OneQAZ Trading Intelligence

get_strategy_distribution

Read-onlyIdempotent

Purpose: Per-strategy breakdown across current paper positions (count, avg P&L, win rate per strategy). Triggers (casual questions too): "what strategies are you running?", "무슨 전략 돌리고 있어?", "which strategy holds the most positions?", "전략별 성적 어때?", "is one strategy dominating?". When to call: diversification audit, per-strategy performance check. Prerequisites: get_positions recommended for raw rows. Next steps: market://{market_id}/derived/strategy-fitness, signals/feedback. Caveats: empty distribution when no positions are open.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`market_id`	Yes	Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us 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_strategy_distribution — 실응답에서 추출(2026-09-23).",
+        "properties": {
+          "distribution": {
+            "items": {},
+            "type": "array"
+          },
+          "market_id": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "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 / market_id / description

Added value: +"Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)"

addedInput schema / properties / market_id / enum

Added value: +[
+  "crypto",
+  "kr_stock",
+  "us_stock"
+]

First observedApr 19, 2026

TDQS

A4/5.0

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 useful behavioral context by noting that the distribution is empty when no positions are open, which prevents misinterpretation of empty 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?

The description is organized into labeled sections and front-loads the purpose. The trigger examples add value for conversational AI routing, though the overall length is slightly more than strictly necessary.

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 only one parameter, rich annotations, and an output schema present, the description covers everything an agent needs: purpose, triggers, when to call, prerequisites, next steps, and caveats. No critical gap remains.

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%, with the market_id parameter fully documented via enum and alias note. The description does not add parameter-level semantics beyond referencing market_id in the next-steps URI, so the schema carries the weight.

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?

The description clearly states the tool's function: a per-strategy breakdown across current paper positions with count, average P&L, and win rate. It is specific about the resource and output focus, though it does not explicitly differentiate itself from the sibling get_strategy_leaderboard.

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 triggers, a 'when to call' section naming diversification audits and per-strategy performance checks, and a prerequisite recommending get_positions for raw rows. It does not state when not to use this tool or name an alternative tool, so it stops short of full exclusion guidance.

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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