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

from typing import Optional, Literal

from loguru import logger
from fastmcp import Context

from basic_memory.config import ConfigManager
from basic_memory import telemetry
from basic_memory.mcp.project_context import (
    detect_project_from_url_prefix,
    get_project_client,
    resolve_project_and_path,
)
from basic_memory.mcp.server import mcp
from basic_memory.schemas.base import TimeFrame
from basic_memory.schemas.memory import (
    ContextResult,
    EntitySummary,
    GraphContext,
    MemoryUrl,
    ObservationSummary,
    RelationSummary,
)


def _format_entity_block(result: ContextResult) -> str:
    """Format a single context result as a markdown block."""
    primary = result.primary_result
    lines = []

    # --- Header ---
    lines.append(f"## {primary.title}")
    if primary.permalink:
        lines.append(f"permalink: {primary.permalink}")
    # RelationSummary has no content field; Entity/Observation do
    if not isinstance(primary, RelationSummary) and primary.content:
        lines.append("")
        lines.append(primary.content)

    # --- Observations ---
    if result.observations:
        lines.append("")
        lines.append("### Observations")
        for obs in result.observations:
            lines.append(f"- [{obs.category}] {obs.content}")

    # --- Relations (from primary's related_results that are RelationSummary) ---
    relation_items: list[RelationSummary] = [
        r for r in result.related_results if isinstance(r, RelationSummary)
    ]
    if relation_items:
        lines.append("")
        lines.append("### Relations")
        for rel in relation_items:
            lines.append(f"- {rel.relation_type} [[{rel.to_entity}]]")

    # --- Related entities (non-relation related results) ---
    related_entities: list[EntitySummary | ObservationSummary] = [
        r for r in result.related_results if not isinstance(r, RelationSummary)
    ]
    if related_entities:
        lines.append("")
        lines.append("### Related")
        for item in related_entities:
            permalink = item.permalink if item.permalink else ""
            lines.append(f"- [[{item.title}]] ({permalink})")

    return "\n".join(lines)


def _format_context_markdown(graph: GraphContext, project: str) -> str:
    """Format GraphContext as compact markdown text.

    Produces a human-readable markdown representation that is much smaller
    than the equivalent JSON, suitable for LLM consumption when structured
    data isn't needed.
    """
    if not graph.results:
        uri = graph.metadata.uri or ""
        return f"No results found for '{uri}' in project '{project}'."

    parts = []

    # --- Title from first primary result ---
    first_title = graph.results[0].primary_result.title
    if len(graph.results) == 1:
        parts.append(f"# Context: {first_title}")
    else:
        uri = graph.metadata.uri or ""
        parts.append(f"# Context: {uri}")

    parts.append("")

    # --- Entity blocks separated by --- ---
    entity_blocks = [_format_entity_block(result) for result in graph.results]
    parts.append("\n\n---\n\n".join(entity_blocks))

    # --- Footer ---
    meta = graph.metadata
    primary_count = meta.primary_count or 0
    related_count = meta.related_count or 0
    parts.append("")
    parts.append("---")
    parts.append(
        f"*{primary_count} primary, {related_count} related"
        f" | depth={meta.depth} | project: {project}*"
    )

    return "\n".join(parts)


@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.

    Memory URL Format:
    - Use paths like "folder/note" or "memory://folder/note"
    - Pattern matching: "folder/*" matches all notes in folder
    - Valid characters: letters, numbers, hyphens, underscores, forward slashes
    - Avoid: double slashes (//), angle brackets (<>), quotes, pipes (|)
    - Examples: "specs/search", "projects/basic-memory", "notes/*"

    Timeframes support natural language like:
    - "2 days ago", "last week", "today", "3 months ago"
    - Or standard formats like "7d", "24h"

    Format options:
    - "json" (default): Structured JSON with internal fields excluded
    - "text": Compact markdown text for LLM consumption
    """,
    annotations={"readOnlyHint": True, "openWorldHint": False},
)
async def build_context(
    url: MemoryUrl,
    project: Optional[str] = None,
    workspace: Optional[str] = None,
    depth: str | int | None = 1,
    timeframe: Optional[TimeFrame] = "7d",
    page: int = 1,
    page_size: int = 10,
    max_related: int = 10,
    output_format: Literal["json", "text"] = "json",
    context: Context | None = None,
) -> dict | str:
    """Get context needed to continue a discussion within a specific project.

    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.

    Project Resolution:
    Server resolves projects using a unified priority chain (same in local and cloud modes):
    Single Project Mode → project parameter → default project.
    Uses default project automatically. Specify `project` parameter to target a different project.

    Args:
        project: Project name to build context from. Optional - server will resolve using hierarchy.
                If unknown, use list_memory_projects() to discover available projects.
        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)
        output_format: Response format - "json" for structured JSON dict,
            "text" for compact markdown text
        context: Optional FastMCP context for performance caching.

    Returns:
        dict (output_format="json"): Structured JSON with internal fields excluded
        str (output_format="text"): Compact markdown representation

    Examples:
        # Continue a specific discussion
        build_context("my-project", "memory://specs/search")

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

        # Get text output for compact context
        build_context("research", "memory://specs/search", output_format="text")

    Raises:
        ToolError: If project doesn't exist or depth parameter is invalid
    """
    # Detect project from memory URL prefix before routing
    if project is None:
        detected = detect_project_from_url_prefix(url, ConfigManager().config)
        if detected:
            project = detected

    # Convert string depth to integer if needed
    if isinstance(depth, str):
        try:
            depth = int(depth)
        except ValueError:
            from mcp.server.fastmcp.exceptions import ToolError

            raise ToolError(f"Invalid depth parameter: '{depth}' is not a valid integer")

    # URL is already validated and normalized by MemoryUrl type annotation

    with telemetry.operation(
        "mcp.tool.build_context",
        entrypoint="mcp",
        tool_name="build_context",
        requested_project=project,
        workspace_id=workspace,
        depth=depth or 1,
        timeframe=timeframe,
        page=page,
        page_size=page_size,
        max_related=max_related,
        output_format=output_format,
        is_memory_url=str(url).startswith("memory://"),
    ):
        async with get_project_client(project, workspace, context) as (client, active_project):
            with telemetry.contextualize(
                project_name=active_project.name,
                workspace_id=workspace,
                tool_name="build_context",
            ):
                logger.info(
                    f"MCP tool call tool=build_context project={active_project.name} "
                    f"url={url} depth={depth} timeframe={timeframe} output_format={output_format}"
                )

                # Resolve memory:// identifier with project-prefix awareness
                _, resolved_path, _ = await resolve_project_and_path(
                    client,
                    url,
                    active_project.name,
                    context,
                )

                # Import here to avoid circular import
                from basic_memory.mcp.clients import MemoryClient

                # Use typed MemoryClient for API calls
                memory_client = MemoryClient(client, active_project.external_id)
                graph = await memory_client.build_context(
                    resolved_path,
                    depth=depth or 1,
                    timeframe=timeframe,
                    page=page,
                    page_size=page_size,
                    max_related=max_related,
                )

                logger.info(
                    f"MCP tool response: tool=build_context project={active_project.name} "
                    f"uri={graph.metadata.uri or resolved_path} "
                    f"primary_count={graph.metadata.primary_count or 0} "
                    f"related_count={graph.metadata.related_count or 0} "
                    f"output_format={output_format}"
                )

                if output_format == "text":
                    return _format_context_markdown(graph, active_project.name)

                return graph.model_dump()