OneQAZ Trading Intelligence

Macro Causality Graph

get_macro_causality_graph_tool

Read-onlyIdempotent

Purpose: Lag-aware causal graph between macro categories (bonds / vix / forex / credit / inflation / liquidity / commodities). Returns only statistically significant lead-lag pairs (e.g. forex -> vix 7d rho=-0.41). Triggers (casual questions too): "what happens to VIX when bonds move?", "금리 오르면 뭐가 움직여?", "which macro leads which?", "거시 지표끼리 인과관계 있어?", "does the dollar lead volatility?". When to call: assess pre-emptive cross-category impact after a macro event. Prerequisites: none. Next steps: get_macro_influence_map for category -> market impact. Caveats: Pearson-based; requires >= 30 samples; p < 0.05 filter.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`max_p_value`	No	Maximum p-value (default 0.05)
`min_abs_corr`	No	Minimum \|corr\| (default 0.15)

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.

Changed1 schema field changedOct 7, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```

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_causality_graph_tool — 실응답에서 추출(2026-09-23).",
+        "properties": {
+          "edges": {
+            "items": {},
+            "type": "array"
+          },
+          "significant_count": {
+            "anyOf": [
+              {
+                "type": "integer"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "total_edges": {
+            "anyOf": [
+              {
+                "type": "integer"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          }
+        },
+        "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"
+]

Changed2 schema fields changedSep 22, 2026
- addedInput schema / properties / max_p_value / description
```
Added value: +"Maximum p-value (default 0.05)"
```
- addedInput schema / properties / min_abs_corr / description
```
Added value: +"Minimum |corr| (default 0.15)"
```
AddedMay 7, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds real behavioral context beyond that: Pearson-based methodology, a >=30-sample requirement, and a p<0.05 significance filter. It stops short of explaining behavior when sample counts are insufficient or what changing the thresholds does to results.

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?

Explicitly labeled sections (Purpose / Triggers / When to call / Prerequisites / Next steps / Caveats) make it front-loaded and scannable. The bilingual trigger list is slightly long but every entry aids routing, so little is wasted.

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?

With an output schema present, return values need not be described, and the annotations carry the safety profile. Given that, the description covers purpose, invocation triggers, prerequisites, follow-up routing, and statistical caveats — nothing an agent needs to 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 both params carry inline descriptions with defaults, so baseline is 3. The description's mention of a 'p < 0.05 filter' loosely maps to max_p_value but adds no syntax, range, or interaction guidance beyond the schema.

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?

States a specific verb+resource with scope: a 'lag-aware causal graph between macro categories', enumerates the categories, and pins down the output as 'statistically significant lead-lag pairs' with a concrete example (forex -> vix 7d rho=-0.41). This is clearly distinguishable from correlation siblings like get_cross_market_correlation and get_sector_correlations_tool by the lead-lag/directional framing.

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?

Provides explicit triggers including casual phrasings in two languages, a 'When to call' condition ('assess pre-emptive cross-category impact after a macro event'), 'Prerequisites: none', and a routing pointer to get_macro_influence_map as the next step. When, why, and where-to-go-next are all covered without inference.

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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