"""search — semantic search across the user's notes and tasks.

Mirrors the existing fable-mcp contract so Claude's prior usage pattern keeps
working. Differences from fable-mcp:
  - calls services.embeddings.semantic_search_notes directly instead of HTTP
  - user_id comes from mcp.current_user_id() rather than a global API key
"""
from __future__ import annotations

from scribe.mcp._context import current_user_id
from scribe.services.embeddings import semantic_search_notes


async def search(
    q: str,
    content_type: str = "all",
    limit: int = 10,
    project_id: int = 0,
) -> dict:
    """Semantic search over the user's existing notes and tasks — Scribe's recall.

    Reach for this BEFORE answering a question about the user's work or starting
    a task: the user's second-brain almost always already holds related prior
    art. Check for an existing ticket before opening a new one (search with
    content_type='task'), and for prior notes/decisions before re-deriving them.
    Treating Scribe as the first place to look — not a place to only write — is
    the difference between it being a trustworthy record and a write-only log.

    Args:
        q: search query string.
        content_type: 'all' (default), 'note' (notes only), or 'task' (tasks only).
        limit: maximum number of results (1-50).
        project_id: Scope results to one project. PASS THE ACTIVE PROJECT'S ID
            whenever a project is in scope (the one you entered with
            enter_project) — otherwise this searches across ALL projects and
            bleeds unrelated work into the result set. 0 = search everything
            (use only when you genuinely want a cross-project sweep).

    Returns:
        {"results": [{"id", "title", "body", "is_task", "tags", "similarity"}],
         "total": int}
    """
    uid = current_user_id()
    limit = max(1, min(limit, 50))
    is_task = {"note": False, "task": True}.get(content_type)  # None => any
    raw = await semantic_search_notes(
        uid, q, limit=limit, is_task=is_task,
        project_id=project_id or None,
    )
    return {
        "results": [
            {
                "id": note.id,
                "title": note.title,
                "body": (note.body or "")[:240],
                "is_task": bool(note.is_task),
                "tags": list(note.tags or []),
                "similarity": float(score),
            }
            for score, note in raw
        ],
        "total": len(raw),
    }


def register(mcp) -> None:
    mcp.tool(name="search")(search)