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OneQAZ Trading Intelligence

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
NameRequiredDescriptionDefault
daysNohow many most-recent chain entries to return (max 400)

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_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"
      +]
  2. Changed1 schema field changed
    • addedInput schema / properties / days / description
      Added value: +"how many most-recent chain entries to return (max 400)"
  3. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses important behaviors: append-only nature at serving-role level, hashes computed once per UTC day, chain inception date, and the verification implication that silent edits will cause recomputation mismatch.

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 long but every labeled section earns its place: Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats, and Output. It is front-loaded with the core purpose and structured for easy parsing by an agent.

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 tool's complexity思想政治, the description includes the output shape, caveats, triggers, and a clear workflow. Nothing an agent needs to decide whether to call it and how to interpret the result 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 optional 'days' parameter is fully documented in the schema with default and max. The description does not add parameter-specific meaning, but the baseline 3 applies because the schema carries the necessary semantics.

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 names a specific resource (the prediction ledger) and a specific purpose (tamper-evidence via a daily SHA-256 hash chain). It clearly differentiates this from ordinary data-retrieval siblings by emphasizing verification and auditability rather than just returning predictions.

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 explicitly provides trigger phrases, states 'FIRST STEP of any serious credibility audit', and recommends periodic re-anchoring. It also lists prerequisites and next steps, so an agent knows exactly when to call this tool versus related ones like get_resolved_predictions.

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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