OneQAZ Trading Intelligence

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.

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

A4.5/5.0

Behavior5/5

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

Annotations already signal read-only, idempotent, and open-world behavior. The description goes further by disclosing the Pearson-based method, the 30-sample minimum, the p<0.05 significance filter, and the inclusion of lag direction and rho values. This is meaningful behavioral context 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.

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, and Caveats. It front-loads the core behavior and keeps each section informative without padding, despite including several trigger examples.

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?

For a read-only tool with two optional parameters, the description covers purpose, invocation context, prerequisites, limitations, and a natural follow-up tool. The presence of an output schema means return-value details are not needed in the description, so nothing essential 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 max_p_value and min_abs_corr have clear descriptions with defaults. The tool description does not add much parameter-specific meaning beyond the caveat 'p < 0.05 filter', which the schema already captures, so the baseline 3 is appropriate.

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 states a specific verb and resource: a lag-aware causal graph between named macro categories, returning only statistically significant lead-lag pairs with an example. This clearly differentiates it from adjacent tools like get_cross_market_correlation and get_macro_influence_map.

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?

It gives explicit trigger queries, a when-to-call condition ('assess pre-emptive cross-category impact after a macro event'), prerequisites, and a next-step pointer to get_macro_influence_map. It lacks an explicit 'don't use when' exclusion, but the context is clear enough for an agent to route correctly.

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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