Web3 Signals MCP

Crypto signal intelligence for AI agents. 5 data dimensions, 20 assets, refreshed every 15 minutes.

Version: 0.1.0 Live API: https://web3-signals-api-production.up.railway.app MCP Endpoint: https://web3-signals-api-production.up.railway.app/mcp/sse Dashboard: web3-signals-api-production.up.railway.app/dashboard

What It Is

A signal fusion engine that scores 20 crypto assets from 0-100 by combining 5 independent data agents:

Each agent runs every 15 minutes. Scores are fused into a composite signal with directional labels (STRONG BUY to STRONG SELL), momentum tracking, and LLM-generated cross-dimensional insights.

What Problem It Solves

AI agents and trading systems need structured, multi-dimensional crypto intelligence — not raw price feeds. This API delivers scored, opinionated signals that combine what whales are doing, what derivatives markets are pricing, what the crowd is saying, and what technicals show — fused into a single actionable score with an LLM explanation of why.

Target Horizon

• Signal refresh: Every 15 minutes
• Accuracy evaluation: 24h, 48h, 7d windows
• Best for: Swing trades (hours to days), portfolio risk monitoring, market regime detection
• Not designed for: Sub-minute scalping or HFT

Assets Covered

BTC ETH SOL BNB XRP ADA AVAX DOT MATIC LINK UNI ATOM LTC FIL NEAR APT ARB OP INJ SUI

Connect via MCP

Add to your MCP config (Claude Desktop, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "web3-signals": {
      "url": "https://web3-signals-api-production.up.railway.app/mcp/sse"
    }
  }
}

Then ask your AI: "What are the current crypto signals?" or "Get me the BTC signal"

MCP Tools

REST API

Endpoints

Example: Single Asset Signal

curl https://web3-signals-api-production.up.railway.app/signal/BTC

{
  "asset": "BTC",
  "timestamp": "2026-02-24T21:49:42.513414+00:00",
  "signal": {
    "composite_score": 31.7,
    "label": "MODERATE SELL",
    "direction": "sell",
    "dimensions": {
      "whale": {
        "score": 7.9,
        "label": "STRONG SELL",
        "detail": "25 accumulate, 33 sell (ratio 43%); exchange inflow",
        "weight": 0.3
      },
      "technical": {
        "score": 35.2,
        "label": "MODERATE SELL",
        "detail": "RSI 30; MACD bullish; trend bearish",
        "weight": 0.25
      },
      "derivatives": {
        "score": 25.0,
        "label": "STRONG SELL",
        "detail": "L/S 0.69",
        "weight": 0.2
      },
      "narrative": {
        "score": 63.5,
        "label": "MODERATE BUY",
        "detail": "vol 0.97 (106 mentions); LLM neutral; trending; 3 sources",
        "weight": 0.15
      },
      "market": {
        "score": 60.0,
        "label": "MODERATE BUY",
        "detail": "-0.8%; F&G 8 extreme fear",
        "weight": 0.1
      }
    },
    "momentum": "degrading",
    "prev_score": 42.1,
    "llm_insight": "Whale capitulation intensifying — 33 sellers dominating with exchange inflow. Derivatives flipped to strong sell. Divergence: narrative and market fear remain bullish, suggesting classic capitulation setup..."
  },
  "market_context": {
    "regime": "extreme_fear",
    "risk_level": "high",
    "signal_momentum": "degrading"
  }
}

