From macro to individual stocks, AI infrastructure that reads market regimes
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For AI agents — MCPBundles Directory API
Recommended: Directory API v1 over HTTP for detail and anonymous invoke when eligible (not MCP JSON-RPC on the publisher URL; no user MCPBundles credentials on v1).
GET https://www.mcpbundles.com/api/directory/v1/servers/oneqaz-mcp-bee981a90f
GET https://www.mcpbundles.com/api/directory/v1/servers/oneqaz-mcp-bee981a90f?live=1 (optional live refresh)
GET https://www.mcpbundles.com/api/directory/v1/servers/oneqaz-mcp-bee981a90f/tools/<tool> (one tool: schema + invoke.url)
POST https://www.mcpbundles.com/api/directory/v1/servers/oneqaz-mcp-bee981a90f/tools/{tool}
POST body: flat JSON of tool argument names and values; not an arguments wrapper. Replace {tool} with a tool name from GET detail.
Restricted agent shell (ChatGPT Codex desktop, some IDE sandboxes):
Default command sandboxes often block outbound network. curl exit 6 or "Could not resolve host" for www.mcpbundles.com is usually sandbox DNS — not evidence this site is down.
Retry the same Directory API GET with sandbox_permissions "require_escalated" and a one-sentence justification on the shell tool (Codex on-request approval). Do not tell the user the site is unreachable from sandbox curl alone.
Example justification: Run the public MCPBundles Directory API search outside the command sandbox to complete the user's MCP server lookup.
If the agent has browser access (e.g. Codex @Browser): open https://www.mcpbundles.com/?q=<query> or stay on this page — no shell network needed. Use WebMCP search_mcp_servers then get_mcp_server (document.modelContext / page tools; refresh with fetchTools or getTools if a tool id is missing from the snapshot). Read-only directory search; same index as Directory API v1 GETs. Neither WebMCP nor Directory API v1 uses user MCPBundles credentials.
Without a browser: after escalated curl fails, use GET https://www.mcpbundles.com/?q=<query> with Accept: text/markdown for ranked results.
Hub MCP (connected session): discover_mcp_servers — same filter semantics as search_mcp_servers.
Contract: https://www.mcpbundles.com/agents.md
OpenAPI: https://www.mcpbundles.com/api/directory/v1/openapi.json
Guide: https://www.mcpbundles.com/guides/mcpbundles-directory-api
For MCP client apps (human connect):
MCP connect URL: https://api.oneqaz.com/mcp
Skill page: https://www.mcpbundles.com/skills/oneqaz-mcp-bee981a90f
Explore capabilities (39)
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Showing 25 of 39 tools from the public preview. Connect the server to inspect the full live tool list.
Get24
Other capabilities1
Get Trade History
read
get_trade_history
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 trade review, single-symbol post-mortem, win-rate audits.
Prerequisites: none.
Next steps: analyze_trades, market://{market_id}/signals/feedback.
Caveats: paper-trading data only (not real money). limit
Get Trade History — 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 trade review, single-symbol post-mortem, win-rate audits.
Prerequisites: none.
Next steps: analyze_trades, market://{market_id}/signals/feedback.
Caveats: paper-trading data only (not real money). limit
Analyze Trades — 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 review.
Prerequisites: get_trade_history recommended for raw rows first.
Next steps: market://{market_id}/signals/feedback for the upstream signals.
Caveats: max 30 days; empty result when no trades
Get Winning Trades — 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 steps: analyze_trades for breakdowns.
Caveats: paper-trading data only.
Args:
market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)
limit: Max results (default 10)
D
Get Losing Trades — 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: analyze_trades for breakdowns.
Caveats: paper-trading data only.
Args:
market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)
limit: Max results (default 10)
Disclaim
Get Positions — 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-trading positions (NOT real money).
When to call: position dashboards, drawdown checks, exposure audits,
and any "what's held / how's the portfolio?" question.
Prerequisites: market://{market_id}/statu
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From macro to individual stocks, AI infrastructure that reads market regimes
How do I connect OneQAZ to my AI agent?
Use the MCP endpoint listed on this page in your MCP client configuration. One-click install pills support Claude, Cursor, VS Code, and other hosts. Copy the remote MCP URL if your client needs a manual entry.
How many tools does OneQAZ provide?
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Get Position Detail — 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.
Prerequisites: confirm the symbol holds a position via get_positions.
Next steps: get_signal_detail, get_role_analysis.
Caveats: returns an error envelope when no position exists for the symbol.
Args:
Get Profitable Positions — 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 tickers.
Prerequisites: none.
Next steps: get_position_detail for full context.
Caveats: paper-trading data only.
Args:
market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)
Get Losing Positions — 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.
Next steps: get_position_detail, get_role_analysis.
Caveats: paper-trading data only.
