MCP server

OneQAZ Trading Intelligence

io.github.wnsod/oneqaz-trading-mcp

Checked 28 Oct 2026, 09:03 UTC · Connected to it and listed its tools

Verifiable crypto/KR/US market intelligence for AI agents: signals, hash-chained ledger. Not advice.

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

46

Token cost

About 20,714 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

28 Oct 2026, 09:03 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http oneqaz-trading-mcp https://api.oneqaz.com/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.oneqaz-trading-mcp]
url = "https://api.oneqaz.com/mcp"

Gemini CLI

{"mcpServers":{"oneqaz-trading-mcp":{"httpUrl":"https://api.oneqaz.com/mcp"}}}

Cursor

{"mcpServers":{"oneqaz-trading-mcp":{"url":"https://api.oneqaz.com/mcp"}}}

VS Code

{"servers":{"oneqaz-trading-mcp":{"type":"http","url":"https://api.oneqaz.com/mcp"}}}

Antigravity

agy mcp add oneqaz-trading-mcp https://api.oneqaz.com/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"oneqaz-trading-mcp","server_url":"https://api.oneqaz.com/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"oneqaz-trading-mcp","server_url":"https://api.oneqaz.com/mcp"}

Tools

• analyze_trades ~440 tokens
  Purpose: Aggregate paper trades by day / pattern / symbol. Triggers (casual questions too): "how's the week been?", "이번 주 매매 성적 어때?", "which patterns are working?", "어떤 종목이 제일 잘 벌었어?", "break down the trades", "daily P&L summary?". When to call: pattern audits, period-over-period performance
• explain_decision ~427 tokens
  Purpose: Multi-layer explanation for a single symbol's recent signal. Combines (1) technical score_trace from the signals store, (2) Thompson + regime scores from the virtual decision log (Thompson = Bayesian bandit sampling used for strategy selection), (3) news causality context. Use t
• fetch ~492 tokens
  Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved server-side — also the bridge for clients without
• get_account_status ~232 tokens
  Purpose: Show which OneQAZ account (or API key) this connection uses, its plan and its rate limits. Requires a connected OneQAZ account — calling it without one prompts your AI app to connect (free, no API key needed; Pro users can use their key). All other tools work without an account. Tri
• get_active_predictions ~374 tokens
  Purpose: Currently pending predictions (outcome IS NULL). Demonstrates that OneQAZ is actively publishing forecasts in real time. Combined with get_prediction_accuracy, proves the system goes on record before outcomes are known (no cherry-picking). Triggers (casual questions too): "what are
• get_backtest_tuning_state ~372 tokens
  Purpose: Continuous self-calibration evidence. Each row shows the auto-tuned lag_hours and sensitivity per cell, derived from real backtest outcomes. Proves the system adapts to measured reality rather than static heuristics. Triggers (casual questions too): "does the system self-correct?",
• get_cross_market_correlation ~452 tokens
  Purpose: Cross-market lead-lag relationships and decoupling events. Shows how markets influence each other (correlations) and when they diverge (decoupling, e.g. BTC up while stocks down). Triggers (casual questions too): "do crypto and stocks move together?", "코인이랑 주식이 따로 노나?", "any dec
• get_daily_brief ~686 tokens
  Purpose: Single-call market overview — engine macro regime + score-ranked example signals + score-sign breadth + yesterday's paper-trading outcomes + active forecast count + narrative. Use this as the first call when answering "how is the market today?". Triggers (call this even for casual q
• get_feature_governance_state ~644 tokens
  Purpose: Current lifecycle state of external features (news, events) under 3-track statistical validation. Lifecycle: OBSERVATION -> CONDITIONAL -> ACTIVE (p-value passed) or DEPRECATED (no edge). Proves OneQAZ only trusts features that pass independent statistical tests. Triggers (casua
• get_feature_governance_status_tool ~264 tokens
