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OneQAZ Trading Intelligence

Trade Outcomes (Bulk Export)

get_trade_outcomes_bulk
Read-onlyIdempotent

Purpose: Cursor-paginated bulk export of the prediction -> trade -> outcome chain — paper trades with realized P&L, each linked (best-effort, same-symbol 2h window) to the signal prediction that preceded entry. Built for pipeline consumers who need offline backtesting data, not conversational snippets. Triggers: "give me your full trade history for backtesting", "bulk export trades", "예측이 실제 매매 성과로 이어졌는지 원데이터로 검증하고 싶다", "download outcomes". When to call: offline verification, periodic ingestion into a research pipeline, or auditing whether signals translate into realized outcomes. Prerequisites: none. For the prediction ledger itself use get_resolved_predictions. Next steps: follow next_cursor until has_more=false; get_resolved_predictions to cross-check linked predictions against the tamper-evident ledger. Caveats: linkage is temporal matching, NOT a foreign key (see meta.linkage). Paper trading only — envelope carries the standard disclaimer once per page. Output: full_data { market, trades[] {id, symbol, action, entry/exit price+ts, profit_loss_pct, holding_duration, entry_signal_score, regime fields, policy_version, sizing fields, linked_prediction{...}|null}, count, linked_prediction_count, next_cursor, has_more, meta }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoexit-time window in days (max 120)
limitNoPage size (default 50, max 500). Cursor-paginated: follow next_cursor while has_more is true to retrieve everything.
cursorNolast trade id from previous page (0 = start)
marketNo"crypto" (default) / "kr_stock" / "us_stock"crypto

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_trade_outcomes_bulk — 실응답에서 추출(2026-09-23).",
      +        "properties": {
      +          "count": {
      +            "anyOf": [
      +              {
      +                "type": "integer"
      +              },
      +              {
      +                "type": "null"
      +              }
      +            ],
      +            "default": null
      +          },
      +          "has_more": {
      +            "anyOf": [
      +              {
      +                "type": "boolean"
      +              },
      +              {
      +                "type": "null"
      +              }
      +            ],
      +            "default": null
      +          },
      +          "linked_prediction_count": {
      +            "anyOf": [
      +              {
      +                "type": "integer"
      +              },
      +              {
      +                "type": "null"
      +              }
      +            ],
      +            "default": null
      +          },
      +          "market": {
      +            "anyOf": [
      +              {
      +                "type": "string"
      +              },
      +              {
      +                "type": "null"
      +              }
      +            ],
      +            "default": null
      +          },
      +          "meta": {
      +            "additionalProperties": true,
      +            "type": "object"
      +          },
      +          "next_cursor": {
      +            "anyOf": [
      +              {
      +                "type": "integer"
      +              },
      +              {
      +                "type": "null"
      +              }
      +            ],
      +            "default": null
      +          },
      +          "trades": {
      +            "items": {},
      +            "type": "array"
      +          },
      +          "window_days": {
      +            "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"
      +]
  3. Changed2 schema fields changed
    • changedInput schema / properties / limit / default
      Previous value: -200New value: +50
    • changedInput schema / properties / limit / description
      Previous value: -"page size (max 500)"New value: +"Page size (default 50, max 500). Cursor-paginated: follow next_cursor while has_more is true to retrieve everything."
  4. Changed4 schema fields changed
    • addedInput schema / properties / cursor / description
      Added value: +"last trade id from previous page (0 = start)"
    • addedInput schema / properties / days / description
      Added value: +"exit-time window in days (max 120)"
    • addedInput schema / properties / limit / description
      Added value: +"page size (max 500)"
    • addedInput schema / properties / market / description
      Added value: +"\"crypto\" (default) / \"kr_stock\" / \"us_stock\""
  5. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already establish readOnly/idempotent/openWorld, but the description goes well beyond them: it discloses that linkage is temporal matching rather than a foreign key, that data is paper-trading only, and that a disclaimer appears once per page. These are exactly the caveats an agent needs before treating the output as authoritative.

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-structured with labeled Purpose/Triggers/When to call/Prerequisites/Next steps/Caveats/Output sections and the core purpose front-loaded. It is on the long side and the bilingual trigger examples are somewhat verbose, but every section carries distinct information rather than repeating the schema.

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 paginated bulk-export tool with an output schema, the description covers data source, linkage caveats, pagination workflow, cross-check path, and output shape. An agent has everything needed to call it correctly and interpret the result; nothing material is missing.

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 schema already documents days/limit/cursor/market with defaults and maxes; the baseline is 3. The description adds operational meaning by spelling out the cursor-follow workflow ('follow next_cursor until has_more=false'), but it does not explain parameter semantics (e.g., why cursor is an integer trade id) beyond what the schema states.

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?

Opens with a specific verb+resource+scope: 'Cursor-paginated bulk export of the prediction -> trade -> outcome chain — paper trades with realized P&L.' It also names the linkage semantics (best-effort, same-symbol 2h window) and explicitly contrasts with the sibling get_resolved_predictions, so an agent can distinguish it from get_trade_history and the prediction tools without opening a schema.

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?

Explicit Triggers section with example utterances, an explicit 'When to call' clause (offline verification, periodic ingestion, auditing), a named alternative for the prediction ledger, and a 'Next steps' section describing the pagination loop. Nothing about when to select this tool 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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