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OneQAZ Trading Intelligence

get_news_leading_indicator_performance

Read-onlyIdempotent

UNVERIFIED — methodology under audit. Do not cite as evidence of predictive capability.

Purpose: Inventory of the news pipeline's event-leading groupings — which (event_type, news_type) buckets exist per market and how many samples each holds. The lead/score metrics themselves are withheld from this response while the calculation method is being audited. Triggers: "what news event groupings does OneQAZ track?", "뉴스 이벤트 분류 어떤 게 있어?", "how many news samples per event type?". When to call: when inspecting news pipeline coverage. This tool does NOT answer questions about predicting or anticipating news — it carries no such evidence. Prerequisites: none. Next steps: get_news_causality_breakdown for the label counts. Caveats: empty when no news events processed in the recent window. Sample counts are coverage figures only; they do not imply statistical validity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
market_idNoMarket identifier (crypto, kr_stock, us_stock, etc.). Aliases coin/kr/us and any letter case are accepted.crypto
target_marketNoAlias for market_id (backward compat)
min_sample_countNoMinimum rows-per-grouping cutoff (default 3). A coverage filter only — it confers no statistical validity.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
full_dataNo
timestampYesRFC3339 UTC, server build time
disclaimerYesCanonical compliance disclaimer (always present)
request_idYes32-hex per-response correlation id
is_real_moneyNo
data_classificationNo
is_investment_adviceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedOutput schema / properties
      Added value: +{
      +  "data_classification": {
      +    "anyOf": [
      +      {
      +        "const": "research_information_only",
      +        "type": "string"
      +      },
      +      {
      +        "type": "null"
      +      }
      +    ],
      +    "default": null
      +  },
      +  "disclaimer": {
      +    "description": "Canonical compliance disclaimer (always present)",
      +    "type": "string"
      +  },
      +  "full_data": {
      +    "anyOf": [
      +      {
      +        "additionalProperties": true,
      +        "description": "`full_data` for get_news_leading_indicator_performance — 실응답에서 추출(2026-09-23).",
      +        "properties": {
      +          "indicators": {
      +            "items": {},
      +            "type": "array"
      +          },
      +          "meta": {
      +            "additionalProperties": true,
      +            "type": "object"
      +          }
      +        },
      +        "type": "object"
      +      },
      +      {
      +        "type": "null"
      +      }
      +    ],
      +    "default": null
      +  },
      +  "is_investment_advice": {
      +    "anyOf": [
      +      {
      +        "const": false,
      +        "type": "boolean"
      +      },
      +      {
      +        "type": "null"
      +      }
      +    ],
      +    "default": null
      +  },
      +  "is_real_money": {
      +    "anyOf": [
      +      {
      +        "const": false,
      +        "type": "boolean"
      +      },
      +      {
      +        "type": "null"
      +      }
      +    ],
      +    "default": null
      +  },
      +  "request_id": {
      +    "description": "32-hex per-response correlation id",
      +    "type": "string"
      +  },
      +  "timestamp": {
      +    "description": "RFC3339 UTC, server build time",
      +    "type": "string"
      +  }
      +}
    • addedOutput schema / required
      Added value: +[
      +  "disclaimer",
      +  "request_id",
      +  "timestamp"
      +]
  2. Changed7 schema fields changed
    • addedInput schema / properties / market_id / description
      Added value: +"Market identifier (crypto, kr_stock, us_stock, etc.). Aliases coin/kr/us and any letter case are accepted."
    • addedInput schema / properties / market_id / enum
      Added value: +[
      +  "crypto",
      +  "kr_stock",
      +  "us_stock"
      +]
    • addedInput schema / properties / min_sample_count / description
      Added value: +"Minimum rows-per-grouping cutoff (default 3). A coverage filter only — it confers no statistical validity."
    • addedInput schema / properties / target_market / anyOf
      Added value: +[
      +  {
      +    "type": "string"
      +  },
      +  {
      +    "type": "null"
      +  }
      +]
    • addedInput schema / properties / target_market / description
      Added value: +"Alias for market_id (backward compat)"
    • addedInput schema / properties / target_market / enum
      Added value: +[
      +  "crypto",
      +  "kr_stock",
      +  "us_stock"
      +]
    • removedInput schema / properties / target_market / type
      Removed value: -"string"
  3. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark this as read-only, idempotent, and open-world, but the description adds important non-obvious behavior: the methodology is unverified, lead/score metrics are withheld during audit, results may be empty when no news events were processed, and sample counts are coverage-only. These caveats go well beyond the structured fields.

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 organized into labeled sections — Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats — with the critical warning front-loaded. Each section earns its place, and there is no filler or duplication beyond necessary emphasis.

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?

Even with a rich output schema and annotations, the description covers prerequisites, empty-result behavior, exclusions, and a named follow-up tool. An agent can determine when to call this tool, what to expect from the response, and where to go next without additional inference.

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

Parameters3/5

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

The input schema already documents all three parameters with descriptions and enums, so schema coverage is 100% and the baseline applies. The description reinforces that sample counts are coverage figures and invokes 'per market,' but it adds no parameter-specific information beyond what the schema already provides.

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 and resource: it 'inventories' the news pipeline's event-leading groupings, showing 'which (event_type, news_type) buckets exist per market and how many samples each holds.' It also explicitly separates itself from predictive claims, which prevents confusion with the tool's 'leading indicator performance' name.

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?

The description gives explicit 'Triggers' and a 'When to call' section: 'when inspecting news pipeline coverage.' It also states what the tool does NOT answer — questions about predicting or anticipating news — and points to get_news_causality_breakdown as the next step, giving an agent clear routing guidance.

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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