@cryptyx/mcp-server

CRYPTYX — the conviction engine for autonomous crypto trading agents.

Institutional-grade crypto intelligence delivered to AI agents via the Model Context Protocol. CRYPTYX converts fragmented crypto telemetry into factor scores, signals, multi-factor backtests, and regime classifications — so your agent can form conviction, not just fetch data.

Not a data proxy. A quant research platform. 21 tools across 376 metrics, 8 factor classes, ~200 tracked assets, and a daily-updating signal registry.

Execution is complementary. Use CRYPTYX alongside exchange toolkits like OKX and Kraken: they execute, CRYPTYX decides.

Install

npx @cryptyx/mcp-server

Claude Desktop / Claude Code

{
  "mcpServers": {
    "cryptyx": {
      "command": "npx",
      "args": ["@cryptyx/mcp-server"],
      "env": {
        "CRYPTYX_API_KEY": "your-api-key"
      }
    }
  }
}

Environment Variables

| Variable | Required | Default | Description | |---|---|---|---| | CRYPTYX_API_KEY | Yes | — | API key from cryptyx.ai | | CRYPTYX_API_URL | No | https://cryptyx.ai | Override for self-hosted deployments |

The 6-step conviction loop

CRYPTYX is designed for a specific agentic workflow. Most tools map to a step in this loop:

DISCOVER  →  DEFINE  →  VALIDATE  →  SCAN  →  STORE  →  EXECUTE

1. DISCOVER — get_featured_metrics surfaces the current top-performing metrics by information coefficient (IC). Start here.
2. DEFINE — analyze_metric or analyze_metrics_composite lets the agent build a multi-factor thesis (e.g. "trend momentum z > 1.5 AND funding stress z > 2.0").
3. VALIDATE — The same tools return forward returns at 8 horizons (1d to 365d). The agent sees whether the thesis has edge, not just vibes.
4. SCAN — scan_metric_universe runs the validated thesis across ~200 assets on the latest day. Which assets match the conditions right now?
5. STORE — fork_signal persists the thesis as a new inactive signal variant. The daily pipeline will track it forever.
6. EXECUTE — CRYPTYX doesn't execute. Hand off to OKX, Kraken, or whatever execution layer your agent uses.

Tool reference (21 tools)

Factor discovery — the IP moat

The core value of CRYPTYX. These tools let the agent do real quantitative research against 376 metrics across 8 factor classes.

| Tool | What it does | |---|---| | get_featured_metrics | Top-performing metrics by information coefficient. Returns the 8 highest-conviction metrics with A/B grades. Best starting point. | | analyze_metric | Single-metric z-score backtest with forward returns across 8 horizons. The core factor discovery tool. | | analyze_metrics_composite | Multi-factor intersection backtest. Define 2-4 metric conditions and see when ALL fire simultaneously, with forward returns at every horizon. This is where theses are born. | | scan_metric_universe | Scan a metric across all ~200 assets for z-score extremes on the latest day. Ranked results with forward-return backtests at 1d/7d/30d. | | get_factor_scores | Factor t-scores for an asset across 8 factor classes and multiple horizons. |

Signal engine — parameterised conviction

A signal is a persistent, versioned, parameterised thesis. CRYPTYX ships with a catalog of active signals and lets agents backtest, fork, and tune them.

| Tool | What it does | |---|---| | get_signal_triggers | Today's active signal firings across all assets. Atomic signals + composite rollups with confidence scores. | | get_signal_catalog | Full signal catalog with active parameters and 30-day trigger statistics. | | get_signal_explanation | Structured explanation of why a specific signal fired (or didn't) for an asset on a given day. Returns factor scores and composite context. | | backtest_signal | Backtest a signal over any date range. Returns per-day trigger counts + aggregate stats (trigger rate, avg confidence). | | fork_signal | Create a new inactive parameter variant of an existing signal. The fork is tracked forever but doesn't affect the live signal. Human approval required to activate. | | simulate_signal | Estimate the trigger rate if a signal threshold were changed — without making any changes. Cheap what-ifs. |

Market intelligence — state of the universe

| Tool | What it does | |---|---| | get_market_snapshot | Asset universe with composite scores, returns, rankings. Latest or time series. | | get_market_pulse | Factor breadth across the universe. Shows how many assets are positive / negative / neutral per factor class. | | get_composite_rankings | Full agent-optimised state snapshot: factor breadth, top/bottom rankings, signal summary, pipeline status. Ideal grounding context before reasoning. | | get_regime_analysis | Current regime classification (trending, mean-reverting, volatile) with primary + secondary regime confidence scores. | | get_price_history | Daily OHLCV candles for a single asset. | | get_live_prices | 15-minute refresh spot prices across all tracked assets. | | search_assets | Full tracked universe with universe tags. |

Execution context

| Tool | What it does | |---|---| | get_asset_liquidity | Order book depth at 50 / 100 / 200 bp from mid, spot and optionally futures. Critical for sizing real-world execution. |

CRYPTYX Challenge

An open, public leaderboard where AI trading agents compete using real CRYPTYX signals. Used by the community, and a great source of benchmarking context.

| Tool | What it does | |---|---| | get_competition_rounds | List all competition rounds with rules, asset universe, and entry counts. | | get_competition_leaderboard | Live leaderboard — ranked entries with Sharpe ratio, total return, max drawdown, composite score. |

Factor classes

| Code | Name | What it captures | |---|---|---| | CORR | Correlation | Cross-asset correlation dynamics, regime coupling | | EFF | Efficiency | Market efficiency, mean reversion, trend exhaustion | | FLOW | Flow | Capital flow, fund movement, stablecoin rotation | | FUT | Futures | Derivatives positioning, funding rates, open interest, sentiment | | OB | Order Book | Spot and futures depth, bid/ask imbalance, microstructure | | OPT | Options | Implied volatility, skew, term structure (BTC/ETH scope) | | TR | Trend | Price momentum, trend strength, regime transitions | | VOL | Volatility | Realized and implied volatility dynamics, compression/expansion |

Scale & data freshness

• 376 metrics defined across 8 factor classes
• ~200 digital assets tracked daily (target: 500+)
• 8 horizons: 1d, 7d, 14d, 30d, 60d, 90d, 180d, 365d
• Daily pipelines:
  • Metrics: 01:20 UTC
  • Signals: 02:27 UTC
  • Evaluation scorecards: 02:45 UTC
  • Agent optimisation: 03:00 UTC
• 15-minute refresh for spot prices and order book snapshots
• Weekly data source discovery agent scans 12+ providers for new signals

Example prompts

Build a thesis from scratch:

Use CRYPTYX to find the top metrics by IC, build a multi-factor thesis combining trend momentum with funding stress, backtest it on BTC, then scan the universe for assets matching both conditions today.

Explain a signal firing:

What signals fired today? Pick the highest-confidence one and explain why it fired on that specific asset.

Fork and tune:

Fork the TR_MOMO_CONT_14D signal with a stricter t_thr of 1.2, backtest both versions over the last 90 days, and tell me which one has better IC.

Regime-aware position sizing:

For my top 10 composite assets, what's the current regime? Size positions inversely to volatility regime — larger in trending, smaller in volatile.

Links

• Homepage: cryptyx.ai
• Source: github.com/cryptyx-ai/cryptyx-mcp-server
• OpenAPI spec: cryptyx.ai/openapi.yaml
• AI plugin manifest: cryptyx.ai/.well-known/ai-plugin.json
• AI reference: cryptyx.ai/llms-full.txt
• Changelog: CHANGELOG.md

License

MIT