# OneQAZ Trading Intelligence MCP server

Live market data, signals, positions, and macro analysis for crypto, KR stocks, and US stocks.

## Links
- Registry page: https://www.getdrio.com/mcp/io-github-wnsod-oneqaz-trading-mcp
- Repository: https://github.com/oneqaz-trading/oneqaz-trading-mcp

## Install
- Command: `uvx oneqaz-trading-mcp`
- Endpoint: https://api.oneqaz.com/mcp
- Auth: Not captured

## Setup notes
- Remote header: X-API-Key (secret)
- Package: Pypi oneqaz-trading-mcp v0.1.4
- Environment variable: DATA_ROOT
- Environment variable: MCP_SERVER_PORT
- Remote endpoint: https://api.oneqaz.com/mcp
- Header: X-API-Key

## Tools
- 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 capped at 1000.

Args:
    market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)
    limit: Max results (default 1000)
    action_filter: Filter by action (all, buy, sell)
    min_pnl: Min P&L % filter (e.g., -5.0)
    max_pnl: Max P&L % filter (e.g., 10.0)
    hours_back: Only trades within last N hours
    symbol: Filter by ticker symbol (e.g., "BTC", "AAPL"); case-insensitive

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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 in the window.

Args:
    market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)
    days: Analysis period in days (default 7, max 30)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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}/status recommended for context.
Next steps: get_position_detail, get_strategy_distribution.
Caveats: paper-trading data only. Positions are not real money holdings.
Disclaimer: Information only, not investment advice.


Args:
    market_id: Market ID (crypto, kr_stock, us_stock)
    min_roi: Min ROI % filter (e.g., -5.0)
    max_roi: Max ROI % filter (e.g., 10.0)
    strategy: Strategy filter (e.g., trend, scalping)
    sort_by: Sort field (profit_loss_pct, entry_timestamp, holding_duration, ai_score)
    sort_order: Sort direction (desc, asc)
    limit: Max results (default 1000) Endpoint: https://api.oneqaz.com/mcp
- 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:
    market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)
    symbol: Asset identifier (preferred; e.g., BTC, ETH, AAPL)
    coin: Legacy alias of symbol (kept for backward compatibility)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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)
    limit: Max results (default 20)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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 (default 20)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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 distribution when no positions are open.

Args:
    market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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://{market_id}/status recommended for context.
Next steps: get_trade_history, get_signals.
Caveats: paper-trading decisions only — no real-money order routing.

Args:
    market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)
    limit: Max results (default 10)
    decision_filter: Filter by decision (buy, sell, hold)
    hours_back: Only decisions within last N hours

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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_decisions to compare with Track B.
Caveats: paper-trading only.

Args:
    market_id: Market ID (crypto, kr_stock, us_stock, commodity, forex, bond)
    symbol: Specific symbol (optional; omit for entire market)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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 slice; symbol-by-symbol scanning;
    any "should I trade X?" question about a live symbol.
Prerequisites: market://{market_id}/signals/summary recommended for global view.
Next steps: get_signal_detail, get_role_analysis.
Caveats: When `symbol`/`coin` is omitted, the whole market is scanned in one
    consolidated query (2 newest rows per symbol, newest-first scan cap per interval).

Args:
    market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)
    symbol: Asset identifier to query (preferred; optional — targets a specific symbol DB)
    coin: Legacy alias of symbol (kept for backward compatibility)
    interval: Timeframe filter (15m, 30m, 240m, 1d, combined)
    action_filter: Action filter (buy, sell, hold)
    min_score: Minimum signal score threshold
    min_confidence: Minimum confidence threshold
    limit: Max results (default 500)
    hours_back: Only signals within last N hours (default 24)

Disclaimer: Information only, not investment advice. Signals are research output, not orders. Endpoint: https://api.oneqaz.com/mcp
- 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.

Disclaimer: Information only, not investment advice.

