"""Build context tool for Basic Memory MCP server."""

from typing import Optional

from loguru import logger

from basic_memory.mcp.async_client import client
from basic_memory.mcp.server import mcp
from basic_memory.mcp.tools.utils import call_get
from basic_memory.mcp.project_session import get_active_project
from basic_memory.schemas.base import TimeFrame
from basic_memory.schemas.memory import (
    GraphContext,
    MemoryUrl,
    memory_url_path,
    normalize_memory_url,
)


@mcp.tool(
    description="""Build context from a memory:// URI to continue conversations naturally.
    
    Use this to follow up on previous discussions or explore related topics.
    Timeframes support natural language like:
    - "2 days ago"
    - "last week" 
    - "today"
    - "3 months ago"
    Or standard formats like "7d", "24h"
    """,
)
async def build_context(
    url: MemoryUrl,
    depth: Optional[int] = 1,
    timeframe: Optional[TimeFrame] = "7d",
    page: int = 1,
    page_size: int = 10,
    max_related: int = 10,
    project: Optional[str] = None,
) -> GraphContext:
    """Get context needed to continue a discussion.

    This tool enables natural continuation of discussions by loading relevant context
    from memory:// URIs. It uses pattern matching to find relevant content and builds
    a rich context graph of related information.

    Args:
        url: memory:// URI pointing to discussion content (e.g. memory://specs/search)
        depth: How many relation hops to traverse (1-3 recommended for performance)
        timeframe: How far back to look. Supports natural language like "2 days ago", "last week"
        page: Page number of results to return (default: 1)
        page_size: Number of results to return per page (default: 10)
        max_related: Maximum number of related results to return (default: 10)
        project: Optional project name to build context from. If not provided, uses current active project.

    Returns:
        GraphContext containing:
            - primary_results: Content matching the memory:// URI
            - related_results: Connected content via relations
            - metadata: Context building details

    Examples:
        # Continue a specific discussion
        build_context("memory://specs/search")

        # Get deeper context about a component
        build_context("memory://components/memory-service", depth=2)

        # Look at recent changes to a specification
        build_context("memory://specs/document-format", timeframe="today")

        # Research the history of a feature
        build_context("memory://features/knowledge-graph", timeframe="3 months ago")

        # Build context from specific project
        build_context("memory://specs/search", project="work-project")
    """
    logger.info(f"Building context from {url}")
    url = normalize_memory_url(url)

    active_project = get_active_project(project)
    project_url = active_project.project_url

    response = await call_get(
        client,
        f"{project_url}/memory/{memory_url_path(url)}",
        params={
            "depth": depth,
            "timeframe": timeframe,
            "page": page,
            "page_size": page_size,
            "max_related": max_related,
        },
    )
    return GraphContext.model_validate(response.json())