  Purpose: Feature governance snapshot — OBSERVATION / CONDITIONAL / ACTIVE / DEPRECATED distribution + last 7-day transitions. Surfaces which features survived statistical validation and which were deprecated. Triggers (casual questions too): "which features are actually used?", "어떤 피처가 살아있어?
• get_group_context ~486 tokens
  Use when the question is about a sector, theme, ETF basket or index basket — its structure state and its members. Returns headline claims, the group's regime state per interval, its market, groups that co-move with it, member symbols with their latest signal bias, symbols the basket leads, and gaps.
• get_latest_decisions ~440 tokens
  Purpose: Track-B (signal-driven) paper-trading decision log (Track B = the signal-engine decision path — indicator/Thompson-sampling driven; Track A = the LLM judgement path, see get_llm_trading_decisions). Triggers (casual questions too): "what did the system decide?", "최근 가상매매 판단 보여줘",
• get_ledger_integrity ~454 tokens
  Purpose: Tamper-evidence for the prediction ledger — a daily SHA-256 hash chain over all created/resolved prediction rows, with the exact canonical recipe published so any third party can recompute and verify. Archive a chain_hash today; if history is ever silently edited, recomputation
• get_llm_trading_decisions ~351 tokens
  Purpose: Track-A (LLM-driven) paper-trading judgement log (Track A = the LLM judgement path, applied to trading only as a capped bias on top of engine signals; Track B = the signal-engine path, see get_latest_decisions). Triggers (casual questions too): "what does the AI think?", "AI는 뭘 사라고
• get_losing_positions ~276 tokens
  Purpose: Losing paper positions (ROI < 0). Convenience wrapper around get_positions(max_roi=-0.01). Triggers (casual questions too): "what's underwater?", "지금 뭐가 물려 있어?", "show me the red ones", "any positions in trouble?", "얼마나 손실 중이야?". When to call: drawdown / risk review. Prerequisites: none
• get_losing_trades ~268 tokens
  Purpose: Losing paper trades only (P&L < 0). Convenience wrapper around get_trade_history(max_pnl=-0.01). Triggers (casual questions too): "어디서 잃었어?", "show me the losses", "what went wrong?", "worst trades?", "손실 난 거래 뭐야?". When to call: failure-pattern review. Prerequisites: none. Next steps:
• get_macro_causality_graph_tool ~321 tokens
  Purpose: Lag-aware causal graph between macro categories (bonds / vix / forex / credit / inflation / liquidity / commodities). Returns only statistically significant lead-lag pairs (e.g. forex -> vix 7d rho=-0.41). Triggers (casual questions too): "what happens to VIX when bonds move?",
• get_macro_context ~465 tokens
  Use when the question is about a macro factor — rates and bonds, the dollar and FX, VIX, credit, liquidity, inflation, commodities or energy — and which markets or stocks it is linked to. Returns headline claims, the macro category's regime state per interval and its instruments, macro categories wi
• get_macro_influence_map ~404 tokens
  Purpose: Expose OneQAZ's pre-defined causal hypothesis map. Each macro category (bonds, forex, vix, credit, liquidity, inflation, commodities, energy) is mapped to a target market with lag_hours + sensitivity (static prior) and an `effective` block with the value the engine actually uses
• get_market_context ~406 tokens
  Use when the question is about a whole market (KR, US, CRYPTO) — its structure regime and how its groups are positioned. Returns headline claims, the market's structure and global macro regime, the macro categories assumed to affect it (with tuned lags), its groups ordered by distance from the middl
• get_monthly_accuracy_trend ~355 tokens
  Purpose: Monthly accuracy time series per (category, target_market, lag_bucket). Use to verify sustained performance and detect recent degradation. Triggers (casual questions too): "is accuracy improving?", "적중률이 좋아지고 있어?", "monthly performance trend?", "최근에 예측 성능 떨어졌어?", "show accuracy over
• get_news_causality_breakdown ~416 tokens