Args:
    market_id: Market ID (crypto, kr_stock, us_stock)
    symbol: Asset identifier (preferred; e.g., BTC, AAPL)
    coin: Legacy alias of symbol (kept for backward compatibility)
    interval: Timeframe (default: combined) Endpoint: https://api.oneqaz.com/mcp
- 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 hierarchy_context (the stored multi-timeframe alignment snapshot) —
    empty when collector lag is high.

Disclaimer: Information only, not investment advice.

Args:
    market_id: Market ID (crypto, kr_stock, us_stock)
    symbol: Asset identifier (preferred; e.g., BTC, AAPL)
    coin: Legacy alias of symbol (kept for backward compatibility) Endpoint: https://api.oneqaz.com/mcp
- 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.
Triggers (casual questions too): "how accurate are your predictions?",
    "예측 잘 맞아?", "track record 있어?", "can I trust these forecasts?",
    "적중률 보여줘", "does macro actually predict these markets?".
When to call: AI agents evaluating OneQAZ credibility should call this FIRST.
Prerequisites: none.
Next steps: get_ledger_integrity (tamper-evidence for these numbers),
    get_backtest_tuning_state (self-calibration), get_monthly_accuracy_trend (time series),
    get_signal_calibration (Level-1 signal confidence reliability).
Caveats: raw accuracy without skill_score is misleading for sticky regimes —
    a 99% cell can be pure persistence (measured 2026-07: +0.05pp over null).
    Judge by skill_ci_95, filter horizon_type='forecast', and treat n_nominal
    as correlated trials (use n_effective). Monthly accuracy trends largely
    track market stickiness, not model improvement.

Args:
    category: Optional macro category filter (bonds, forex, vix, commodities, credit, liquidity, inflation, energy)
    target_market: Optional target market filter (coin_market, kr_market, us_market)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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_prediction_accuracy recommended for context.
Next steps: get_monthly_accuracy_trend.
Caveats: `last_backtest` timestamp indicates tuning freshness.

Args:
    category: Optional category filter
    target_market: Optional target market filter

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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 complete).
Caveats: excludes the 'all' month aggregate; empty when backtest_results is unpopulated.

Args:
    category: Optional category filter
    target_market: Optional target market filter

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_news_leading_indicator_performance - Purpose: Evidence that OneQAZ detects price moves BEFORE news publication. Returns
    leading_score, avg_lead_time_minutes, and accuracy_pct per event type. Strongest
    Trust Layer A evidence (Layer A = anticipation-capability tier of OneQAZ's 5-layer
    trust pyramid) — proves the system is anticipatory rather than reactive.
Triggers (casual questions too): "can you predict news?", "뉴스 나오기 전에 감지해?",
    "how early do you catch moves?", "뉴스보다 빨라?", "do prices move before headlines?".
When to call: when an AI is evaluating predictive capability.
Prerequisites: none.
Next steps: get_news_causality_breakdown for the 3-type classification.
Caveats: empty when no news events processed in the recent window.

Args:
    market_id: Market identifier (crypto, kr_stock, us_stock, etc.)
    target_market: Alias for market_id (backward compat)
    min_sample_count: Minimum sample count for statistical significance (default 3)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_news_causality_breakdown - Purpose: Three-bucket news classification proving systematic discrimination between
    anticipated and surprise events. ANTICIPATED = scheduled + pre-move detected,
    SURPRISE_WITH_PRECURSOR = cascade anomaly (macro -> ETF -> stock) caught early,
    SURPRISE = pure unexpected.
Triggers (casual questions too): "was that news already priced in?", "그 뉴스 예견된 거였어?",
    "how many surprise events this week?", "돌발 뉴스 비율 어때?", "did the market see it coming?".
When to call: after get_news_leading_indicator_performance.
Prerequisites: none.
Next steps: market://{market_id}/external/causality for raw causality data.
Caveats: window limited to recent days.

Args:
    market_id: Market identifier
    days: Lookback window in days (default 7)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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 ("do they validate their own inputs?").
Prerequisites: none.
Next steps: none (meta evidence).
Caveats: empty when feature_gate_evaluator has not yet run cycles.

