OneQAZ Trading Intelligence

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. Highest-transparency tool — the causal reasoning is visible and measurable. 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: static hypothesis only; see tuning state for current adjustments.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`market_id`	No	Optional target market filter (coin_market, kr_market, us_market). Aliases coin/kr/us and any letter case are accepted.

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_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"
+]

Changed4 schema fields changedSep 22, 2026

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"
```

First observedApr 19, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnly/openWorld/idempotent hints, and the description adds valuable context beyond them: the map is 'pre-defined' and 'static hypothesis only', and runtime calibration lives in get_backtest_tuning_state. This clarifies that the tool does not reflect live adjustments, which is important behavioral information not present in 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 labeled sections (Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats) and is front-loaded with the core purpose. The example trigger phrases add useful context rather than padding, and every 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 a simple optional parameter, rich annotations, and an output schema, so the description does not need to explain return values. It covers the purpose, triggers, invocation context, prerequisites, next steps, and the key caveat that the map is static. Nothing an agent needs to select or call this correctly 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 description coverage is 100%, and the single optional market_id parameter is fully described in the schema, including aliases. The tool description itself does not mention or elaborate on the parameter, so it adds no meaning beyond what the schema already provides; baseline 3 applies.

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 explicitly states the tool's purpose: expose OneQAZ's pre-defined causal hypothesis map, with a specific resource ('Each macro category ... mapped to a target market with lag_hours + sensitivity'). It clearly conveys a unique, inspectable causal model rather than a generic analysis tool, making it distinguishable from siblings like get_cross_market_correlation or get_backtest_tuning_state.

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 guidance: 'when an AI wants to understand WHY we make certain predictions', plus concrete trigger phrases and prerequisites. It does not explicitly state when not to use it or name alternative tools for the same kind of question, though the caveat referencing get_backtest_tuning_state implies where to go for runtime adjustments.

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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