OneQAZ Trading Intelligence

Feature Governance Status

get_feature_governance_status_tool

Read-onlyIdempotent

Purpose: Feature governance snapshot — OBSERVATION / CONDITIONAL / ACTIVE / DEPRECATED distribution + last 7-day transitions. Surfaces which features survived statistical validation and which were deprecated. Triggers (casual questions too): "which features are actually used?", "어떤 피처가 살아있어?", "any features promoted recently?", "피처 검증 현황 어때?", "did anything get deprecated?". When to call: trust evaluation, "which features are live right now?". Prerequisites: none. Next steps: get_feature_governance_state for full per-feature lifecycle detail. Caveats: promoter cycle runs hourly.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

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_feature_governance_status_tool — 실응답에서 추출(2026-09-23).",
+        "properties": {
+          "by_status": {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          "recent_transitions": {
+            "items": {},
+            "type": "array"
+          },
+          "total": {
+            "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"
+]

AddedMay 7, 2026

TDQS

A4.4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint, so the safety and purity profile is covered. The description adds genuine value with the caveat that the promoter cycle runs hourly, communicating data staleness. It does not describe pagination or exactly how transitions are computed, so it goes modestly beyond annotations rather than far.

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?

Well front-loaded with labeled sections (Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats), so an agent can scan it quickly. The dual-language trigger examples are useful for fuzzy matching but slightly inflate the length; every line still maps to a routing or behavioral decision.

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?

An output schema exists, so return values need no explanation, yet the description still names the status categories and the transition window. Combined with the explicit prerequisites, next-step routing, and freshness caveat, an agent has everything needed to call and interpret this zero-arg tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline is 4 per the rubric. There are no parameter semantics to clarify and the description makes no misleading claims about inputs.

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 (feature governance snapshot) and enumerates the exact output taxonomy (OBSERVATION/CONDITIONAL/ACTIVE/DEPRECATED) plus the 7-day transition window. It also distinguishes itself from the sibling get_feature_governance_state, which is named as offering full per-feature lifecycle detail.

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/natural-language phrasings in two languages, a 'when to call' condition (trust evaluation, 'which features are live right now?'), a 'Prerequisites: none' note, and a 'Next steps' routing to the complementary state tool. Nothing about selection is left to 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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