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OneQAZ Trading Intelligence

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Macro Influence Map

get_macro_influence_map
Read-onlyIdempotent

Purpose: Expose OneQAZ's pre-defined causal hypothesis map. Each macro category (bonds, forex, vix, credit, liquidity, inflation, commodities, energy) is mapped to a target market with lag_hours + sensitivity (static prior) and an effective block with the value the engine actually uses (backtest_tuning when the cell passes the tuning gate). Triggers (casual questions too): "how do rates affect crypto?", "금리가 코인에 어떻게 영향 줘?", "what's your causal model?", "예측 논리가 뭐야?", "which macro drives which market?". When to call: when an AI wants to understand WHY we make certain predictions. Prerequisites: none. Next steps: get_backtest_tuning_state for runtime calibration of these hypotheses. Caveats: top-level values are static priors — cite each cell's effective block for what the engine uses (meta.effective_status reports lookup failures).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
market_idNoOptional target market filter (coin_market, kr_market, us_market). Aliases coin/kr/us and any letter case are accepted.

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_macro_influence_map — 실응답에서 추출(2026-09-23).",
+        "properties": {
+          "influence_map": {
+            "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. Changed4 schema fields changed
    • addedInput schema / properties / market_id / anyOf
      Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
    • addedInput schema / properties / market_id / description
      Added value: +"Optional target market filter (coin_market, kr_market, us_market). Aliases coin/kr/us and any letter case are accepted."
    • addedInput schema / properties / market_id / enum
      Added value: +[
+  "crypto",
+  "kr_stock",
+  "us_stock"
+]
    • removedInput schema / properties / market_id / type
      Removed value: -"string"
  4. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations only cover read-only/idempotent/open-world safety, so the description carries real behavioral load here and does: it warns that top-level values are static priors while the `effective` block reflects backtest_tuning when the tuning gate passes, and notes meta.effective_status reports lookup failures. That is genuine engine-behavior disclosure beyond the 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?

Front-loaded with 'Purpose:' then organized into Triggers / When to call / Prerequisites / Next steps / Caveats, so an agent can skim to the relevant block. Mildly verbose with the bilingual trigger list, but each section carries distinct information.

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 annotations cover the safety profile; the description still supplies the interpretive key (priors vs effective block) that an agent needs to cite results correctly. Coverage of the sibling differentiation would make it 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 description coverage is 100% and the single market_id parameter is fully documented in the schema (including alias and case handling), so the baseline is 3. The description adds nothing about the optional market filter.

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 resource and its structure: 'pre-defined causal hypothesis map' with macro categories mapped to target markets via lag_hours + sensitivity, plus an `effective` block. Clear enough that an agent knows what it will get. However, it never distinguishes itself from the sibling get_macro_causality_graph_tool, which sounds like an adjacent causal-map resource.

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?

Gives explicit triggers including multilingual casual phrasings ('how do rates affect crypto?', '금리가 코인에 어떻게 영향 줘?') and a 'When to call' clause (understanding WHY predictions are made), plus a next-step pointer to get_backtest_tuning_state. No exclusion guidance versus the causality-graph sibling, which is the one gap.

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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