Args:
    market_id: Optional market filter (defaults to coin)
    target_market: Alias for market_id (backward compat)
    status_filter: Optional status filter (OBSERVATION, CONDITIONAL, ACTIVE, DEPRECATED)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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. Proves systematic edge at the sector-rotation level.
Triggers (casual questions too): "how good are your sector calls?", "섹터 예측 잘 맞아?",
    "sector rotation accuracy?", "그룹 단위 적중률 보여줘", "can you time sector moves?".
When to call: when an AI wants to see Layer D evidence (Layer D = sector-structure
    tier of the 5-layer trust pyramid).
Prerequisites: none.
Next steps: get_structure_validation_history for the daily trend.
Caveats: empty until structure-learning cycles complete.

Args:
    market_id: Optional market filter (crypto, kr_stock, us_stock)
    group_name: Optional group/sector filter (e.g., layer1, defi, sector, broad_index)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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 for the macro-level comparison.
Caveats: returns an overall_hit_rate summary across the window.

Args:
    market_id: Optional market filter
    days: Lookback window in days (default 90)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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 trust-validation step.
Prerequisites: none.
Next steps: market://{market_id}/signals/summary for live signals.
Caveats: `min_trades` filter enforces statistical validity. Strategies are paper-tested,
    not real-money executed.

Args:
    market_id: Market identifier (crypto, kr_stock, us_stock)
    target_market: Alias for market_id (backward compat)
    top_n: Top N strategies to return (default 20)
    limit: Alias for top_n (client-compat)
    min_trades: Minimum trades count for inclusion (default 10)
    include_per_symbol: Include per-symbol PG partition results (default True)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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.
Prerequisites: none.
Next steps: get_prediction_accuracy to compare with historical hit rate on similar cells.
Caveats: returns most recent first.

Args:
    target_market: Optional target market filter (coin_market, kr_market, us_market)
    limit: Max active predictions to return (default 20)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- 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 understand WHY we make certain predictions.
Prerequisites: none.
Next steps: get_backtest_tuning_state for runtime calibration of these hypotheses.
Caveats: static hypothesis only; see tuning state for current adjustments.

Args:
    market_id: Optional target market filter (coin_market, kr_market, us_market)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- explain_decision - Purpose: Multi-layer explanation for a single symbol's recent research 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 this when an AI must present
    a structured "why" rather than a raw verdict.
Triggers (casual questions too): "why is BTC bullish?", "왜 이 종목이 매수야?",
    "explain that signal", "판단 근거 설명해줘", "walk me through the reasoning".
When to call: when the user asks "why is this signal bullish/bearish?".
Prerequisites: identify the symbol via get_signals or get_latest_decisions first.
Next steps: none (this completes the explanation chain).
Caveats: `symbol` must match the per-symbol signal store filename (lowercase).
    Output is research evidence, NOT a buy or sell recommendation.

Args:
    market_id: Market identifier (crypto, kr_stock, us_stock; aliases coin/kr/us)
    symbol: Symbol to explain (e.g., btc, eth, 005930)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_cross_market_correlation - 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 decoupling lately?", "시장끼리 상관관계 어때?", "is BTC tracking the Nasdaq?".
When to call: when analyzing macro regime changes or divergent signals.
Prerequisites: none.
Next steps: get_macro_influence_map for the static causal hypotheses.
Caveats: correlation data may be empty until enough regime changes accumulate.

Args:
    source_market: Optional source market filter
    target_market: Optional target market filter

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_sector_correlations_tool - 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 concentrated?", "ETF 상관관계 보여줘", "is tech basically one trade right now?".
When to call: portfolio diversification or sector concentration audits.
Prerequisites: none.
Next steps: get_symbol_peer_links_tool for per-symbol lead-lag inside a sector.
Caveats: refreshed every 6 hours; 60-day lookback.

Args:
    market_id: coin / kr_stock / us_stock
    top_k: Number of top pairs to return

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_macro_causality_graph_tool - 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?", "금리 오르면 뭐가 움직여?",
    "which macro leads which?", "거시 지표끼리 인과관계 있어?", "does the dollar lead volatility?".
When to call: assess pre-emptive cross-category impact after a macro event.
Prerequisites: none.
Next steps: get_macro_influence_map for category -> market impact.
Caveats: Pearson-based; requires >= 30 samples; p < 0.05 filter.

