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OneQAZ Trading Intelligence

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Performance Metrics

get_performance_metrics
Read-onlyIdempotent

Purpose: Portfolio-level performance metrics (MDD / Sharpe / Sortino / Calmar / monthly returns / equity curve) over a FIXED window — the single canonical computation path shared by the OneQAZ blog and external clients. Triggers (casual questions too): "what's the max drawdown?", "MDD 얼마야?", "샤프 비율 보여줘", "monthly returns table?", "트랙레코드 지표", "에쿼티 커브 데이터". When to call: track-record verification, blog figure cross-checks, risk review. Prerequisites: none. Next steps: get_trade_history for the underlying trades, analyze_trades for breakdowns. Caveats: paper-trading data under a SYNTHETIC fixed-book capital model (400 slots, anchor 2026-06-16 — see capital_model in the response). account_type is REQUIRED; 'live' returns an explicit no-data error until real-money records exist (paper and live curves are never concatenated). Fixed window → same inputs always reproduce the same numbers (as-of verifiable).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketYescoin | kr | us | all (aliases crypto/kr_stock/us_stock accepted). 'all' = fixed 1/3 allocation across the three books.
window_endNoISO date. Default today (KST).
account_typeYesREQUIRED. 'paper' (simulated) or 'live' (real — not yet available).
window_startNoISO date (YYYY-MM-DD). Default 2026-06-16 (public track-record anchor).
include_daily_curveNoinclude per-day equity curve rows (default false).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
full_dataNo
timestampYesRFC3339 UTC, server build time
disclaimerYesCanonical compliance disclaimer (always present)
request_idYes32-hex per-response correlation id
is_real_moneyNo
data_classificationNo
is_investment_adviceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  2. Changed2 schema fields changed
    • 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_performance_metrics — 실응답에서 추출(2026-09-23).",
+        "properties": {
+          "account_type": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "capital_model": {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          "market": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "metrics": {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          "monthly_returns": {
+            "items": {},
+            "type": "array"
+          },
+          "totals": {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          "window": {
+            "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"
+]
  3. Changed5 schema fields changed
    • addedInput schema / properties / account_type / description
      Added value: +"REQUIRED. 'paper' (simulated) or 'live' (real — not yet available)."
    • addedInput schema / properties / include_daily_curve / description
      Added value: +"include per-day equity curve rows (default false)."
    • addedInput schema / properties / market / description
      Added value: +"coin | kr | us | all (aliases crypto/kr_stock/us_stock accepted). 'all' = fixed 1/3 allocation across the three books."
    • addedInput schema / properties / window_end / description
      Added value: +"ISO date. Default today (KST)."
    • addedInput schema / properties / window_start / description
      Added value: +"ISO date (YYYY-MM-DD). Default 2026-06-16 (public track-record anchor)."
  4. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only declare read-only/idempotent/open-world, but the description adds substantial non-obvious behavior: a synthetic fixed-book capital model (400 slots, anchor 2026-06-16), the hard no-data error for account_type='live', and the rule that paper and live curves are never concatenated. The fixed-window determinism guarantee is also disclosed, which is genuinely useful context an agent cannot infer from annotations.

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?

Clearly front-loaded and labeled (Purpose / Triggers / When to call / Prerequisites / Next steps / Caveats), so the agent can scan quickly. It is somewhat long due to the multilingual trigger list, but every section carries actionable content, so it stays just short of ideal brevity.

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 explained; the description still covers prerequisites, caveats, the required-parameter failure mode, and routing to adjacent tools. Nothing material is missing for an agent to invoke it correctly.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema: it explains the fixed-window semantics that govern window_start/window_end and reinforces that account_type is REQUIRED with a hard-failure behavior for 'live'. These are semantic nuances not fully captured by the schema field descriptions.

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 ('Portfolio-level performance metrics') and enumerates the exact metrics returned (MDD / Sharpe / Sortino / Calmar / monthly returns / equity curve) over a fixed window. It also positions itself as 'the single canonical computation path,' which distinguishes it from sibling breakdown tools like analyze_trades and get_trade_history.

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 'When to call' contexts (track-record verification, blog cross-checks, risk review), a trigger list including casual/multilingual phrasings, prerequisites ('none'), and named next-step alternatives (get_trade_history, analyze_trades). The agent knows exactly when this tool is the right pick versus its siblings.

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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