@Cryptyx/ Server

CRYPTYX — the intelligence layer for digital assets.

Institutional-grade digital asset intelligence delivered via the Model Context Protocol. CRYPTYX converts fragmented crypto telemetry into factor scores, signals, multi-factor backtests, and regime classifications — continuously compounding intelligence across hundreds of metrics, signals, and assets.

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. Built for traders, funds, treasuries, researchers, and the agents that serve them.

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

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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

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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.

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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.

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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

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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

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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.

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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