Example: Portfolio Summary

curl https://web3-signals-api-production.up.railway.app/signal

{
  "status": "success",
  "timestamp": "2026-02-24T21:49:42+00:00",
  "data": {
    "portfolio_summary": {
      "top_buys": [
        {"asset": "ETH", "score": 53.2, "label": "NEUTRAL", "conviction": "moderate"},
        {"asset": "SUI", "score": 50.7, "label": "NEUTRAL", "conviction": "moderate"},
        {"asset": "DOT", "score": 49.4, "label": "NEUTRAL", "conviction": "moderate"}
      ],
      "top_sells": [
        {"asset": "SOL", "score": 36.9, "label": "MODERATE SELL"},
        {"asset": "XRP", "score": 34.0, "label": "MODERATE SELL"},
        {"asset": "BTC", "score": 31.7, "label": "MODERATE SELL"}
      ],
      "market_regime": "extreme_fear",
      "risk_level": "high",
      "signal_momentum": "degrading",
      "assets_improving": 0,
      "assets_degrading": 6
    },
    "signals": {
      "BTC": { "composite_score": 31.7, "label": "MODERATE SELL", "..." : "..." },
      "ETH": { "composite_score": 53.2, "label": "NEUTRAL", "..." : "..." }
    }
  }
}

Example: Performance / Reputation

curl https://web3-signals-api-production.up.railway.app/performance/reputation

{
  "status": "active",
  "reputation_score": 72,
  "accuracy_30d": 72.3,
  "signals_evaluated": 840,
  "signals_correct": 607,
  "by_timeframe": {
    "24h": {"total": 280, "hits": 196, "accuracy": 70.0},
    "48h": {"total": 280, "hits": 201, "accuracy": 71.8},
    "7d":  {"total": 280, "hits": 210, "accuracy": 75.0}
  },
  "by_asset": {
    "BTC": 75.0,
    "ETH": 70.0,
    "SOL": 68.5
  },
  "methodology": {
    "direction_extraction": "score >60 = bullish, <40 = bearish, 40-60 = neutral",
    "neutral_threshold": "price move <=2% = correct for neutral signals",
    "scoring": "binary (hit/miss)",
    "window": "30-day rolling",
    "timeframes": ["24h", "48h", "7d"],
    "price_source": "CoinGecko"
  }
}

Signal Labels

Performance Tracking

The system tracks its own signal accuracy — no self-reported claims:

• Snapshots captured every 12 hours (1 per asset, max 40/day)
• Evaluation at 24h, 48h, and 7d windows against actual price movement
• Direction match: Did the predicted direction (bullish/bearish/neutral) match the actual price move?
• Neutral threshold: Price move <=2% counts as correct for neutral signals
• Price source: CoinGecko (independent, no API key needed)
• Window: 30-day rolling, recalculated every evaluation cycle

Discovery Protocols

Project Structure

/api                  FastAPI server, dashboard, middleware
/mcp_server           MCP tool definitions (stdio + SSE)
/signal_fusion        Weighted score fusion engine
/whale_agent          On-chain flow tracking
/technical_agent      RSI, MACD, MA analysis
/derivatives_agent    Funding rate, OI, L/S ratio
/narrative_agent      Reddit, News, Trending, LLM sentiment
/market_agent         Price, volume, Fear & Greed
/shared               Storage layer, base agent, profile loader
/orchestrator         15-minute agent runner
README.md
AGENTS.md

Self-Hosting

git clone https://github.com/manavaga/web3-signals-mcp.git
cd web3-signals-mcp

cp .env.example .env
# Edit .env with your API keys

pip install -r requirements.txt
python -m api.server

Environment Variables

Roadmap

Near-term (building now)

• Calibration buckets — Group signals by score range (e.g. 70-80) and track accuracy per bucket. Answers: "When we say 75, how often is that actually bullish?" (needs 24h+ of accuracy data)
• Magnitude scoring — Move beyond binary hit/miss to measure how much the predicted move captured vs actual move. (needs 1 week of data)

Medium-term

• Confidence-weighted penalties — Penalize high-conviction misses more than low-conviction ones. A "STRONG BUY" that dumps should hurt reputation more than a "MODERATE BUY" that goes flat. (needs calibration data)
• Correlation vs BTC baseline — Compare signal accuracy against a naive "just follow BTC" strategy. If we can't beat that, the signal isn't adding value. (needs 30 days of data)

Future

• x402 micropayments — Pay-per-signal via HTTP 402
• Additional assets — Expand beyond 20
• More data sources — Twitter/X, Farcaster, CryptoPanic (currently disabled, pending API access)

License

MIT