OneQAZ Trading Intelligence

Prediction Ledger Integrity

get_ledger_integrity

Read-onlyIdempotent

Purpose: Tamper-evidence for the prediction ledger — a daily SHA-256 hash chain over all created/resolved prediction rows, with the exact canonical recipe published so any third party can recompute and verify. Archive a chain_hash today; if history is ever silently edited, recomputation will not match. Triggers: "how do I know these predictions weren't backfilled?", "is the track record tamper-proof?", "예측 조작 안 했다는 증거 있어?", "verify ledger integrity". When to call: FIRST STEP of any serious credibility audit, and periodically to re-anchor (each entry commits to all prior history via prev_chain_hash). Prerequisites: none. Raw rows for recomputation: get_resolved_predictions. Next steps: get_resolved_predictions (fetch a day's raw rows, recompute its hash). Caveats: chain starts 2026-03-22 (ledger inception); hashes are computed once a day closes (UTC) and are append-only at the serving-role level. Output: full_data { recipe_version, recipe, chain_length, first_day, last_day, entries[] {day, created_count, resolved_count, created_hash, resolved_hash, prev_chain_hash, chain_hash, computed_at}, verification_hint }.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days`	No	how many most-recent chain entries to return (max 400)

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.

Changed1 schema field changedOct 7, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```

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_ledger_integrity — 실응답에서 추출(2026-09-23).",
+        "properties": {
+          "chain_length": {
+            "anyOf": [
+              {
+                "type": "integer"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "entries": {
+            "items": {},
+            "type": "array"
+          },
+          "external_anchors": {
+            "additionalProperties": true,
+            "type": "object"
+          },
+          "first_day": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "last_day": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "recipe": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "recipe_version": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null
+          },
+          "verification_hint": {
+            "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"
+]

Changed1 schema field changedSep 22, 2026
- addedInput schema / properties / days / description
```
Added value: +"how many most-recent chain entries to return (max 400)"
```
AddedJul 7, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations cover read-only/idempotent/open-world, and the description adds genuinely non-obvious behavior on top: the chain inception date (2026-03-22), that hashes are computed once a day closes in UTC, and that entries are append-only at the serving-role level. It also explains the prev_chain_hash commitment model, which explains why re-anchoring matters — context no annotation conveys.

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?

Front-loaded with a labeled Purpose line and organized into Triggers/When/Prerequisites/Next steps/Caveats/Output sections, so an agent can scan quickly. It is somewhat over-stuffed — the duplicated multilingual trigger list and the Output block restate the output schema field-by-field — costing some economy.

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?

An output schema exists, so the description need not detail returns, yet it still supplies the edge cases an agent needs: chain start date, UTC daily closure, append-only semantics, and a verification hint. Nothing required to invoke or interpret this 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 coverage is 100% and the single parameter ('days', max 400) is fully documented in the schema itself. The prose never elaborates on the parameter's meaning or default behavior, so the description adds nothing beyond what the schema already provides — the baseline 3 for a fully-covered single-param tool.

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 ('tamper-evidence for the prediction ledger') and defines the mechanism precisely — a daily SHA-256 hash chain over created/resolved prediction rows with a published canonical recipe. It is clearly distinguishable from siblings like get_resolved_predictions, which is framed as the raw-data counterpart rather than the integrity check.

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?

Gives explicit triggers (including multilingual phrasings), names the condition for calling ('FIRST STEP of any serious credibility audit'), states prerequisites ('none'), and routes to next steps ('get_resolved_predictions'). This is textbook when/when-not/alternatives 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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