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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 }.

Args: days: how many most-recent chain entries to return (max 400)

Disclaimer: Information only, not investment advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark the tool as read-only, open-world, and idempotent. The description adds valuable behavioral context: the hash chain begins 2026-03-22, hashes are computed once daily (UTC), and entries are append-only at the serving-role level. It also discloses the verification_hint output, exceeding what annotations provide.

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 well-structured with labeled sections (Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats, Output, Args) and front-loaded with the core purpose. It is slightly long but every section provides actionable information without redundancy.

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?

Despite having an output schema, the description goes beyond by explaining the output shape, caveats, prerequisites, and usage context. Given the tool's complexity, this is fully complete and leaves no critical gaps for an agent to understand when and how to invoke it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole parameter 'days' is explained in the description as 'how many most-recent chain entries to return (max 400)', adding crucial semantics beyond the schema which only specifies type and default. This fully compensates for the 0% schema description coverage.

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 clearly states the tool's purpose: tamper-evidence for the prediction ledger via a SHA-256 hash chain with a published canonical recipe. It specifies the resource (prediction ledger) and the verb (get integrity/verify), and it is clearly distinct from sibling tools like get_resolved_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?

Provides explicit triggers, 'when to call' guidance, prerequisites, and suggested next steps. It even names the sibling tool to use for raw rows and frames this as the first step in credibility audits, making alternatives and context clear.

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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TDQS

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there are overlapping areas such as get_feature_governance_state vs get_feature_governance_status_tool and the convenience wrappers for losing/winning positions/trades. Descriptions clarify relationships well, so confusion is limited.

Naming Consistency4/5

The vast majority follow a consistent 'get_' prefix with descriptive nouns, but inconsistencies exist: some tools have '_tool' suffix (e.g., get_macro_causality_graph_tool) while similar ones do not, and governance tools use 'state' vs 'status_tool'. Overall, the pattern is predictable.

Tool Count2/5

39 tools is excessive for the apparent scope. Many are redundant convenience wrappers (get_losing_positions, get_winning_trades, etc.) that duplicate filters on other tools, and there are near-duplicate governance tools. The count could be significantly consolidated.

Completeness5/5

The tool surface is remarkably comprehensive, covering signals, trades, predictions, positions, macro relationships, news causality, strategies, structure, governance, and ledger integrity. There are no obvious functional gaps, and the tools form a well-integrated evidence chain.