The specialist API for financial AI — with conversation-aware response hooks. Your AI agent shouldn't just see prices — it should be able to prove the signals it's acting on have worked, and know…
Oneqaz Trading Mcp is an MCP project maintained in the oneqaz-trading/oneqaz-trading-mcp repository. The maintainer describes its purpose as “The specialist API for financial AI — with conversation-aware response hooks. Your AI agent shouldn't just see prices — it should be able to prove…”. Read the project documentation for the complete scope.
The maintainer’s configuration uses https://api.oneqaz.com/mcp. Check the project’s account and authentication instructions when adding the endpoint to your client.
Project documentation · Repository · Discovery listing. This profile records the public documentation checked on 2026-10-08.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| get_daily_brief | Single-call market overview: macro regime + top 5 strong signals + yesterday's paper-trading P&L + active prediction count + Korean narrative. The natural… |
| get_ledger_integrity | Tamper-evidence for the prediction ledger: a daily SHA-256 hash chain over all created/resolved macro-regime prediction rows, with the exact canonical recipe… |
| get_resolved_predictions | Raw row-level prediction ledger: every macro regime prediction's full lifecycle (created_at → resolved_at → outcome) — audit the evidence judgment by judgment. |
| get_trade_outcomes_bulk | Cursor-paginated bulk export of the prediction → trade → outcome chain (paper trades with realized P&L, each linked best-effort — same symbol, 2h window — to… |
| get_performance_metrics | Portfolio-level MDD / Sharpe / Sortino / Calmar / win-rate per market and account type (paper / live), optional daily equity curve. Paper P&L is gross… |
| get_signal_calibration | Reliability diagram data for signal confidence: realized hit rate per confidence bucket with ECE summary — verify whether a 0.9-confidence signal actually hits |
| search | ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest strong signals across all three markets. Result ids… |
| fetch | Connector-standard fetch of a single result by id returned from search. |
| get_prediction_accuracy | Per-cell hit rates with persistence & majority-class baselines, skill score + autocorrelation-corrected CI, known_biases, ledger_health |
| get_backtest_tuning_state | Active tuning parameters + last recalibration timestamp |
| get_monthly_accuracy_trend | Monthly accuracy series per (category, target market, lag) — read against the baselines above |
| get_news_leading_indicator_performance | UNVERIFIED — coverage of news event groupings; lead-time / score metrics withheld while the methodology is audited |
| get_news_causality_breakdown | UNVERIFIED — counts per pipeline news label (ANTICIPATED / SURPRISE_WITH_PRECURSOR / SURPRISE); lead metrics withheld |
| get_feature_governance_state | Features by status (OBSERVATION/CONDITIONAL/ACTIVE/DEPRECATED) |
| get_structure_calibration | Sector / ETF-level hit rates with the majority-class baseline needed to read them — measurement, not an edge claim |
| get_structure_validation_history | Historical structure-validation scores |
| get_strategy_leaderboard | Paper-tested RL strategies; per_symbol_leaderboard is the measured surface (the GLOBAL pool may include synthesized values) |
| get_active_predictions | Currently-open macro predictions with outcome tracking |
| get_macro_influence_map | Macro → market causal hypotheses (lag hours + sensitivity) |
| get_cross_market_correlation | Cross-market correlation matrix |
| get_role_analysis | Role-based strategy analysis |
| get_signals | market_id, symbol, min_score, max_score, action_filter, interval |
| get_signal_detail | market_id, symbol, interval |
| explain_decision | market_id, symbol |
| get_sector_correlations_tool | Intra-market ETF/sector correlation matrix + auto-cluster (60d window, 6h refresh) |
| get_macro_causality_graph_tool | Lag-aware causality between 8 macro categories (bonds/vix/forex/credit/inflation/liquidity/commodities/energy) |
| get_symbol_peer_links_tool | Symbol-to-symbol lead-lag (e.g. META → AMZN 15min lag, ρ=+0.62) |
| get_feature_governance_status_tool | Feature lifecycle distribution + last-7-day status transitions |
| get_positions | Open paper positions with ROI |
| get_position_detail | Single position deep-dive |
| get_strategy_distribution | Position counts by strategy |
| get_trade_history | Closed paper trades (filters: action, P&L, time) |
| analyze_trades | Aggregate trade analytics |
| get_latest_decisions | Recent signal → decision transitions |
| get_llm_trading_decisions | LLM-generated decision logs |
{
"mcpServers": {
"oneqaz-trading": {
"url": "https://api.oneqaz.com/mcp"
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
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