binance-intelligence-mcp

MCP server providing 12 computed intelligence tools for Binance. Unlike raw API wrappers, each tool combines multiple Binance endpoints into derived analytics — accumulation detection, whale tracking, market impact simulation, smart money radar, candlestick pattern scanning, correlation matrix, regime classification, DCA backtesting, funding rate scanning, funding extremes detection, funding history analysis, and basis spread scanning.

No API keys needed — all tools use public Binance endpoints.

Installation

pip install binance-intelligence-mcp

Or install from source:

git clone https://github.com/mefai-dev/binance-intelligence-mcp.git
cd binance-intelligence-mcp
pip install .

Quick Start

Run the server:

binance-intelligence-mcp
# or
python -m binance_intelligence

MCP Client Configuration

Add to your MCP client config:

{
  "mcpServers": {
    "binance-intelligence": {
      "command": "binance-intelligence-mcp"
    }
  }
}

Or with Python module:

{
  "mcpServers": {
    "binance-intelligence": {
      "command": "python",
      "args": ["-m", "binance_intelligence"]
    }
  }
}

Tools

#	Tool	Description	Endpoints Used
1	detect_accumulation	Smart accumulation detector with 4 sub-scores	klines, openInterestHist, premiumIndex, takerBuySellRatio
2	scan_whale_trades	Large trade scanner with tier classification	aggTrades
3	simulate_market_impact	Order book walk simulator for slippage analysis	depth
4	smart_money_radar	6-factor smart money composite score	topLongShortPositionRatio, topLongShortAccountRatio, globalLongShortAccountRatio, takerBuySellRatio, openInterestHist, klines
5	scan_candlestick_patterns	Classic pattern detection with confidence scores	klines
6	compute_correlation_matrix	Pearson correlation between trading pairs	klines
7	classify_market_regime	ADX/ATR-based regime classification	klines, premiumIndex
8	backtest_dca	DCA vs lump-sum backtester	klines
9	scan_funding_rates	Funding rate heatmap across all futures pairs	premiumIndex, fundingInfo
10	detect_funding_extremes	Extreme funding rate arbitrage opportunities	premiumIndex, fundingInfo
11	analyze_funding_history	Historical funding rate analysis for a symbol	fundingRate
12	scan_basis_spread	Spot-futures basis spread (contango/backwardation)	premiumIndex

Tool Details

1. detect_accumulation

Detects smart accumulation by combining volume analysis, open interest trends, funding rate proximity, and taker buy/sell ratio into a composite score (0-100).

Parameters:

• symbols (list[str], optional): Trading pairs. Default: top 12 futures pairs.

Sub-scores:

• volume_surge: Current volume vs 20-period average
• oi_buildup: Open interest linear regression trend
• stealth_mode: Funding rate closeness to zero
• buyer_aggression: Taker buy ratio above neutral

Example output:

{
  "tool": "detect_accumulation",
  "count": 3,
  "results": [
    {
      "symbol": "ETHUSDT",
      "scores": {
        "volume_surge": 72.5,
        "oi_buildup": 65.3,
        "stealth_mode": 89.0,
        "buyer_aggression": 58.2
      },
      "composite": 70.1,
      "signal": "STRONG"
    }
  ]
}

2. scan_whale_trades

Scans recent aggregate trades to identify large orders. Classifies by tier: Dolphin ($50K-$250K), Whale ($250K-$1M), Mega (>$1M).

Parameters:

• symbols (list[str], optional): Trading pairs. Default: top 6 pairs.
• min_usd (float, optional): Minimum trade size. Default: 50000.

Example output:

{
  "tool": "scan_whale_trades",
  "results": [
    {
      "symbol": "BTCUSDT",
      "trade_count": 15,
      "total_buy_usd": 2450000,
      "total_sell_usd": 1230000,
      "net_pressure_usd": 1220000,
      "net_direction": "BUY",
      "biggest_trade": {
        "usd_value": 1200000,
        "side": "BUY",
        "tier": "MEGA"
      },
      "tiers": {"dolphin": 8, "whale": 5, "mega": 2}
    }
  ]
}

3. simulate_market_impact

Walks the live order book to simulate how a large market order would execute.

Parameters:

• symbol (str): Trading pair. Default: "BTCUSDT".
• side (str): "BUY" or "SELL".
• amount_usd (float): Order size in USD. Default: 100000.

Example output:

{
  "tool": "simulate_market_impact",
  "symbol": "BTCUSDT",
  "side": "BUY",
  "levels_consumed": 12,
  "avg_fill_price": 67542.30,
  "worst_fill_price": 67580.00,
  "slippage_pct": 0.056,
  "impact_rating": "MODERATE"
}

4. smart_money_radar

Combines 6 independent data factors into a composite score (0-100).

Parameters:

• symbols (list[str], optional): Default: top 12 pairs.

Factors (each scored -1 to +1):

1. Top trader position ratio
2. Top trader account ratio
3. Global long/short account ratio
4. Taker buy/sell ratio
5. Open interest trend
6. Price momentum

5. scan_candlestick_patterns

Detects classic candlestick patterns with confidence scores.

