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OneQAZ Trading Intelligence

get_signals

Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinNo
limitNo
symbolNo
intervalNo
market_idYes
min_scoreNo
hours_backNo
action_filterNo
min_confidenceNo

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

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavior beyond that: the caveat about market-wide scanning when symbol is omitted (including per-symbol row caps and scan caps), and the explicit disclaimer that outputs are research signals, not orders or advice. 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.

Conciseness5/5

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

The description is well-structured with clear headings (Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats, Args, Disclaimer), ensuring information is scannable. Every section earns its place; while detailed, it remains efficient and front-loaded with the most important information.

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?

Given the tool's complexity (9 params, 1 required, output schema present), the description covers all necessary aspects: purpose, trigger examples, selection context, prerequisites, follow-up tools, parameter semantics, behavioral caveats, and a safety disclaimer. It is complete for an agent to decide when and how to invoke it.

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%, so the description fully compensates by enumerating all 9 parameters with meaningful details (e.g., 'coin: Legacy alias of symbol', interval allowed values, default limit, hours_back window). This adds semantics well beyond the bare schema and clarifies optionality and defaults.

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 opens with a specific verb ('Query') and resource ('research signals') with dynamic filters, clearly stating what the tool does. It distinguishes itself from siblings by mentioning next steps like get_signal_detail and get_role_analysis, and contrasts with get_latest_decisions.

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 ('should I buy / sell X?', casual phrasing), when-to-call scenarios (drilling into a specific slice, symbol-by-symbol scanning), and prerequisites (market:// summary). It also lists appropriate siblings to pair with or follow up, offering clear guidance on when to use this tool versus alternatives.

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.