Args:
market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)
limit: Max results
Get Strategy Distribution — 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: diversification audit, per-strategy performance check.
Prerequisites: get_positions recommended for raw rows.
Next steps: market://{market_id}/derived/strategy-fitness, signals/feedback.
Caveats: empty d
Get Latest Decisions — 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?", "최근에 뭐 샀어? 팔았어?",
"why did you buy X?", "show recent buy/sell calls", "오늘 매매 판단 뭐 했어?",
"any trades triggered today?".
When to call: review recent automated decisions and their outcomes.
Prerequisites: market://{m
Get Llm Trading Decisions — 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 해?",
"show the LLM's trade calls", "AI 판단 근거 보여줘", "does the AI agree with the signals?".
When to call: inspect LLM-generated reasoning and trade calls.
Prerequisites: none.
Next steps: get_latest
Get Signals — Purpose: Query research signals with dynamic filters (symbol / interval / action / score / confidence).
Triggers (casual questions too): "should I buy / sell X?", "살까 말까?", "good entry?",
"what's the signal for BTC / AAPL / 삼성전자?", "is X bullish or bearish?",
"any buy signals right now?". Returns a research signal + score (NOT an order or advice —
always surface the disclaimer). Pair with get_latest_decisions to show what the system did.
When to call: drilling into a specific signal
Get Signal Detail — Purpose: Per-symbol signal deep-dive — latest signal + history + feedback.
Triggers (casual questions too): "why is BTC a buy?", "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 signal context.
Prerequisites: confirm existence via get_signals first.
Next steps: get_role_analysis, get_position_detail.
Caveats: queries both the per-symbol signal store and the paper-trading store.
Disclai
Get Role Analysis — 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: multi-timeframe analysis, cross-role agreement checks.
Prerequisites: get_signal_detail recommended.
Next steps: market://{market_id}/unified/symbol/{symbol}, get_position_detail.
Caveats: based on
Get Prediction Accuracy — 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_effective vs
n_nominal, and accuracy_v2_only (post 2026-07-08 methodology cutover).
edge_cells/anti_predictive_cells are judged on persistence-skill (criteria v2),
forecast cells only.
Trig
Get Backtest Tuning State — Purpose: Continuous self-calibration evidence. Each entry 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?", "시스템이 스스로 보정해?",
"how is it calibrated?", "튜닝 상태 보여줘", "is it adapting to what actually happened?".
When to call: after get_prediction_accuracy, to show the system updates itself.
Prerequisites:
Get Monthly Accuracy Trend — 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 time".
When to call: after get_prediction_accuracy and get_backtest_tuning_state — completes the trust chain.
Prerequisites: get_prediction_accuracy recommended.
Next steps: none (trust chain complet
Get News Leading Indicator Performance — 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 from this response
while the calculation method is being audited.
Triggers: "what news event groupings does OneQAZ track?",
"뉴스 이벤트 분류 어떤 게 있어?", "how many news samples per event type?".
When
Get News Causality Breakdown — 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 heuristic
(macro -> ETF -> stock). SURPRISE = neither matched.
These are pipeline labels, not validated classifications; the labelling rule
and its lead/anticipation metrics are under audit a
Get Feature Governance State — 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 (casual questions too): "do you validate your own inputs?", "피처 검증은 어떻게 해?",
"which signals passed testing?", "통계 검증 통과한 피처 뭐야?", "how do you avoid junk features?".
When to call: meta-level trust audit
Get Structure Calibration — 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 claim of edge — as of 2026-09-18 the
measured skill (accuracy minus baseline) is negative in all three markets.
Triggers (casual questions too): "how good are your sector calls?", "섹터 예측 잘 맞아?",
Get Structure Validation History — 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 매일 검증해?",
"daily hit-rate trend?", "요즘 섹터 예측 성적 어때?", "is the sector edge holding up?".
When to call: after get_structure_calibration.
Prerequisites: none.
Next steps: get_monthly_accuracy_trend f
Get Strategy Leaderboard — 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 for trust auditing.
Triggers (casual questions too): "what are the best strategies?", "제일 잘 버는 전략 뭐야?",
"top strategies?", "전략 순위 보여줘", "which strategy has the best win rate?".
When to call: final
Get Active Predictions — 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 you predicting right now?", "지금 어떤 예측 걸려 있어?",
"current forecasts?", "예측을 미리 기록해 두는 거야?", "anything on the record before it resolves?".
When to call: to verify ongoing prediction activity.
Prerequ
Get Macro Influence Map — 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. Highest-transparency tool —
the causal reasoning is visible and measurable.
Triggers (casual questions too): "how do rates affect crypto?", "금리가 코인에 어떻게 영향 줘?",
"what's your causal model?", "예측 논리가 뭐야?", "which macro drives which market?".
When to call: when an AI wants to
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