OneQAZ Trading Intelligence

get_news_causality_breakdown

Read-onlyIdempotent

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 Do not cite as evidence of predictive capability.

Purpose: Counts of news items per internal label the pipeline assigns. ANTICIPATED = the item matched a scheduled/calendar event. SURPRISE_WITH_PRECURSOR = the item was flagged by the cascade-anomaly heuristic (macro -> ETF -> stock). SURPRISE = neither matched. These are pipeline labels, not validated classifications; the labelling rule and its lead/anticipation metrics are under audit and withheld here. Triggers: "how many news items per category this week?", "뉴스 라벨 분포 어때?", "how many calendar-matched events?". When to call: when inspecting news label coverage. This tool does NOT establish that the market did or did not see an event coming. Prerequisites: none. Next steps: market://{market_id}/external/causality for raw causality rows. Caveats: window limited to recent days.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days`	No	Lookback window in days (default 7)
`market_id`	No	Market identifier. Aliases coin/kr/us and any letter case are accepted.	crypto

Output Schema

TableJSON Schema

Name	Required	Description
`full_data`	No
`timestamp`	Yes	RFC3339 UTC, server build time
`disclaimer`	Yes	Canonical compliance disclaimer (always present)
`request_id`	Yes	32-hex per-response correlation id
`is_real_money`	No
`data_classification`	No
`is_investment_advice`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 23, 2026

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_causality_breakdown — 실응답에서 추출(2026-09-23).",
+        "properties": {
+          "breakdown": {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          "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"
+]

Changed3 schema fields changedSep 22, 2026

addedInput schema / properties / days / description

Added value: +"Lookback window in days (default 7)"

addedInput schema / properties / market_id / description

Added value: +"Market identifier. Aliases coin/kr/us and any letter case are accepted."

addedInput schema / properties / market_id / enum

Added value: +[
+  "crypto",
+  "kr_stock",
+  "us_stock"
+]

First observedApr 19, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already indicate safe read-only, open-world hints, and idempotency, so the baseline burden is lower. The description additionally warns that the methodology is under audit and the labels are not validated, which is crucial for responsible use. It also notes the window is limited to recent days. Only minor gap: no explicit statement about whether the tool can return empty results, but given annotations, this is sufficient.

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 sections (Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats). It front-loads the most critical caveat (UNVERIFIED) and remains concise with no redundant explanations. Each sentence earns its place.

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 tool has an output schema, so return values are documented elsewhere. The description fully covers purpose, usage context, limitations, and next steps. It provides all necessary context for an agent to decide whether to call it and what to do with the results. Given the complexity of the labels and the audit status, this is exceptionally complete.

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?

Schema coverage is 100%, with detailed descriptions for both parameters. The description does not need to elaborate further. It adds some context by mentioning 'recent days' which aligns with the days parameter, but that is minimal. Baseline 3 is appropriate because the schema handles parameter documentation.

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 clearly states that the tool counts news items per internal label, and it explicitly defines each of the three labels (ANTICIPATED, SURPRISE_WITH_PRECURSOR, SURPRISE). It distinguishes itself from sibling tools by clarifying that it only inspects news label coverage and does not establish market anticipation, which is a unique purpose.

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 provides explicit trigger phrases in multiple languages, a clear 'When to call' section, and a 'Next steps' pointer to a related tool (get_macro_causality_graph_tool via the path). It also states what this tool does NOT do, which effectively excludes inappropriate usage. This is exemplary 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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