  UNVERIFIED — methodology under audit. Do not cite as evidence of predictive capability. Purpose: Counts of news items per internal label the pipeline assigns. ANTICIPATED = the item matched a scheduled/calendar event. SURPRISE_WITH_PRECURSOR = the item was flagged by the cascade-anomaly heu
• get_news_context ~469 tokens
  Use when the question is about a specific news item or a news keyword or event (FOMC, 반도체, tariff) — which markets, symbols and topics it is tagged with. Returns headline claims, the item (or keyword match counts), its title-similarity cluster, the market, symbol and topic tags assigned by the colle
• get_news_leading_indicator_performance ~498 tokens
  UNVERIFIED — methodology under audit. Do not cite as evidence of predictive capability. Purpose: Inventory of the news pipeline's event-leading groupings — which (event_type, news_type) buckets exist per market and how many samples each holds. The lead/score metrics themselves are withheld
• get_performance_metrics ~570 tokens
  Purpose: Portfolio-level performance metrics (MDD / Sharpe / Sortino / Calmar / monthly returns / equity curve) over a FIXED window — the single canonical computation path shared by the OneQAZ blog and external clients. Triggers (casual questions too): "what's the max drawdown?", "MDD 얼마야?",
• get_position_detail ~361 tokens
  Purpose: Per-symbol paper position deep-dive (position + recent trades + decisions). Triggers (casual questions too): "how's the BTC position doing?", "삼성전자 얼마나 벌고 있어?", "why are you holding X?", "그 종목 지금 수익률 어때?", "tell me about the AAPL position". When to call: full context for one ticker. Pre
• get_positions ~591 tokens
  Purpose: List current paper-trading positions, with dynamic filters (ROI / strategy / sort). Triggers (casual questions too): "what are you holding?", "current positions?", "뭐 들고 있어?", "what's the exposure / portfolio?", "any winners / losers right now?", "how's the book doing?". Paper-tradi
• get_prediction_accuracy ~776 tokens
  Purpose: Per-cell (category, target_market, lag_bucket) hit rates WITH the honest baselines (schema 1.1): persistence_accuracy (the null model — regimes are sticky, so raw accuracy mostly measures regime persistence, not alpha), skill_score with autocorrelation-corrected skill_ci_95, n_e
• get_profitable_positions ~283 tokens
  Purpose: Profitable paper positions (ROI > 0). Convenience wrapper around get_positions(min_roi=0.01). Triggers (casual questions too): "what's winning right now?", "지금 뭐가 수익 나고 있어?", "show me the green ones", "best open positions?", "어떤 종목이 잘 가고 있어?". When to call: quickly surface winning ticke
• get_resolved_predictions ~655 tokens
  Purpose: Raw, row-level prediction ledger — every macro regime prediction's full lifecycle (created_at -> resolved_at -> outcome). This is the auditable evidence behind get_prediction_accuracy's aggregates: AI agents can snapshot open predictions, wait, then verify outcomes themselves wi
• get_role_analysis ~413 tokens
  Purpose: Role-aware signal alignment per symbol (timing / trend / swing / regime) plus hierarchy alignment. Triggers (casual questions too): "is BTC bullish across timeframes?", "단기랑 장기가 같은 방향이야?", "multi-timeframe view for AAPL?", "시간대별 신호가 일치해?", "short-term vs long-term signal?". When to call
• get_sector_correlations_tool ~331 tokens
  Purpose: Intra-market ETF / group correlation matrix and auto-cluster output. Quantifies structural co-movement (e.g. ARKK <-> QQQ) for diversification and sector-avoidance reasoning. Triggers (casual questions too): "which sectors move together?", "어떤 섹터끼리 같이 움직여?", "am I too concentrat
• get_signal_calibration ~518 tokens
  Purpose: Reliability diagram data for Level-1 signal confidence — realized hit rate per confidence bucket ([0.5,0.6) ... [0.9,1.0]) with ECE summary. Lets an agent verify whether a 0.9-confidence signal actually hits ~90%. Triggers (casual questions too): "is your confidence calibrated?",
• get_signal_detail ~424 tokens
