Snowdrop MCP

"""Assess DEX liquidity depth for a potential trade."""
from __future__ import annotations

from datetime import datetime, timezone
from typing import Any

TOOL_META: dict[str, Any] = {
    "name": "liquidity_depth_analyzer",
    "description": "Estimates price impact and slippage for CFMM pools.",
    "inputSchema": {
        "type": "object",
        "properties": {
            "pool_reserves": {"type": "object"},
            "trade_size": {"type": "number"},
        },
        "required": ["pool_reserves", "trade_size"],
    },
    "outputSchema": {
        "type": "object",
        "properties": {
            "status": {"type": "string"},
            "data": {
                "type": "object",
                "properties": {
                    "price_impact_pct": {"type": "number"},
                    "slippage_pct": {"type": "number"},
                    "effective_price": {"type": "number"},
                    "recommendation": {"type": "string"},
                },
            },
            "timestamp": {"type": "string"},
        },
    },
}


def liquidity_depth_analyzer(
    pool_reserves: dict[str, Any],
    trade_size: float,
    **_: Any,
) -> dict[str, Any]:
    """Return price impact diagnostics for an xy=k pool."""

    try:
        token_a_reserve = float(pool_reserves.get("token_a_reserve"))
        token_b_reserve = float(pool_reserves.get("token_b_reserve"))
        if token_a_reserve <= 0 or token_b_reserve <= 0:
            raise ValueError("Pool reserves must be positive")
        if trade_size <= 0:
            raise ValueError("trade_size must be positive")

        mid_price = token_b_reserve / token_a_reserve
        k = token_a_reserve * token_b_reserve
        new_token_a = token_a_reserve + trade_size
        new_token_b = k / new_token_a
        token_b_out = token_b_reserve - new_token_b
        effective_price = token_b_out / trade_size
        price_impact = (effective_price - mid_price) / mid_price
        slippage = abs(price_impact) * 100

        if slippage < 1:
            recommendation = "proceed"
        elif slippage < 3:
            recommendation = "caution"
        else:
            recommendation = "abort"

        data = {
            "price_impact_pct": round(price_impact * 100, 4),
            "slippage_pct": round(slippage, 4),
            "effective_price": round(effective_price, 8),
            "recommendation": recommendation,
        }
        return {
            "status": "success",
            "data": data,
            "timestamp": datetime.now(timezone.utc).isoformat(),
        }
    except Exception as exc:
        _log_lesson("liquidity_depth_analyzer", str(exc))
        return {
            "status": "error",
            "data": {"error": str(exc)},
            "timestamp": datetime.now(timezone.utc).isoformat(),
        }


def _log_lesson(skill_name: str, error: str) -> None:
    """Append a Ralph Wiggum lesson entry."""

    with open("logs/lessons.md", "a", encoding="utf-8") as handle:
        handle.write(f"- [{datetime.now(timezone.utc).isoformat()}] {skill_name}: {error}\n")