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OneQAZ Trading Intelligence

get_monthly_accuracy_trend

Read-onlyIdempotent

Purpose: Monthly accuracy time series per (category, target_market, lag_bucket). Use to verify sustained performance and detect recent degradation. Triggers (casual questions too): "is accuracy improving?", "적중률이 좋아지고 있어?", "monthly performance trend?", "최근에 예측 성능 떨어졌어?", "show accuracy over time". When to call: after get_prediction_accuracy and get_backtest_tuning_state — completes the trust chain. Prerequisites: get_prediction_accuracy recommended. Next steps: none (trust chain complete). Caveats: excludes the 'all' month aggregate; empty when backtest_results is unpopulated.

Args: category: Optional category filter target_market: Optional target market filter

Disclaimer: Information only, not investment advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNo
target_marketNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoSet true on error responses
actionNoRecommended client action (error path)
reasonNoHuman-readable cause (error path)
full_dataNo
retryableNoWhether the client should retry (error path)
timestampYesRFC3339 UTC, server build time
ai_summaryNoOne-line AI-oriented summary (success path)
disclaimerYesCanonical compliance disclaimer (always present)
error_codeNoStable error identifier; see mcp_error_policy.md
request_idYes32-hex per-response correlation id
_llm_summaryNo
action_valueNo
_next_actionsNo
fallback_noteNo
fallback_toolNoSuggested fallback (error path)
is_real_moneyNo
_value_signalsNo
summary_for_userNoOne-line jargon-free Korean summary (success path)
data_classificationNo
is_investment_adviceNo
ai_summary_ttl_secondsNo
_market_state_narrativeNo
ai_summary_generated_atNoRFC3339 UTC
_followup_questions_for_userNo

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds valuable caveats about excluding the 'all' month aggregate and being empty when backtest_results is unpopulated, which goes beyond the structured metadata. 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 well-structured with labeled sections (Purpose, Triggers, When to call, Caveats) and front-loaded purpose. It is a bit lengthy due to trigger examples and disclaimer, but each section earns its place and improves usability.

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 output schema exists, the description covers the essential context: purpose, triggers, usage chain, prerequisites, caveats, and disclaimer. The tool is simple (2 optional params) and the description fully enables correct selection and invocation.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for parameter semantics. It only restates 'Optional category filter' and 'Optional target market filter', adding no format, allowed values, or behavior details. The parameter names are self-evident but the description adds minimal value over the bare schema.

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 'Monthly accuracy time series per (category, target_market, lag_bucket)' with a specific verb ('verify') and resource. It distinguishes itself from siblings like get_prediction_accuracy by emphasizing the time-series/trend aspect and explicitly listing trigger phrases for degradation detection.

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 when-to-call guidance ('after get_prediction_accuracy and get_backtest_tuning_state'), prerequisites, next steps ('trust chain complete'), and example trigger questions. This fully orients the agent on placement relative to alternatives.

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.