OneQAZ Trading Intelligence

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Read-onlyIdempotent

Purpose: ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest score-ranked combined signals across crypto / kr_stock / us_stock. Returns result ids consumable by the fetch tool. Triggers: ChatGPT connectors and Deep Research call this automatically for any user query routed to OneQAZ ("bitcoin signal", "prediction accuracy", "korean stocks today", ...). Other AI clients may use it as a keyword starting point when unsure which tool/resource to call. When to call: first step of connector-style discovery. MCP-native clients can instead browse tools/list + resources/list directly. Prerequisites: none. Next steps: pass any result id to fetch for the full document. Caveats: corpus is rebuilt at most every 10 minutes (tool/resource catalog + top-20 signals by |score| per market). Ticker / company-name queries (BTC, NVDA, 삼성전자) resolve to signal:{market}:{symbol} first; market words (S&P, KOSPI, crypto) surface that market's resources. Every result id is fetchable. Empty results come with a note explaining coverage. Output: {results: [{id, title, url}], disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape. Output nature: Observed signal state computed from live market prices each cycle. signal_score is an internal ranking score, not a probability; bias is its sign. Context for the user's question, not investment advice.

Input Schema

Name	Required	Description	Default
`query`	Yes	free-text search string (English/Korean, symbols like BTC/AAPL)

Output Schema

Name	Required	Description	Default
`results`	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 / description

Added value: +"`full_data` for search — 실응답에서 추출(2026-09-23)."

addedOutput schema / properties

Added value: +{
+  "results": {
+    "items": {},
+    "type": "array"
+  }
+}

Changed1 schema field changedSep 22, 2026
- addedInput schema / properties / query / description
```
Added value: +"free-text search string (English/Korean, symbols like BTC/AAPL)"
```
AddedJul 7, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Adds substantial context beyond readOnly/idempotent/openWorld annotations: a 10-minute corpus rebuild cadence, the exact corpus composition (tool/resource catalog plus top-20 signals by |score| per market), query-resolution rules for tickers and market words, and the behavior on empty results (a `note` explaining coverage). These are freshness and edge-case traits the annotations cannot express.

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?

Labeled sections (Purpose/Triggers/When to call/Prerequisites/Next steps/Caveats/Output) make it strongly front-loaded and skimmable. Minor waste: the explicit `Output:` envelope restates the return shape that an output schema already carries, and "Output nature" partially overlaps the disclaimer note.

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?

For a one-parameter discovery tool with an output schema and safety annotations, everything an agent needs is present: freshness limits, query interpretation, sibling routing, failure mode, and the non-advice framing. No material gap remains.

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

Parameters4/5

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

Schema coverage is 100% and the single `query` param is documented in-schema, so baseline would be 3; the description earns extra credit by explaining how query text is interpreted — English/Korean free text, tickers resolving to `signal:{market}:{symbol}`, and market words surfacing that market's resources. That is meaningfully more than the schema's "free-text search string".

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 (discovery search) over a specific resource set (tools, resources, top-20 score-ranked signals across crypto/kr_stock/us_stock), and explicitly ties its output to the `fetch` sibling by result id. An agent can distinguish it from `get_signals` or `fetch` without opening any schema.

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 (ChatGPT connectors/Deep Research auto-routing), a when-to-call rule ("first step of connector-style discovery"), a named alternative for MCP-native clients (tools/list + resources/list), and a next-step handoff to `fetch`. When-not guidance is also present via the alternative path.

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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