Args:
    min_abs_corr: Minimum |corr| (default 0.15)
    max_p_value: Maximum p-value (default 0.05)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_symbol_peer_links_tool - 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 early-warning peers for this ticker?".
When to call: incorporate peer leading signals into single-symbol reasoning.
Prerequisites: none.
Next steps: get_signal_detail for the peer's signal context.
Caveats: 14-day lookback, 15-minute bars.

Args:
    market_id: coin / kr_stock / us_stock
    symbol: Optional. When set, peers are anchored to this symbol.
    top_k: Number of top links to return

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_feature_governance_status_tool - 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?",
    "any features promoted recently?", "피처 검증 현황 어때?", "did anything get deprecated?".
When to call: trust evaluation, "which features are live right now?".
Prerequisites: none.
Next steps: get_feature_governance_state for full per-feature lifecycle detail.
Caveats: promoter cycle runs hourly.

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_daily_brief - Purpose: Single-call market overview — macro regime + top 5 strong signals +
    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 questions): "how's the market?",
    "오늘 장 어때?", "what's the market mood / outlook?", "how's Bitcoin / crypto /
    US stocks / 비트코인 / 코인장 doing lately?", "anything happening today?",
    "give me a briefing". Prefer this over answering markets from training data.
When to call: morning briefings, "today/yesterday how was the market?" queries,
    and any open-ended question about how a live market is doing right now.
Prerequisites: none.
Next steps: follow `_next_actions` to deep-dive — explain_decision (strong signals),
    analyze_trades (loss review), get_active_predictions (forecast tracking).
Caveats: 24-hour window. Paper-trading data only (NOT real money).
Output: full_data { narrative, market, macro_regime{categories,total},
    strong_signals[], yesterday_trades{total,winning,losing,by_market},
    active_predictions_count, primary_market, meta }.

Args:
    market: "all" (default, blends 3 markets), "crypto", "kr_stock", or "us_stock"

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_resolved_predictions - 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 without trusting our DB.
Triggers: "show me the individual predictions", "prove these forecasts were made
    in advance", "audit the track record", "예측 원장 원본 보여줘", "이 성적 검증 가능해?".
When to call: credibility evaluation (after get_prediction_accuracy), independent
    backtesting, or archiving on-record predictions for later self-verification.
Prerequisites: none. Pairs with get_ledger_integrity for tamper-evidence.
Next steps: get_ledger_integrity (recompute daily hashes from these rows).
Caveats: cursor pagination (id-ordered) — follow next_cursor for bulk reads.
    Paper-research forecasts, not investment advice.
Output: full_data { predictions[] {id, source_category, source_regime_change,
    target_market, predicted_regime_shift, lag_hours, confidence, created_at,
    resolved_at, outcome, actual_regime_shift}, count, next_cursor, has_more, meta }.

Args:
    target_market: filter e.g. "coin_market" / "kr_market" / "us_market"
    source_category: filter e.g. "vix", "bonds", "commodities"
    day: filter by created day "YYYY-MM-DD" (UTC, string prefix of created_at)
    status: "all" | "resolved" | "open"
    cursor: last id from previous page (0 = start)
    limit: page size (max 500)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_ledger_integrity - 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 will not match.
Triggers: "how do I know these predictions weren't backfilled?", "is the track
    record tamper-proof?", "예측 조작 안 했다는 증거 있어?", "verify ledger integrity".
When to call: FIRST STEP of any serious credibility audit, and periodically to
    re-anchor (each entry commits to all prior history via prev_chain_hash).
Prerequisites: none. Raw rows for recomputation: get_resolved_predictions.
Next steps: get_resolved_predictions (fetch a day's raw rows, recompute its hash).
Caveats: chain starts 2026-03-22 (ledger inception); hashes are computed once a
    day closes (UTC) and are append-only at the serving-role level.
Output: full_data { recipe_version, recipe, chain_length, first_day, last_day,
    entries[] {day, created_count, resolved_count, created_hash, resolved_hash,
    prev_chain_hash, chain_hash, computed_at}, verification_hint }.

