# OneQAZ Trading Intelligence (remote · api.oneqaz.com)

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

- Trust score: 55/100 (low)
- Change this week: +4
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `api.oneqaz.com`: 55/100 (this document), [markdown](https://verifymcp.io/servers/wnsod-oneqaz-trading-mcp/api.md), [page](https://verifymcp.io/servers/wnsod-oneqaz-trading-mcp/api)
- pypi · `oneqaz-trading-mcp`: 13/100, [markdown](https://verifymcp.io/servers/wnsod-oneqaz-trading-mcp/oneqaz-trading-mcp.md), [page](https://verifymcp.io/servers/wnsod-oneqaz-trading-mcp/oneqaz-trading-mcp)

## Channel facts

- Endpoint: `https://api.oneqaz.com/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.0.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Endpoint Security**: 46/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation not fully verified: no authorisation is required to call this server, and 39 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe.
  - HTTPS not yet verified: we couldn't determine whether a plaintext access path exists.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 53/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (poor).
  - Context-footprint check failed: tool/resource definitions use about 13357 tokens (~238/item across 56 items; 39 tools + 17 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 71/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 0% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

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

### Codex

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

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "wnsod-oneqaz-trading-mcp": {
      "type": "remote",
      "url": "https://api.oneqaz.com/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add wnsod-oneqaz-trading-mcp --url https://api.oneqaz.com/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  wnsod-oneqaz-trading-mcp:
    url: "https://api.oneqaz.com/mcp"
```

### Other

```json
{
  "mcpServers": {
    "wnsod-oneqaz-trading-mcp": {
      "type": "http",
      "url": "https://api.oneqaz.com/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-02 (score 55, −4)

- [security regression] HTTPS: pass → unverified

### 2026-08-01 (score 59, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-31 (score 58, +5)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 53, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-28 (score 52, +1)

No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-27 (score 51, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-26 (score 50)

First indexed and scored.

## MCP tools (39)

### `get_trade_history` (~324 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 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.

Input parameters:

- `action_filter` (string)
- `hours_back`
- `limit` (integer)
- `market_id` (string, required)
- `max_pnl`
- `min_pnl`
- `symbol`

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `analyze_trades` (~204 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 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.

Input parameters:

- `days` (integer)
- `market_id` (string, required)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_winning_trades` (~172 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 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.

Input parameters:

- `limit` (integer)
- `market_id` (string, required)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_losing_trades` (~172 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: 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.

Input parameters:

- `limit` (integer)
- `market_id` (string, required)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_positions` (~320 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-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)

Input parameters:

- `limit` (integer)
- `market_id` (string, required)
- `max_roi`
- `min_roi`
- `sort_by` (string)
- `sort_order` (string)
- `strategy`

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_position_detail` (~211 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.
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.

Input parameters:

- `coin`
- `market_id` (string, required)
- `symbol`

### `get_profitable_positions` (~177 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 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.

Input parameters:

- `limit` (integer)
- `market_id` (string, required)

### `get_losing_positions` (~173 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.
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.

Input parameters:

- `limit` (integer)
- `market_id` (string, required)

### `get_strategy_distribution` (~174 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: 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.

Input parameters:

- `market_id` (string, required)

### `get_latest_decisions` (~274 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?", "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.

Input parameters:

- `decision_filter` (string)
- `hours_back` (integer)
- `limit` (integer)
- `market_id` (string, required)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_llm_trading_decisions` (~221 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 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.

Input parameters:

- `market_id` (string, required)
- `symbol` (string)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_signals` (~422 tokens)

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, every per-symbol DB is scanned (slower, 2 rows per DB).

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.

Input parameters:

- `action_filter` (string)
- `coin`
- `hours_back` (integer)
- `interval` (string)
- `limit` (integer)
- `market_id` (string, required)
- `min_confidence` (number)
- `min_score` (number)
- `symbol`

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_signal_detail` (~224 tokens)

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)

Input parameters:

- `coin`
- `interval` (string)
- `market_id` (string, required)
- `symbol`

### `get_role_analysis` (~229 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: 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)

Input parameters:

- `coin`
- `market_id` (string, required)
- `symbol`

### `get_prediction_accuracy` (~409 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_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.

Input parameters:

- `category` (string)
- `target_market` (string)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_backtest_tuning_state` (~200 tokens)

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.

Input parameters:

- `category` (string)
- `target_market` (string)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_monthly_accuracy_trend` (~185 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 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.

Input parameters:

- `category` (string)
- `target_market` (string)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_news_leading_indicator_performance` (~262 tokens)

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.

