markettrace-agent-feed

markettrace-agent-feed

Read-only crypto perps microstructure for AI agents: normalized cross-exchange market state (funding + multi-year percentile, OI, volume, CVD, order-book imbalance, liquidations, basis), OHLCV, 15-min state history, and measured conditional outcomes (historical base rates, not predictions) — 6 assets across Binance, Bybit, OKX and Hyperliquid, every metric with self-declared coverage and freshness

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MarketTrace agent-feed

Read-only crypto perps microstructure for AI agents — normalized cross-exchange market state with self-declared coverage and freshness on every metric. Facts and normalization, no verdicts: the agent interprets.

  • Hosted MCP: https://api.markettrace.ai/mcp (Streamable HTTP, OAuth — no API keys)
  • Official registry: ai.markettrace/agent-feed
  • Docs: https://markettrace.ai/agents

This repo is the front door — connection configs, the interface contract, and a thin stdio bridge. The data pipeline itself (4-venue ingest, archives, normalization) is not open source.


What it serves

6 assets (BTC, ETH, SOL, BNB, XRP, DOGE) across Binance, Bybit, OKX, Hyperliquid:

Tool What it answers
get_market_state One normalized snapshot: funding + multi-year percentile, OI, volume, CVD, order-book imbalance, liquidations, basis, drivers. "Is ETH positioning stretched?"
get_funding_percentile Current funding ranked against its own multi-year history (0–100) + same-sign streak.
get_liquidations_recent Cross-exchange liquidation totals for a window: USD, long/short split.
get_ohlcv Consolidated cross-exchange candles (5m…1d) for ATR/range/RV math.
get_conditional_outcomes Measured forward-return history after a stated condition — base rates instead of folklore. "What happened historically after funding above the 90th percentile?"
get_state_history Time series of any numeric state field from the 15-minute archive — the trend view behind the snapshot.

Data: funding rates, open interest, cumulative volume delta (CVD), order-book depth, liquidations, OHLCV candles.

Honesty model: every metric carries a coverage entry (venues, window depth, freshness); thin history answers with disclosed depth instead of made-up numbers; conditional outcomes go history_silent below the evidence floor; every response self-declares its age. Reports history, not predictions.

Connect

Claude (web/desktop): Settings → Connectors → Add custom connector → https://api.markettrace.ai/mcp → authorize (email magic link).

Claude Code:

claude mcp add --transport http markettrace https://api.markettrace.ai/mcp

Stdio-only clients (via the standard OAuth-capable bridge):

npx -y mcp-remote https://api.markettrace.ai/mcp

More client configs in examples/mcp-configs.md.

Local stdio bridge (this repo)

mcp_server.py is a zero-dependency stdio bridge: it starts and answers introspection (initialize, tools/list) with no credentials — the bundled tools.json is a snapshot of the hosted server's own contract. Tool calls are proxied to the hosted endpoint when MARKETTRACE_BEARER is set; without it they return a pointer to the hosted OAuth endpoint instead of data. It holds no methodology — just a client.

Refresh the contract: tools.json is a {version, generated_at, tools} snapshot of the live server's tools/list — regenerate it by capturing that response and stamping the current contract version (mirrors feed.version in get_market_state).

python3 mcp_server.py            # Python 3.9+, no dependencies

Or with Docker:

docker build -t markettrace-bridge . && docker run -i markettrace-bridge

Things to ask

  • "What's the market state for BTC — is positioning stretched?"
  • "What happened historically after funding above the 90th percentile?"
  • "How did open interest build over the last 3 days?"
  • "How much got liquidated on ETH in the last hour — longs or shorts?"

Terms

Informational market data only — not financial advice. Privacy Policy · Terms of Service · Contact: support@markettrace.ai

The bridge in this repo is MIT-licensed (LICENSE); the hosted service is governed by the Terms above.

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