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News Causality Breakdown

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 Do not cite as evidence of predictive capability.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
NameRequiredDescriptionDefault
daysNoLookback window in days (default 7)
market_idNoMarket identifier. Aliases coin/kr/us and any letter case are accepted.crypto

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. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  2. 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_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"
+]
  3. Changed3 schema fields changed
    • 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"
+]
  4. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover read-only/idempotent/open-world safety, so the bar is lower; the description nonetheless adds substantive behavioral context: the labels are unvalidated pipeline heuristics, the methodology is under audit, and the window is limited to recent days. That is exactly the kind of caveat an agent cannot get from 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.

Conciseness4/5

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

Front-loads the unverified disclaimer, then organizes content into Purpose/Triggers/When/Prerequisites/Next steps/Caveats. Sections earn their place, though the bilingual trigger list and repeated non-predictive warnings add slight redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return values need no explanation, and the description covers scope, caveats, and the follow-up resource. Complete enough for an agent to call it correctly; only a sibling-level routing statement is missing.

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% and both parameters are documented in the schema, including the enum and default. The description adds no parameter-level detail beyond that, so the baseline 3 applies.

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?

States a specific verb+resource ('counts of news items per internal label the pipeline assigns') and defines the three labels, which is meaningfully more than a restatement of the title. It does not name a sibling tool to contrast against, relying instead on a resource URI for the follow-up.

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 'When to call', sample triggers, 'Prerequisites: none', and a next step, plus an exclusion ('does NOT establish that the market did or did not see an event coming'). Lacks a direct contrast with a sibling tool, which would be needed for a 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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