Input parameters:

- `market_id` (string)
- `min_sample_count` (integer)
- `target_market` (string)

### `get_news_causality_breakdown` (~216 tokens)

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.

Input parameters:

- `days` (integer)
- `market_id` (string)

### `get_feature_governance_state` (~252 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 (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.

Input parameters:

- `market_id` (string)
- `status_filter` (string)
- `target_market` (string)

### `get_structure_calibration` (~242 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. 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.

Input parameters:

- `group_name` (string)
- `market_id` (string)

### `get_structure_validation_history` (~199 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 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.

Input parameters:

- `days` (integer)
- `market_id` (string)

### `get_strategy_leaderboard` (~323 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 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.

Input parameters:

- `include_per_symbol` (boolean)
- `limit` (integer)
- `market_id` (string)
- `min_trades` (integer)
- `target_market` (string)
- `top_n` (integer)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_active_predictions` (~209 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 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.

Input parameters:

- `limit` (integer)
- `target_market` (string)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_macro_influence_map` (~215 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. 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.

Input parameters:

- `market_id` (string)

### `explain_decision` (~285 tokens)

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.

Input parameters:

- `market_id` (string, required)
- `symbol` (string, required)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_cross_market_correlation` (~203 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 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.

Input parameters:

- `source_market` (string)
- `target_market` (string)

### `get_sector_correlations_tool` (~207 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 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.

Input parameters:

- `market_id` (string)
- `top_k` (integer)

### `get_macro_causality_graph_tool` (~241 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?", "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.

Input parameters:

- `max_p_value` (number)
- `min_abs_corr` (number)

### `get_symbol_peer_links_tool` (~227 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 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.

Input parameters:

- `market_id` (string)
- `symbol`
- `top_k` (integer)

### `get_feature_governance_status_tool` (~157 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?",
    "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.

### `get_daily_brief` (~315 tokens)

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.

Input parameters:

- `market` (string)

Output parameters:

- `_followup_questions_for_user`
- `_llm_summary`
- `_market_state_narrative`
- `_next_actions`
- `_value_signals`
- `action`: Recommended client action (error path)
- `action_value`
- `ai_summary`: One-line AI-oriented summary (success path)
- `ai_summary_generated_at`: RFC3339 UTC
- `ai_summary_ttl_seconds`
- `data_classification`
- `disclaimer` (string): Canonical compliance disclaimer (always present)
- `error`: Set true on error responses
- `error_code`: Stable error identifier; see mcp_error_policy.md
- `fallback_note`
- `fallback_tool`: Suggested fallback (error path)
- `full_data`
- `is_investment_advice`
- `is_real_money`
- `reason`: Human-readable cause (error path)
- `request_id` (string): 32-hex per-response correlation id
- `retryable`: Whether the client should retry (error path)
- `summary_for_user`: One-line jargon-free Korean summary (success path)
- `timestamp` (string): RFC3339 UTC, server build time

### `get_resolved_predictions` (~419 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 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.

Input parameters:

- `cursor` (integer)
- `day`
- `limit` (integer)
- `source_category`
- `status` (string)
- `target_market`

### `get_ledger_integrity` (~312 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 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.

Input parameters:

- `days` (integer)

### `get_trade_outcomes_bulk` (~391 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 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.

Input parameters:

- `cursor` (integer)
- `days` (integer)
- `limit` (integer)
- `market` (string)

### `search` (~269 tokens)

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.

Input parameters:

- `query` (string, required)

### `fetch` (~335 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 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.

Input parameters:

- `id` (string, required)

### `get_signal_calibration` (~308 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?",
    "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.

Input parameters:

- `interval` (string)
- `market_id` (string)
- `variant` (string)

### `get_performance_metrics` (~407 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?",
    "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.

Input parameters:

- `account_type` (string, required)
- `include_daily_curve` (boolean)
- `market` (string, required)
- `window_end`
- `window_start`

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/wnsod-oneqaz-trading-mcp/api#diagnostics

## Score history

- 2026-08-03: 55
- 2026-08-02: 55
- 2026-08-01: 59
- 2026-07-31: 58
- 2026-07-30: 53
- 2026-07-29: 52
- 2026-07-28: 52
- 2026-07-27: 51
- 2026-07-26: 50

## Links

- Remote endpoint: https://api.oneqaz.com/mcp
- Repository: https://github.com/oneqaz-trading/oneqaz-trading-mcp
- Changelog RSS feed: https://verifymcp.io/servers/wnsod-oneqaz-trading-mcp/api/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/wnsod-oneqaz-trading-mcp/api/changelog.json
- HTML version of this page: https://verifymcp.io/servers/wnsod-oneqaz-trading-mcp/api