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OneQAZ Trading Intelligence

by io.github.wnsod
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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. 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"
+]
  2. 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)."
  3. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already signal readOnly, openWorld, and idempotent behavior. The description adds substantial caveats beyond those annotations: synthetic fixed-book capital model, account_type='live' returning an explicit no-data error, paper/live curves never being concatenated, and fixed-window reproducibility.

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 clear labels—Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats—and front-loads the core purpose and metrics. Every section provides distinct, non-redundant information that earns its place.

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?

Given the rich input schema, output schema, and annotations, the description is complete: it covers purpose, trigger phrasing, use cases, prerequisites, follow-up tools, and critical behavioral caveats. An agent has everything needed to select and correctly invoke this 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?

Input schema coverage is 100%, so the baseline is a 3. The description adds extra meaning beyond the schema by emphasizing that account_type is required, that 'live' returns a no-data error until real records exist, and that the fixed window makes results reproducible for the same 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?

The description states a specific verb and resource: portfolio-level performance metrics (MDD, Sharpe, Sortino, Calmar, monthly returns, equity curve) over a fixed window. It further distinguishes itself as 'the single canonical computation path' shared by the OneQAZ blog and external clients, clearly separating it from sibling metric tools.

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?

The description provides explicit triggers, including casual question examples, a 'When to call' section, prerequisites, and next steps. It does not formally enumerate when not to use this tool versus specific sibling tools, but the canonical-path framing and follow-up tool pointers supply strong orientation.

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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