#!/usr/bin/env python3
"""Ten31 CRM MCP server (Workstream C).

Exposes CRM reads, retrieval modes, and logged writes to the Claude Agent SDK
over MCP (stdio). All logic lives in crm_tools.py (tested independently); this
file is the thin transport wrapper.

Run:
    pip install mcp            # one-time (MCP Python SDK)
    CRM_DB_PATH=/data/crm.db python3 backend/mcp/server.py

Register with the Agent SDK / Claude Code as an stdio MCP server pointing at this
script. NO outbound/contact tools are exposed — that capability is gated to
Phase 3 behind the compliance review (CLAUDE.md guardrails #4, #6).
"""
import os
import sys

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
import crm_tools as t  # noqa: E402

from mcp.server.fastmcp import FastMCP  # noqa: E402

mcp = FastMCP("ten31-crm")


# ── reads ──
@mcp.tool()
def get_entity(lp_id: str) -> dict:
    """Fetch a canonical LP/organization/person entity by id, with its linked
    source records and interaction count."""
    return t.get_entity(lp_id)


@mcp.tool()
def search_records(query: str = "", entity_kind: str = "", limit: int = 20) -> dict:
    """Structured search over canonical entities by name substring and kind
    ('lp' | 'organization' | 'person')."""
    return t.search_records(query=query or None, entity_kind=entity_kind or None, limit=limit)


@mcp.tool()
def get_interaction_history(lp_id: str, limit: int = 20) -> dict:
    """Merged, dated interaction history (communications + fundraising grid notes)
    for a canonical entity."""
    return t.get_interaction_history(lp_id, limit=limit)


# ── retrieval modes ──
@mcp.tool()
def hybrid_search(query: str, top_k: int = 8, lp_id: str = "", doc_type: str = "",
                  date_from: int = 0, date_to: int = 0) -> dict:
    """Dense + BM25 + rerank retrieval (default; best for entity-heavy queries).
    Optional filters: lp_id, doc_type, date_from/date_to (epoch seconds)."""
    return t.hybrid_search(query, top_k=top_k, lp_id=lp_id or None, doc_type=doc_type or None,
                           date_from=date_from or None, date_to=date_to or None)


@mcp.tool()
def semantic_search(query: str, top_k: int = 8, lp_id: str = "", doc_type: str = "") -> dict:
    """Dense-only retrieval (high recall)."""
    return t.semantic_search(query, top_k=top_k, lp_id=lp_id or None, doc_type=doc_type or None)


@mcp.tool()
def keyword_search(query: str, top_k: int = 8, lp_id: str = "", doc_type: str = "") -> dict:
    """High-precision lexical retrieval (sparse leg + rerank)."""
    return t.keyword_search(query, top_k=top_k, lp_id=lp_id or None, doc_type=doc_type or None)


# ── writes (logged) ──
@mcp.tool()
def log_interaction(action: str, actor_type: str = "agent", actor_id: str = "",
                    target_id: str = "", payload: dict = None, source: str = "mcp") -> dict:
    """Append an entry to the append-only interaction log (guardrail #5)."""
    return t.log_interaction(action, actor_type=actor_type, actor_id=actor_id or None,
                             target_id=target_id or None, payload=payload, source=source)


@mcp.tool()
def set_entity_enrichment(lp_id: str, fields: dict, actor_id: str = "analyst") -> dict:
    """One-way enrichment write into a canonical entity (thesis_fit, segment,
    warmth_score, accreditation_status, etc.). Logged automatically."""
    return t.set_entity_enrichment(lp_id, fields, actor_id=actor_id)


if __name__ == "__main__":
    mcp.run()