// MCP TOOL REFERENCE

ONEQAZ TRADING INTELLIGENCE TOOLS

32 tools from the OneQAZ Trading Intelligence MCP Server, categorised by risk level.

View the OneQAZ Trading Intelligence policy → Token cost: 6,571 tokens →

// ALL 32 ONEQAZ TRADING INTELLIGENCE TOOLS

READ 32 tools

Read analyze_trades Purpose: Aggregate paper trades by day / pattern / symbol. When to call: pattern audits, period-over-period... Read explain_decision Purpose: Multi-layer explanation for a single symbol's recent research signal. Combines (1) technical s... Read get_active_predictions Purpose: Currently pending predictions (outcome IS NULL). Demonstrates that OneQAZ is actively publishi... Read get_backtest_tuning_state Purpose: Continuous self-calibration evidence. Each entry shows the auto-tuned lag_hours and sensitivit... Read get_cross_market_correlation Purpose: Cross-market lead-lag relationships and decoupling events. Shows how markets influence each ot... Read get_daily_brief Purpose: Single-call market overview — macro regime + top 5 strong signals + yesterday's paper-trading ... Read get_feature_governance_state Purpose: Current lifecycle state of external features (news, events) under 3-track statistical validati... Read get_feature_governance_status_tool Purpose: Feature governance snapshot — OBSERVATION / CONDITIONAL / ACTIVE / DEPRECATED distribution + l... Read get_latest_decisions Purpose: Track-B (signal-driven) paper-trading decision log. When to call: review recent automated decision... Read get_llm_trading_decisions Purpose: Track-A (LLM-driven) paper-trading judgement log. When to call: inspect LLM-generated reasoning an... Read get_losing_positions Purpose: Losing paper positions (ROI < 0). Convenience wrapper around get_positions(max_roi=-0.01). When to... Read get_losing_trades Purpose: Losing paper trades only (P&L < 0). Convenience wrapper around get_trade_history(max_pnl=-0.01). W... Read get_macro_causality_graph_tool Purpose: Lag-aware causal graph between macro categories (bonds / vix / forex / credit / inflation / li... Read get_macro_influence_map Purpose: Expose OneQAZ's pre-defined causal hypothesis map. Each macro category (bonds, forex, vix, cre... Read get_monthly_accuracy_trend Purpose: Monthly accuracy time series per (category, target_market, lag_bucket). Use to verify sustaine... Read get_news_causality_breakdown Purpose: Three-bucket news classification proving systematic discrimination between anticipated and sur... Read get_news_leading_indicator_performance Purpose: Evidence that OneQAZ detects price moves BEFORE news publication. Returns leading_score, avg_l... Read get_position_detail Purpose: Per-symbol paper position deep-dive (position + recent trades + decisions). When to call: full con... Read get_positions Purpose: List current paper-trading positions, with dynamic filters (ROI / strategy / sort). When to call: ... Read get_prediction_accuracy Purpose: Long-term hit rate per (category, target_market, lag_bucket) cell, with sample_count and Wilso... Read get_profitable_positions Purpose: Profitable paper positions (ROI > 0). Convenience wrapper around get_positions(min_roi=0.01). When... Read get_role_analysis Purpose: Role-aware signal alignment per symbol (timing / trend / swing / regime) plus hierarchy alignment.... Read get_sector_correlations_tool Purpose: Intra-market ETF / group correlation matrix and auto-cluster output. Quantifies structural co-... Read get_signal_detail Purpose: Per-symbol signal deep-dive — latest signal + history + feedback. When to call: drilling into a si... Read get_signals Purpose: Query research signals with dynamic filters (symbol / interval / action / score / confidence). Whe... Read get_strategy_distribution Purpose: Per-strategy breakdown across current paper positions (count, avg P&L, win rate per strategy). Whe... Read get_strategy_leaderboard Purpose: Top RL-learned research strategies — GLOBAL pool + per-symbol partition. Layer E evidence. The... Read get_structure_calibration Purpose: Level 2 (ETF / basket / sector) prediction calibration. Returns hit_rate_ema per (market, grou... Read get_structure_validation_history Purpose: Daily validation history of Level 2 structure predictions. Each row shows the hit_rate for a s... Read get_symbol_peer_links_tool Purpose: Symbol-level lead-lag links (e.g. META -> AMZN, lag=15m, rho=+0.53). When `symbol` is set, onl... Read get_trade_history Purpose: Query paper-trading history with dynamic filters (action / P&L / time / symbol). When to call: pas... Read get_winning_trades Purpose: Winning paper trades only (P&L > 0). Convenience wrapper around get_trade_history(min_pnl=0.01). W...

// RUNNING THIS SERVER

The managed route: connect OneQAZ Trading Intelligence through the PolicyLayer gateway — every tool call above is checked against your policy before it runs, with a full audit log.

DIRECT INSTALL (UNMANAGED) npx -y pypi:oneqaz-trading-mcp

// FAQ

How many tools does the OneQAZ Trading Intelligence MCP server have? +

The OneQAZ Trading Intelligence MCP server exposes 32 tools across 1 categories: Read.

How do I enforce policies on OneQAZ Trading Intelligence tools? +

Route the OneQAZ Trading Intelligence server through the PolicyLayer gateway. Define allow, deny, or approval rules per tool in the dashboard — they are enforced on every call before it reaches the server.

What risk categories do OneQAZ Trading Intelligence tools fall into? +

OneQAZ Trading Intelligence tools are categorised as Read (32). Each category has a recommended default policy.

Let agents act without letting them run wild.

Route your MCP servers through PolicyLayer and every tool call is checked against your policy before it runs — allow, deny, or require approval. Per-identity grants. Full audit log. Live in minutes.

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