"""MCP server for the web researcher.

Exposes a single tool `research` that delegates to WebResearcher.
Run with: python -m researchers.web.server
"""

import asyncio
import os
import sys
from typing import Optional

from mcp.server.fastmcp import FastMCP

from researchers.web.agent import WebResearcher
from researchers.web.models import ResearchConstraints

mcp = FastMCP(
    name="marchwarden-web-researcher",
    instructions=(
        "A Marchwarden web research specialist. "
        "Call the research tool with a question to get a grounded, "
        "evidence-based answer with citations, gaps, open questions, "
        "and confidence scoring."
    ),
)


def _read_secret(key: str) -> str:
    """Read a secret from ~/secrets file."""
    secrets_path = os.path.expanduser("~/secrets")
    with open(secrets_path) as f:
        for line in f:
            if line.startswith(f"{key}="):
                return line.split("=", 1)[1].strip()
    raise ValueError(f"Key {key} not found in {secrets_path}")


def _get_researcher() -> WebResearcher:
    """Create a WebResearcher with keys from ~/secrets."""
    return WebResearcher(
        anthropic_api_key=_read_secret("ANTHROPIC_API_KEY"),
        tavily_api_key=_read_secret("TAVILY_API_KEY"),
        model_id=os.environ.get("MARCHWARDEN_MODEL", "claude-sonnet-4-5-20250514"),
    )


@mcp.tool()
async def research(
    question: str,
    context: Optional[str] = None,
    depth: str = "balanced",
    max_iterations: int = 5,
    token_budget: int = 20000,
) -> str:
    """Research a question using web search and return a structured answer.

    Args:
        question: The question to investigate.
        context: What the caller already knows (optional).
        depth: Research depth — "shallow", "balanced", or "deep".
        max_iterations: Maximum number of search/fetch iterations (1-20).
        token_budget: Maximum tokens to spend (1000-100000).

    Returns:
        JSON string containing the full ResearchResult with answer,
        citations, gaps, discovery_events, open_questions, confidence,
        and cost_metadata.
    """
    researcher = _get_researcher()
    constraints = ResearchConstraints(
        max_iterations=max_iterations,
        token_budget=token_budget,
    )

    result = await researcher.research(
        question=question,
        context=context,
        depth=depth,
        constraints=constraints,
    )

    return result.model_dump_json(indent=2)


def main():
    """Run the MCP server on stdio."""
    mcp.run(transport="stdio")


if __name__ == "__main__":
    main()