
|
|
"structuredContent": data, "isError": False}
|
|
|
|
|
|
def retext(result, data):
|
|
"""A tool result with `data` as its structured content and as its text (the rest of its content kept)."""
|
|
result["structuredContent"] = data
|
|
content = list(result.get("content") or [])
|
|
text = {"type": "text", "text": tool_text(data)}
|
|
if content and isinstance(content[0], dict) and content[0].get("type") == "text":
|
|
content[0] = text
|
|
else:
|
|
content.insert(0, text)
|
|
result["content"] = content
|
|
|
|
|
|
def _failure(message):
|
|
return {"content": [{"type": "text", "text": message}], "isError": True}
|