Parameters:

• symbols (list[str], optional): Default: top 12 pairs.
• interval (str): "1h" or "4h". Default: "4h".

Detected patterns: Hammer, Inverted Hammer, Bullish/Bearish Engulfing, Doji, Morning/Evening Star, Three White Soldiers, Three Black Crows.

6. compute_correlation_matrix

Computes Pearson correlation coefficients between close prices of multiple symbols.

Parameters:

• symbols (list[str], optional): 2-20 pairs. Default: top 8.
• interval (str): Default: "4h".
• limit (int): Lookback periods. Default: 90.

7. classify_market_regime

Classifies each symbol into one of four regimes using ADX, ATR, and volume analysis.

Parameters:

• symbols (list[str], optional): Default: top 12 pairs.

Regimes:

• TRENDING: Strong directional movement (ADX >= 25)
• RANGING: Low directional movement
• VOLATILE_BREAKOUT: High ADX + high ATR
• LOW_ACTIVITY: Low volume and volatility

8. backtest_dca

Backtests Dollar-Cost Averaging vs lump-sum investing over historical data.

Parameters:

• symbol (str): Default: "BTCUSDT".
• amount_per_interval (float): USD per purchase. Default: 100.
• interval_days (int): Days between purchases. Default: 7 (weekly).
• total_days (int): Historical lookback. Default: 365.

9. scan_funding_rates

Scans all futures pairs for current funding rates, producing a heatmap sorted by absolute rate.

Parameters:

• top_n (int, optional): Number of results. Default: 20.

Output includes: rate%, annualized APR, mark/index premium, minutes to next funding, direction (LONGS_PAY/SHORTS_PAY/NEUTRAL).

10. detect_funding_extremes

Detects extreme funding rates across all pairs with severity classification and arbitrage hints.

Severity levels: ELEVATED (>0.03%), HIGH (>0.05%), EXTREME (>0.1%)

Output includes: severity, opportunity score, urgency (IMMINENT/SOON/UPCOMING), arbitrage hint.

11. analyze_funding_history

Analyzes historical funding rates for a single symbol with comprehensive statistics.

Parameters:

• symbol (str): Default: "BTCUSDT".
• limit (int): Historical periods. Default: 500.

Output includes: average/median/std dev, trend direction, cumulative cost, annualized cost, volatility score (0-100), distribution.

12. scan_basis_spread

Scans spot-futures basis spread across all pairs, identifying contango and backwardation.

Parameters:

• top_n (int, optional): Number of results. Default: 20.

Output includes: basis%, state (CONTANGO/BACKWARDATION/FLAT), annualized basis from funding rates.

Architecture

┌─────────────────────────────────────────────────┐
│                 MCP Client                       │
│         (any MCP-compatible client)              │
└──────────────────┬──────────────────────────────┘
                   │ stdio (JSON-RPC)
┌──────────────────▼──────────────────────────────┐
│              server.py (FastMCP)                  │
│        12 @mcp.tool() functions                  │
└──────────────────┬──────────────────────────────┘
                   │
┌──────────────────▼──────────────────────────────┐
│              tools/*.py                          │
│   Pure async functions with scoring algorithms   │
│                                                  │
│  accumulation │ whale    │ impact   │ smart_money │
│  patterns     │ correlation │ regime │ dca        │
└──────────────────┬──────────────────────────────┘
                   │
┌──────────────────▼──────────────────────────────┐
│            client.py (BinanceClient)             │
│   Async aiohttp │ Rate limiting │ No API key     │
└──────────────────┬──────────────────────────────┘
                   │ HTTPS
┌──────────────────▼──────────────────────────────┐
│         Binance Public API                       │
│   api.binance.com │ fapi.binance.com             │
└─────────────────────────────────────────────────┘

Binance Endpoints Used

All endpoints are public and require no authentication:

Endpoint	Type	Used By
/fapi/v1/klines	Futures	accumulation, smart_money, patterns, correlation, regime, dca
/fapi/v1/aggTrades	Futures	whale
/fapi/v1/depth	Futures	impact
/fapi/v1/premiumIndex	Futures	accumulation, regime, funding_scan, funding_extremes, basis_spread
/futures/data/openInterestHist	Futures	accumulation, smart_money
/futures/data/topLongShortPositionRatio	Futures	smart_money
/futures/data/topLongShortAccountRatio	Futures	smart_money
/futures/data/globalLongShortAccountRatio	Futures	smart_money
/futures/data/takerlongshortRatio	Futures	accumulation, smart_money
/fapi/v1/fundingInfo	Futures	funding_scan, funding_extremes
/fapi/v1/fundingRate	Futures	funding_history
/api/v3/klines	Spot	(available)

Development

git clone https://github.com/mefai-dev/binance-intelligence-mcp.git
cd binance-intelligence-mcp
python -m venv .venv
source .venv/bin/activate
pip install -e . pytest pytest-asyncio

Run tests:

pytest tests/ -v

All tests are mock-based — no API keys or network access needed.

License

MIT