Args:
    days: how many most-recent chain entries to return (max 400)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_trade_outcomes_bulk - 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 that preceded entry. Built for pipeline consumers who
    need offline backtesting data, not conversational snippets.
Triggers: "give me your full trade history for backtesting", "bulk export trades",
    "예측이 실제 매매 성과로 이어졌는지 원데이터로 검증하고 싶다", "download outcomes".
When to call: offline verification, periodic ingestion into a research pipeline,
    or auditing whether signals translate into realized outcomes.
Prerequisites: none. For the prediction ledger itself use get_resolved_predictions.
Next steps: follow next_cursor until has_more=false; get_resolved_predictions to
    cross-check linked predictions against the tamper-evident ledger.
Caveats: linkage is temporal matching, NOT a foreign key (see meta.linkage).
    Paper trading only — envelope carries the standard disclaimer once per page.
Output: full_data { market, trades[] {id, symbol, action, entry/exit price+ts,
    profit_loss_pct, holding_duration, entry_signal_score, regime fields,
    policy_version, sizing fields, linked_prediction{...}|null}, count,
    linked_prediction_count, next_cursor, has_more, meta }.

Args:
    market: "crypto" (default) / "kr_stock" / "us_stock"
    cursor: last trade id from previous page (0 = start)
    limit: page size (max 500)
    days: exit-time window in days (max 120)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- search - Purpose: ChatGPT-connector-standard discovery search over OneQAZ's live surface —
    tools, resources, and the latest strong combined signals across crypto /
    kr_stock / us_stock. Returns result ids consumable by the `fetch` tool.
Triggers: ChatGPT connectors and Deep Research call this automatically for any
    user query routed to OneQAZ ("bitcoin signal", "prediction accuracy",
    "korean stocks today", ...). Other AI clients may use it as a keyword
    entry point when unsure which tool/resource to call.
When to call: first step of connector-style discovery. MCP-native clients can
    instead browse tools/list + resources/list directly.
Prerequisites: none.
Next steps: pass any result id to `fetch` for the full document.
Caveats: corpus is rebuilt at most every 10 minutes (tool/resource catalog +
    top-20 strong signals per market). Empty results list means no match.
Output: {results: [{id, title, url}], disclaimer, is_investment_advice,
    data_classification} — flat envelope, OpenAI fixed shape.

Args:
    query: free-text search string (English/Korean, symbols like BTC/AAPL)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- fetch - 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 MCP resource
    support, e.g. Gemini); `signal:{market}:{symbol}` returns the symbol's
    latest combined research signal.
Triggers: ChatGPT connectors / Deep Research call this after `search`. Clients
    without MCP resource support can call it directly with a known resource id,
    e.g. fetch("resource:market://global/summary").
When to call: whenever the full content behind a search result id is needed.
Prerequisites: a valid id — from `search` results or a known namespace id.
Next steps: for tool docs, call the named tool via tools/call; for signals,
    get_signal_detail / explain_decision for deeper evidence.
Caveats: uncovered resource uris return description-only text (no fabricated
    data). `text` is a JSON document for resource/signal ids.
Output: {id, title, text, url, metadata, disclaimer, is_investment_advice,
    data_classification} — flat envelope, OpenAI fixed shape.

Args:
    id: document id — "tool:{name}", "resource:{uri}", or
        "signal:{market}:{symbol}" (market: crypto / kr_stock / us_stock)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_signal_calibration - 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?",
    "confidence 0.9 믿어도 돼?", "시그널 확신도 실제 적중률 보여줘",
    "how reliable are signal confidences?".
When to call: before trusting get_signals confidence values as probabilities.
Prerequisites: none.
Next steps: get_prediction_accuracy (macro-layer skill), get_signals.
Caveats: snapshot is daily; observation window ≈ signals table retention
    (~2 weeks); n is nominal (correlated trials — see meta.sample_caveat).

