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OneQAZ Trading Intelligence

get_position_detail

Read-onlyIdempotent

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 Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinNo
symbolNo
market_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is known. The description adds valuable behavioral context beyond this: it returns an 'error envelope when no position exists,' and clarifies this is a 'paper position' tool. These details are not inferable from the annotations and help the agent anticipate failure modes. There is no contradiction with annotations.

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 labeled sections (Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats, Args, Disclaimer). It front-loads the purpose and every section adds useful information. There is minor redundancy between 'Purpose' and 'When to call' (both mention per-symbol/full context), but the overall length is reasonable for the amount of guidance provided.

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?

For a tool with 3 params (1 required) and an output schema, the description is thoroughly complete. It covers what the tool does, when to use it with example triggers, prerequisites, next steps, a caveat (error envelope for no position), and parameter semantics. Since an output schema exists, the description need not detail return values. This gives the agent all necessary context to select and invoke the tool effectively.

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

Parameters4/5

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

Schema coverage is 0% (no property descriptions), so the description's Args section is essential. It explains market_id with accepted values and aliases ('crypto, kr_stock, us_stock; aliases coin/kr/us accepted'), symbol with examples, and coin as a legacy alias. This adds meaning beyond the raw schema, though it could be slightly more explicit about the relationship between symbol and coin (e.g., which takes precedence). Overall, it compensates well for the schema gap.

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 states a specific verb+resource+scope: 'Per-symbol paper position deep-dive (position + recent trades + decisions).' This clearly distinguishes it from sibling tools like get_positions (which lists positions) and analyze_trades (which analyzes trades broadly). The 'full context for one ticker' phrasing further clarifies its focused purpose.

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?

The description provides explicit triggers ('how's the BTC position doing?') and a 'When to call' section ('full context for one ticker'). It also gives a prerequisite (confirm position via get_positions) and next steps (get_signal_detail, get_role_analysis). However, it does not explicitly state when NOT to use this tool or directly name alternatives as exclusions, though the prerequisite strongly implies using get_positions for listing positions. This is clear guidance but lacks an explicit when-not clause.

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.