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OneQAZ Trading Intelligence

get_winning_trades

Read-onlyIdempotent

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 Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
market_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoSet true on error responses
actionNoRecommended client action (error path)
reasonNoHuman-readable cause (error path)
full_dataNo
retryableNoWhether the client should retry (error path)
timestampYesRFC3339 UTC, server build time
ai_summaryNoOne-line AI-oriented summary (success path)
disclaimerYesCanonical compliance disclaimer (always present)
error_codeNoStable error identifier; see mcp_error_policy.md
request_idYes32-hex per-response correlation id
_llm_summaryNo
action_valueNo
_next_actionsNo
fallback_noteNo
fallback_toolNoSuggested fallback (error path)
is_real_moneyNo
_value_signalsNo
summary_for_userNoOne-line jargon-free Korean summary (success path)
data_classificationNo
is_investment_adviceNo
ai_summary_ttl_secondsNo
_market_state_narrativeNo
ai_summary_generated_atNoRFC3339 UTC
_followup_questions_for_userNo

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With annotations already declaring readOnlyHint, openWorldHint, and idempotentHint, the description adds context beyond them: the wrapper behavior (min_pnl=0.01), the caveat 'paper-trading data only', and a disclaimer. It does not contradict annotations and provides useful additional behavioral context, though it omits details like pagination which are likely covered by the output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear labels (Purpose, Triggers, When to call, etc.) and front-loaded with the core purpose. Each section earns its place, though the extensive trigger examples and disclaimer could be seen as slightly verbose. Overall, efficient and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, usage scenarios, prerequisites (none), next steps, caveats, parameter semantics, and limitations. Combined with the annotations and output schema, it is fully complete for this simple 2-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description compensates fully: it explains market_id values ('crypto, kr_stock, us_stock; aliases coin/kr/us accepted') and limit's meaning ('Max results (default 10)'). This adds significant meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with a specific verb and resource: 'Winning paper trades only (P&L > 0)' and further clarifies it is a wrapper around get_trade_history(min_pnl=0.01). This clearly distinguishes it from siblings like get_losing_trades and get_trade_history.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit triggers ('what worked?', 'show me the winners'), when to call ('success-pattern review'), and next steps ('analyze_trades for breakdowns'). It also identifies it as a wrapper around get_trade_history, implying when to use the alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there are overlapping areas such as get_feature_governance_state vs get_feature_governance_status_tool and the convenience wrappers for losing/winning positions/trades. Descriptions clarify relationships well, so confusion is limited.

Naming Consistency4/5

The vast majority follow a consistent 'get_' prefix with descriptive nouns, but inconsistencies exist: some tools have '_tool' suffix (e.g., get_macro_causality_graph_tool) while similar ones do not, and governance tools use 'state' vs 'status_tool'. Overall, the pattern is predictable.

Tool Count2/5

39 tools is excessive for the apparent scope. Many are redundant convenience wrappers (get_losing_positions, get_winning_trades, etc.) that duplicate filters on other tools, and there are near-duplicate governance tools. The count could be significantly consolidated.

Completeness5/5

The tool surface is remarkably comprehensive, covering signals, trades, predictions, positions, macro relationships, news causality, strategies, structure, governance, and ledger integrity. There are no obvious functional gaps, and the tools form a well-integrated evidence chain.