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
9 assets (BTC, ETH, SOL, BNB, XRP, DOGE, HYPE, ZEC, ENA) across Binance, Bybit, OKX, Hyperliquid:
| Tool | What it answers |
|---|---|
get_market_state |
One normalized snapshot: funding + its trailing 2-year percentile, OI, volume, CVD, order-book imbalance, liquidations, basis, drivers. "Is ETH positioning stretched?" |
get_funding_percentile |
Current funding ranked against its own trailing 2-year window (0–100) + same-sign streak. |
get_liquidations_recent |
Cross-exchange liquidation notional estimates for a window: USD, long/short split. |
get_ohlcv |
Consolidated cross-exchange candles (5m…1d) with per-candle delta (taker buy − taker sell), for ATR, ranges, CVD and 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. |
get_volume_profile |
Volume-profile levels per UTC day from the consolidated tape: POC, value area high/low, value-area width and its rank, a multi-day composite and naked POCs. "Is price inside yesterday's value area?" |
get_big_trades |
Large aggressive orders (fills sharing venue, side and timestamp summed into one trade): per-side totals plus the biggest prints with venue, price and USD size. "Were the whale market orders buying or selling?" |
get_footprint_events |
Order-book wall events from the 1-minute footprint: absorbed and pulled walls with peak, executed and closing size in USD, plus thin-book minutes. "Were bid walls pulled before this drop?" |
get_stacked_imbalances |
Stacked footprint imbalances from the consolidated tape: diagonal buy and sell runs per candle (1m…1h) with price band and USD size. "Where did aggressive buyers stack up on BTC this morning?" |
help |
A guide to the server itself: every tool grouped by the question it answers, a working example call for each, the symbols, how to read the scales, the limits, and where data without its own tool lives (open interest, CVD, funding rate). "Which tool gives me open interest?" |
Data: funding rates, open interest, cumulative volume delta (CVD), order-book depth, liquidations, OHLCV candles with per-candle delta, volume-profile levels, large aggressive orders, order-book wall events, stacked footprint imbalances.
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).
Every client signs in the same way: it opens a browser, you enter your email, and a sign-in link arrives. Open it on the same device and in the same browser where you started.
Claude Code (adding the server does not sign you in, so run both):
claude mcp add --transport http markettrace https://api.markettrace.ai/mcp
claude mcp login markettrace
Codex:
codex mcp add markettrace --url https://api.markettrace.ai/mcp
codex mcp login markettrace
Cursor: add this to ~/.cursor/mcp.json (or use Add to Cursor on
https://markettrace.ai/agents) and sign in when Cursor asks:
{
"mcpServers": {
"markettrace": { "url": "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?"
- "Where are the key volume levels on BTC — any untested POCs nearby?"
- "Did a large resting order get absorbed on SOL in the last hour?"
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.