OneQAZ Trading Intelligence

get_resolved_predictions

Read-onlyIdempotent

Purpose: Raw, row-level prediction ledger — every macro regime prediction's full lifecycle (created_at -> resolved_at -> outcome). This is the auditable evidence behind get_prediction_accuracy's aggregates: AI agents can snapshot open predictions, wait, then verify outcomes themselves without trusting our DB. Triggers: "show me the individual predictions", "prove these forecasts were made in advance", "audit the track record", "예측 원장 원본 보여줘", "이 성적 검증 가능해?". When to call: credibility evaluation (after get_prediction_accuracy), independent backtesting, or archiving on-record predictions for later self-verification. Prerequisites: none. Pairs with get_ledger_integrity for tamper-evidence. Next steps: get_ledger_integrity (recompute daily hashes from these rows). Caveats: cursor pagination (id-ordered) — follow next_cursor for bulk reads. Paper-research forecasts, not investment advice. Output: full_data { predictions[] {id, source_category, source_regime_change, target_market, predicted_regime_shift, lag_hours, confidence, created_at, resolved_at, outcome, actual_regime_shift}, count, next_cursor, has_more, meta }.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`day`	No	filter by created day "YYYY-MM-DD" (UTC, string prefix of created_at)
`limit`	No	page size (max 500)
`cursor`	No	last id from previous page (0 = start)
`status`	No	"all" \| "resolved" \| "open"	all
`target_market`	No	filter e.g. "coin_market" / "kr_market" / "us_market". Aliases coin/kr/us and any letter case are accepted.
`source_category`	No	filter e.g. "vix", "bonds", "commodities"

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_resolved_predictions — 실응답에서 추출(2026-09-23).",
+        "properties": {
+          "count": {
+            "anyOf": [
+              {
+                "type": "integer"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "filters": {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          "has_more": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "meta": {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          "next_cursor": {
+            "anyOf": [
+              {
+                "type": "integer"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "predictions": {
+            "items": {},
+            "type": "array"
+          }
+        },
+        "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"
+]

Changed7 schema fields changedSep 22, 2026

addedInput schema / properties / cursor / description

Added value: +"last id from previous page (0 = start)"

addedInput schema / properties / day / description

Added value: +"filter by created day \"YYYY-MM-DD\" (UTC, string prefix of created_at)"

addedInput schema / properties / limit / description
```
Added value: +"page size (max 500)"
```

addedInput schema / properties / source_category / description

Added value: +"filter e.g. \"vix\", \"bonds\", \"commodities\""

addedInput schema / properties / status / description
```
Added value: +"\"all\" | \"resolved\" | \"open\""
```

addedInput schema / properties / target_market / description

Added value: +"filter e.g. \"coin_market\" / \"kr_market\" / \"us_market\". Aliases coin/kr/us and any letter case are accepted."

addedInput schema / properties / target_market / enum

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

AddedJul 7, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavior beyond that: cursor pagination is explicitly id-ordered with 'follow next_cursor for bulk reads,' and it discloses the non-financial nature ('Paper-research forecasts, not investment advice'). No contradiction with 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?

The description is organized into labeled sections (Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats, Output) and front-loads the core purpose. It is somewhat long but each section serves a role; the only minor redundancy is mentioning get_ledger_integrity in both 'Pairs with' and 'Next steps'.

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 six optional parameters, a fully documented input schema, and an existing output schema, the description closes the remaining gaps: it explains pagination mechanics, points to the companion integrity tool, and states the research-only caveat. Nothing an agent needs to call the tool correctly is missing.

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%, so each of the six parameters is already documented in the schema. The description adds only marginal guidance about cursor pagination ('follow next_cursor for bulk reads'), which slightly extends the schema's 'last id from previous page' but does not substantially compensate beyond the baseline.

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 clear verb and resource: it is a 'Raw, row-level prediction ledger' with the full lifecycle 'created_at -> resolved_at -> outcome'. It explicitly distinguishes itself from get_prediction_accuracy's aggregates, letting an agent tell it apart from sibling tools immediately.

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?

It lists concrete triggers ('show me the individual predictions', 'audit the track record'), a 'When to call' section covering credibility evaluation after get_prediction_accuracy, independent backtesting, and archiving, and a 'Next steps' recommendation pairing it with get_ledger_integrity. This gives explicit, actionable routing with no inference needed.

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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