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MarketTrace agent feed

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streamable-httpMITupdated 2mo ago

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

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What can you do with MarketTrace agent feed?

MarketTrace agent-feed

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

This repo is the front door โ€” 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 (0โ€“100) + same-sign streak.
get_liquidations_recent Cross-exchange liquidation totals for a window: USD, long/short split.
get_ohlcv Consolidated cross-exchange candles (5mโ€ฆ1d) for ATR/range/RV math.
get_conditional_outcomes Measured forward-return history after a stated condition โ€” 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 โ€” 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 โ†’ Connectors โ†’ Add custom connector โ†’ https://api.markettrace.ai/mcp โ†’ 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 โ€” 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 โ€” just a client.

Refresh the contract: tools.json is a {version, generated_at, tools} snapshot of the live server's tools/list โ€” 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 โ€” 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 โ€” longs or shorts?"

Terms

Informational market data only โ€” not financial advice. Privacy Policy ยท Terms of Service ยท Contact: support@markettrace.ai

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