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OneQAZ Trading Intelligence

by io.github.wnsod
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Read-onlyIdempotent

Purpose: ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest strong 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 entry 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 strong signals 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesfree-text search string (English/Korean, symbols like BTC/AAPL)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  2. Changed2 schema fields changed
    • addedOutput schema / description
      Added value: +"`full_data` for search — 실응답에서 추출(2026-09-23)."
    • addedOutput schema / properties
      Added value: +{
+  "results": {
+    "items": {},
+    "type": "array"
+  }
+}
  3. Changed1 schema field changed
    • addedInput schema / properties / query / description
      Added value: +"free-text search string (English/Korean, symbols like BTC/AAPL)"
  4. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already cover safety (readOnlyHint, idempotentHint, openWorldHint), and the description layers on non-obvious behavior: corpus rebuild cadence (~10 min), ticker/company-name resolution rules to signal:{market}:{symbol}, market-word routing, and that empty results carry an explanatory `note`. This is genuinely additive context beyond the structured fields.

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 scannable and the purpose is front-loaded. It is longer than strictly necessary — some connective phrasing could be trimmed — but nearly every line carries operational information.

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 discovery entry-point with freshness constraints and special query-resolution rules, the description covers prerequisites, hand-off, staleness, empty-result behavior, and even restates the output envelope (though an output schema exists). Nothing an agent needs to call it correctly is missing.

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?

With one parameter at 100% schema coverage the baseline is 3, but the description adds real meaning: accepted languages (English/Korean), symbol forms (BTC/AAPL/삼성전자), example queries, and how the query is interpreted (tickers resolve to signals, market words surface resources). Minor gain over the schema, which already documents the param.

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 (OneQAZ's tools, resources, and top combined signals across crypto/kr_stock/us_stock), and distinguishes itself from `fetch` by declaring that fetch consumes its result ids. An agent can tell exactly what this does and how it relates to the sibling it hands off to.

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?

Explicitly names triggers (ChatGPT connectors / Deep Research auto-invoke), the position in the workflow ('first step of connector-style discovery'), an alternative path (MCP-native clients can browse tools/list + resources/list instead), and the next step (pass id to `fetch`). When-to-use and when-not-to-use are both covered.

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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