"""UI-only task progress tool.

The tool gives the model a structured way to publish a short user-visible plan.
Its result is intentionally tiny; the full plan stays in the assistant tool_call
arguments for Web rendering and is compacted out of older LLM context.
"""
from __future__ import annotations

import json
from typing import Any, ClassVar

from .base import Tool


class TaskProgressTool(Tool):
    name = "task_progress"
    description = (
        "Publish the complete current user-visible progress checklist for this run. "
        "Use only for meaningful multi-step work. Every call must include the full checklist, "
        "including unchanged steps; never send a partial step patch. Keep stable step ids while "
        "revising the plan, allow at most one in_progress step, and mark every step completed "
        "before a successful final answer. This is a UI progress signal, not a work product."
    )
    parameters: ClassVar[dict[str, Any]] = {
        "type": "object",
        "additionalProperties": False,
        "properties": {
            "explanation": {
                "type": "string",
                "description": "Optional short reason when the plan materially changes.",
            },
            "steps": {
                "type": "array",
                "description": "The complete current checklist. Keep to 3-7 user-meaningful steps.",
                "items": {
                    "type": "object",
                    "additionalProperties": False,
                    "properties": {
                        "id": {"type": "string", "description": "Stable short id, e.g. s1."},
                        "title": {"type": "string", "description": "Short user-visible step title."},
                        "status": {
                            "type": "string",
                            "enum": ["pending", "in_progress", "completed"],
                        },
                    },
                    "required": ["id", "title", "status"],
                },
            },
        },
        "required": ["steps"],
    }

    def execute(self, **kwargs: Any) -> str:
        steps = kwargs.get("steps")
        out: dict[str, Any] = {
            "ok": True,
            "step_count": len(steps) if isinstance(steps, list) else 0,
        }
        return json.dumps(out, ensure_ascii=False, separators=(",", ":"))