Args:
    market_id: Optional filter (crypto | kr_stock | us_stock)
    interval: Optional candle interval filter (e.g. 15m, 30m, 240m, 1d)
    variant: "v1" (raw heuristic confidence, default) or "v2"
        (outcome-based shadow confidence — RCA C2, accumulating since 2026-07-21)

Disclaimer: Information only, not investment advice. Endpoint: https://api.oneqaz.com/mcp
- get_performance_metrics - 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?",
    "샤프 비율 보여줘", "monthly returns table?", "트랙레코드 지표", "에쿼티 커브 데이터".
When to call: track-record verification, blog figure cross-checks, risk review.
Prerequisites: none.
Next steps: get_trade_history for the underlying trades, analyze_trades for breakdowns.
Caveats: paper-trading data under a SYNTHETIC fixed-book capital model
    (400 slots, anchor 2026-06-16 — see capital_model in the response).
    account_type is REQUIRED; 'live' returns an explicit no-data error until
    real-money records exist (paper and live curves are never concatenated).
    Fixed window → same inputs always reproduce the same numbers (as-of verifiable).

Args:
    market: coin | kr | us | all (aliases crypto/kr_stock/us_stock accepted).
        'all' = fixed 1/3 allocation across the three books.
    account_type: REQUIRED. 'paper' (simulated) or 'live' (real — not yet available).
    window_start: ISO date (YYYY-MM-DD). Default 2026-06-16 (public track-record anchor).
    window_end: ISO date. Default today (KST).
    include_daily_curve: include per-day equity curve rows (default false).

Disclaimer: Information only, not investment advice. Simulated performance. Endpoint: https://api.oneqaz.com/mcp

## Resources
- market://health - 서버 헬스체크

Returns:
    서버 상태 정보 (status, timestamp, version)
[출력 스키마] status(str), timestamp(str:ISO8601), version(str), server(str), project_root(str). MIME type: text/plain
- market://info - 서버 정보 — 정적 자기소개 + 전체 카탈로그로의 포인터

[2026-07-08] 스테일 정리: 종전엔 레거시 SQLite 경로(SIGNAL_DIR_PATHS 등)를
데이터 소스로 소개하고 endpoints 를 축약판(18개)으로 나열해 meta/discovery 와
불일치했다 (Wave I 이후 실데이터는 전부 PG). 이제 데이터 소스는 PG 스키마
기준으로 서술하고, 카탈로그는 introspection 기반 단일 소스로 위임한다.

