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 쨌쨌쨌 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 쨌쨌쨌 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 .

Related MCP server: binance-announcements-mcp

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 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

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쨌쨌‚         (any MCP-compatible client)              쨌쨌‚
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                   쨌쨌‚ stdio (JSON-RPC)
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쨌쨌‚              server.py (FastMCP)                  쨌쨌‚
쨌쨌‚        12 @mcp.tool() functions                  쨌쨌‚
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쨌쨌‚              tools/*.py                          쨌쨌‚
쨌쨌‚   Pure async functions with scoring algorithms   쨌쨌‚
쨌쨌‚                                                  쨌쨌‚
쨌쨌‚  accumulation 쨌쨌‚ whale    쨌쨌‚ impact   쨌쨌‚ smart_money 쨌쨌‚
쨌쨌‚  patterns     쨌쨌‚ correlation 쨌쨌‚ regime 쨌쨌‚ dca        쨌쨌‚
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쨌쨌‚            client.py (BinanceClient)             쨌쨌‚
쨌쨌‚   Async aiohttp 쨌쨌‚ Rate limiting 쨌쨌‚ No API key     쨌쨌‚
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                   쨌쨌‚ HTTPS
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쨌쨌‚         Binance Public API                       쨌쨌‚
쨌쨌‚   api.binance.com 쨌쨌‚ fapi.binance.com             쨌쨌‚
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Binance Endpoints Used

All endpoints are public and require no authentication:

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 쨌쨌쨌 no API keys or network access needed.

License

MIT