  Purpose: Per-symbol signal deep-dive — latest signal + history + feedback. Triggers (casual questions too): "why is BTC scored this way?", "그 시그널 근거가 뭐야?", "signal history for AAPL?", "이 종목 시그널 자세히 보여줘", "how has this signal performed before?". When to call: drilling into a single ticker's s
• get_signals ~875 tokens
  Purpose: Query signals with dynamic filters (symbol / interval / action / score / confidence). Triggers (casual questions too): "what's the signal for BTC / AAPL / 삼성전자?", "is X bullish or bearish?", "how does X look right now?". When the user asks whether to trade X, OneQAZ gives context (r
• get_strategy_distribution ~298 tokens
  Purpose: Per-strategy breakdown across current paper positions (count, avg P&L, win rate per strategy). Triggers (casual questions too): "what strategies are you running?", "무슨 전략 돌리고 있어?", "which strategy holds the most positions?", "전략별 성적 어때?", "is one strategy dominating?". When to call: div
• get_strategy_leaderboard ~537 tokens
  Purpose: Top RL-learned research strategies — GLOBAL pool + per-symbol partition. Layer E evidence (Layer E = strategy-performance tier of the 5-layer trust pyramid). The GLOBAL pool may include synthesized win_rate values, so per_symbol_leaderboard is the primary measured-edge surface f
• get_structure_calibration ~592 tokens
  Purpose: Level 2 (ETF / basket / sector granularity — Level 1 is individual symbols) prediction calibration. Returns hit_rate_ema per (market, group, interval, regime_bucket) with sample counts, **plus the majority-class baseline needed to interpret them**. This is measurement, NOT a cla
• get_structure_validation_history ~347 tokens
  Purpose: Daily validation history of Level 2 structure predictions (Level 2 = ETF / basket / sector granularity). Each row shows the hit_rate for a specific day, enabling time-series verification of sustained performance. Triggers (casual questions too): "sector accuracy over time?", "구조 예측
• get_symbol_context ~546 tokens
  Use when the question is about one stock or coin — its current signal state, the OneQAZ structure group it belongs to, and which assets have tracked lead-lag links to it. Returns self-contained headline claims first, then the symbol's observed state, upstream links (its group, macro/ETF/basket instr
• get_symbol_peer_links_tool ~356 tokens
  Purpose: Symbol-level lead-lag links (e.g. META -> AMZN, lag=15m, rho=+0.53). When `symbol` is set, only peers that lead or follow that symbol are returned. Triggers (casual questions too): "what moves before NVDA?", "이 종목보다 먼저 움직이는 종목 있어?", "which stocks follow AAPL?", "선행 종목 알려줘", "any ear
• get_system_card ~319 tokens
  Purpose: Start here. One-page card for an agent meeting OneQAZ for the first time: what it is (public beta, operated by an individual, SLA: none), coverage (markets, bar intervals, universe sizes), data freshness (ok / degraded per pipeline component), and the measured track record with weak
• get_trade_history ~560 tokens
  Purpose: Query paper-trading history with dynamic filters (action / P&L / time / symbol). Triggers (casual questions too): "what trades happened lately?", "최근 거래 내역 보여줘", "how did the BTC trades go?", "승률 어때?", "show me the trade log", "how many trades won this week?". When to call: past tra
• get_trade_outcomes_bulk ~603 tokens
  Purpose: 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 the signal prediction recorded before the position was opened. Built for pipeline consumers who need offline backtesting
• get_winning_trades ~270 tokens
  Purpose: Winning paper trades only (P&L > 0). Convenience wrapper around get_trade_history(min_pnl=0.01). Triggers (casual questions too): "what worked?", "뭐가 제일 잘 벌었어?", "show me the winners", "best trades lately?", "수익 난 거래 보여줘". When to call: success-pattern review. Prerequisites: none. Next
• search ~497 tokens
  Purpose: ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest score-ranked combined signals across crypto / kr_stock / us_stock. Returns result ids consumable by the `fetch` tool. Triggers: ChatGPT connectors and Deep Research call this au

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