Returns:
    서버 메타 정보 + 카탈로그 포인터
[출력 스키마] name(str), description(str), version(str), data_backend{...},
catalog{discovery,tool_chains}, public_endpoints{...}. MIME type: text/plain
- market://meta/pg-pool - [역할] MCP 프로세스의 psycopg ConnectionPool 통계 (스키마별 in-use/avail/waiter).
[호출 시점] 풀 고갈 의심 시. waiter>0 가 연속되면 pool 크기 or 쿼리 지연 문제.
[출력 스키마] pools{schema→{pool_size,available,requests_queued,requests_waiting,...}}. MIME type: text/plain
- market://meta/tool-chains - [역할] 도구/리소스 의존관계, 추천 호출 체인, 사용 가이드를 반환합니다.
[호출 시점] 어떤 도구를 어떤 순서로 호출해야 하는지 안내 필요 시. 세션 시작 시 한번 호출 권장.
[선행 조건] 없음.
[후속 추천] usage_hint.start_here에 명시된 Resource부터 시작.
[주의] 메타 정보이므로 실시간 데이터가 아닙니다.
[출력 스키마] version(str), tool_chains{chain_id→{name,description,steps[{order,call,type,purpose}]}}, dependency_graph{uri→{requires,recommended,next,note}}, usage_hint{start_here,unified_vs_individual,signal_depth}. MIME type: text/plain
- market://meta/discovery - [역할] 전체 Resource(static+template)와 Tool 카탈로그 + OneQAZ positioning 을 한 번에 반환.
       카탈로그는 등록된 tool/resource 의 introspection 결과로 동적 생성되어
       시스템이 진화해도 항상 최신. data_freshness 는 PG 실측값.
[호출 시점] 세션 시작 시 한 번. Tool/Resource 탐색 출발점이며
             거대 AI 가 OneQAZ 사용·추천 판단의 base reasoning 으로 쓴다.
[선행 조건] 없음.
[후속 추천] specialist_domains 와 manifest.tools_by_category 보고 목적에 맞는 tool 선택,
             또는 market://meta/tool-chains 에서 추천 chain 선택.
[출력 스키마] version, generated_at, positioning{specialist_domains,trust_principles,
             what_we_do_NOT_provide,philosophy}, catalog{tools_by_category,static_resources,
             template_resources,counts}, data_freshness{label→{status,lag_seconds,last_ts}},
             market_ids[], common_categories[], notes. MIME type: text/plain
- market://global/summary - [역할] 원자재/국채/외환 등 전체 매크로 레짐 요약.
[호출 시점] 시장 전체 방향성 파악 시 첫 번째로 호출.
[선행 조건] 없음 (최상위 Resource).
[후속 추천] market://global/category/{category}, market://{market_id}/unified.
[주의] 캐시 TTL=300초.
[출력 스키마] ai_summary 래핑. full_data: overall{regime,score}, categories{id→{regime_dominant,sentiment_avg,symbols}}, mtf_summary{aligned_symbols,misaligned_symbols}, _llm_summary(str). MIME type: text/plain
- market://global/categories - [역할] 사용 가능한 매크로 카테고리 목록과 DB 존재 여부.
[호출 시점] 카테고리 확인 시.
[선행 조건] 없음.
[후속 추천] market://global/category/{category}.
[주의] DB 존재 여부만 확인.
[출력 스키마] categories[{id(str),db_path(str),exists(bool)}]. MIME type: text/plain
- market://all/summary - [역할] crypto+kr_stock+us_stock 3개 시장 상태를 한번에 반환.
[호출 시점] 전체 포트폴리오 개요 시 가장 먼저 호출.
[선행 조건] 없음.
[후속 추천] market://{market_id}/status, market://unified/cross-market.
[주의] TTL=60초. 3개 시장 순차 조회.
[출력 스키마] timestamp(str), markets{market_id→{market_regime,positions_summary{total,profitable,avg_pnl},performance{win_rate,total_profit_pct},recent_24h{trades,wins,avg_pnl}}}, _llm_summary(str). MIME type: text/plain
- market://structure/all - [역할] 모든 시장의 ETF/바스켓 구조 분석 요약. [호출 시점] 시장 구조 전체 파악 시. [선행 조건] 없음. [후속 추천] market://{market_id}/structure, market://global/summary. [주의] 구조 요약 JSON 없는 시장은 건너뜀. [출력 스키마] market_id→{overall{regime,score}, groups{group_id→{regime_dominant,regime_avg,confidence,timing_signal}}}. MIME type: text/plain
- market://indicators/fear-greed - [역할] Fear & Greed Index(시장 심리, 0-100). [호출 시점] 시장 심리 빠르게 파악 시. [선행 조건] 없음. [후속 추천] market://indicators/regime, market://indicators/context. [주의] Alternative.me API. TTL=300초. API 장애 시 캐시 사용.
[출력 스키마] ai_summary 래핑. full_data: value(int:0-100), classification(str:Extreme Fear|Fear|Neutral|Greed|Extreme Greed), adjustment{threshold_adj,position_mult,strategy_hint}, interpretation(str), _llm_summary(str). MIME type: text/plain
