OneQAZ Trading Intelligence

get_strategy_leaderboard

Read-onlyIdempotent

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 Schema

TableJSON Schema

Name	Required	Default
`limit`	No
`top_n`	No
`market_id`	No	crypto
`min_trades`	No
`target_market`	No
`include_per_symbol`	No

Output Schema

TableJSON Schema

Name	Required	Description
`error`	No	Set true on error responses
`action`	No	Recommended client action (error path)
`reason`	No	Human-readable cause (error path)
`full_data`	No
`retryable`	No	Whether the client should retry (error path)
`timestamp`	Yes	RFC3339 UTC, server build time
`ai_summary`	No	One-line AI-oriented summary (success path)
`disclaimer`	Yes	Canonical compliance disclaimer (always present)
`error_code`	No	Stable error identifier; see mcp_error_policy.md
`request_id`	Yes	32-hex per-response correlation id
`_llm_summary`	No
`action_value`	No
`_next_actions`	No
`fallback_note`	No
`fallback_tool`	No	Suggested fallback (error path)
`is_real_money`	No
`_value_signals`	No
`summary_for_user`	No	One-line jargon-free Korean summary (success path)
`data_classification`	No
`is_investment_advice`	No
`ai_summary_ttl_seconds`	No
`_market_state_narrative`	No
`ai_summary_generated_at`	No	RFC3339 UTC
`_followup_questions_for_user`	No

TDQS

A4.5/5.0

Behavior5/5

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

Discloses important behavioral caveats beyond readOnly/openWorld/idempotent annotations: GLOBAL pool may include 'synthesized win_rate values', per_symbol_leaderboard is the 'primary measured-edge surface', min_trades enforces statistical validity, and strategies are paper-tested. This significantly helps agent trust decisions.

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?

Well-structured with Purpose, Triggers, When to call, Next steps, Caveats, Args, and Disclaimer; front-loaded with purpose. Slightly verbose due to trigger examples and disclaimer, but each section serves a purpose.

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?

Complete for a read-only query tool: covers purpose, invocation context, prerequisites, caveats, next steps, and all parameters; output schema exists, so return details need not be in description.

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 has 0% description coverage, but the Args section explains all six parameters and their aliases (market_id, target_market, top_n, limit, min_trades, include_per_symbol) plus defaults. This fully compensates for the schema gap.

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

Purpose4/5

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

Description clearly states tool returns 'Top RL-learned research strategies — GLOBAL pool + per-symbol partition' and includes trigger examples. However, it lacks an explicit verb and does not name sibling alternatives, so it does not fully meet the 5-level sibling differentiation.

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

Usage Guidelines4/5

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

Provides explicit triggers ('what are the best strategies?', 'top strategies?') and specifies 'When to call: final trust-validation step' with prerequisites none. It does not mention when not to use or alternative tools, so just misses 5.

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.

get_strategy_leaderboard

Input Schema

Output Schema

TDQS

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TDQS

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