- market://indicators/regime - [역할] 4-Layer 시장 레짐(Short/Mid/Long/SuperLong 레이어별 레짐/점수/변동성). [호출 시점] 다중 시간프레임 레짐 분석 시. [선행 조건] 없음. [후속 추천] market://indicators/context, market://global/summary. [주의] MarketAnalyzer 의존.
[출력 스키마] ai_summary 래핑. full_data: score(float), regime(str), volatility(float), raw_score(float), details{sl,long,mid,short}, _llm_summary(str). MIME type: text/plain
- market://indicators/context - [역할] Fear&Greed + 4-Layer 레짐 결합 종합 시장 컨텍스트. [호출 시점] 시장 지표 한번에 확인 시. fear-greed+regime 2개 호출 대체. [선행 조건] 없음. [후속 추천] market://{market_id}/unified, market://all/summary. [주의] 한쪽 실패해도 나머지 반환.
[출력 스키마] ai_summary 래핑. full_data: fear_greed{value,classification,adjustment{...}}, regime_analysis{score,regime,volatility,details{...}}, sentiment_regime_aligned(bool), alignment_note(str), _llm_summary(str). MIME type: text/plain
- market://global/macro_events - [역할] 활성 매크로 이벤트의 라이프사이클 상태(전쟁/금융위기 등 장기 이벤트). [호출 시점] 진행 중인 글로벌 매크로 이벤트 확인 시. [선행 조건] market://global/summary 권장. [후속 추천] market://unified/cross-market. [주의] 이벤트 없으면 빈 배열. [출력 스키마] _contract 포함. total_events(int), active_events(int), events[{event_id,title,category,lifecycle_state,current_sensitivity,peak_sensitivity,affected_markets,recent_developments[],market_sensitivities{}}], _llm_summary(str). MIME type: text/plain
- market://unified/cross-market - [역할] 교차시장 상관관계(금-BTC, 국채-주식, 달러-신흥시장 등 패턴 감지). [호출 시점] 시장간 상관관계/디커플링 분석 시. [선행 조건] market://global/summary 권장. [후속 추천] market://derived/cross-decoupling, market://all/summary. [주의] TTL=120초. 사전 정의된 패턴 기반.
[출력 스키마] _contract 포함. directions{symbol→{direction,regime,category}}, correlations[{pattern_name,interpretation,score,active}], active_count(int), dominant_interpretation(str|null), global_regime{overall_regime,overall_score}, market_structure{market_id→{overall_regime,groups{...}}}, _llm_summary(str). MIME type: text/plain
- market://derived/event-leading - [역할] 이벤트 선행 점수(시장의 이벤트 선반영 정도). [호출 시점] 이벤트 기반 매매 전략 수립 시. [선행 조건] external/causality 권장. [후속 추천] market://derived/reaction-speed. [주의] 데이터 축적 필요. [출력 스키마] ai_summary 래핑. full_data: scores[{event_type,market_id,news_type,leading_score,avg_lead_time_minutes,avg_anticipation_ratio,accuracy_pct,sample_count}], _contract{...}, _llm_summary(str). MIME type: text/plain
- market://derived/cross-decoupling - [역할] 크로스마켓 디커플링 지수(상관 시장간 이탈 감지). [호출 시점] 교차시장 이상 징후 감지 시. [선행 조건] market://unified/cross-market 권장. [후속 추천] market://global/summary. [주의] 계산 데이터 없으면 빈 결과. [출력 스키마] ai_summary 래핑. full_data: pairs[{source_market,target_market,decoupling_index,correlation_breakdown,regime_divergence,timing_lag_divergence}], overall_decoupling(float), _contract{...}, _llm_summary(str). MIME type: text/plain
- market://derived/reaction-speed - [역할] 뉴스 반응 속도(시장의 뉴스 반응 시간/정확도). [호출 시점] 뉴스 기반 매매 타이밍 최적화 시. [선행 조건] external/summary 권장. [후속 추천] market://derived/event-leading. [주의] news_reaction_speed 기반. [출력 스키마] ai_summary 래핑. full_data: speed_bands[{news_type,market_id,speed_band,count,avg_reaction_score,avg_lag_minutes,absorption_time_minutes,direction_accuracy}], _contract{...}, _llm_summary(str). MIME type: text/plain

## Prompts
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## Metadata
- Owner: io.github.wnsod
- Version: 1.0.0
- Runtime: Pypi
- Transports: STDIO, HTTP
- License: Not captured
- Language: Not captured
- Stars: Not captured
- Updated: Apr 7, 2026
- Source: https://registry.modelcontextprotocol.io