tw-market-data

Company health check

company_health_check

Read-onlyIdempotent

一檔股票的誠實體檢:成長、獲利品質、估值、籌碼、紅旗,每個數字可驗證。

    ★ 這是**事實整理**,不是投資建議、不是預測、不是目標價。輸出帶
    `does_not_prove`,轉述時請一併保留。

    ★ POINT-IN-TIME:`as_of` 逐資料集尊重揭露時差 —— 月營收用法定截止日推得的
    估計時點,財報用出表日,估值是當日。不給 `as_of` 就用「現在可知的最新」,
    **不是今天**。

    ★ 沒看到的東西會列在 `cannot_see`,並說明是「查了沒有」「查詢失敗」還是
    「散佈權未取得」—— 三者不同。⚠️ 不要替 `cannot_see` 裡的項目估算或補值。

    ★ `checked_and_clear` 是「查過而且確認沒有」(例如近一年無裁罰),
    和 `cannot_see` 的「不知道」是兩回事。

    ★ 代號解析不到會回 `needs_disambiguation` 附候選 —— 請回問使用者,不要挑一個。

    ★ PRECONDITIONS:這支工具**自己**去拿九個資料集,所以 rows 不需要先用
    `query_dataset` 取好。它 requires 的是身分:呼叫端 must be 一個已識別的
    session(X-API-Key 或已驗證的 OAuth 登入),因為體檢會寫進呼叫者的工作區
    脈絡。方案沒有涵蓋某個資料集時,那一項會以 `not_licensed` 出現在
    `cannot_see`,而不是讓整支工具失敗 —— 所以不需要 entitlement 先檢查。

    ★ SIDE EFFECTS:read-only。does not 寫入任何資料表,不建立警示、不下單。

Input Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`ticker`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.3/5.0

Behavior5/5

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

Beyond the annotations, it explains point-in-time semantics (as_of and 'latest known, not today'), the cannot_see categories, checked_and_clear vs cannot_see, disambiguation behavior, self-fetching of nine datasets, licensing fallback, and read-only side effects. No contradiction with annotations; all consistent.

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?

Bulleted structure with bold labels makes the dense content scannable and front-loads the one-line summary. Each bullet carries substantive operational or behavioral guidance; slightly long but not padded.

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 tool that self-fetches nine datasets, the description covers preconditions, auth, licensing, edge cases, output semantics, and side effects. Since an output schema exists, omission of exact return shape is acceptable.

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?

The description adds detailed meaning to as_of (disclosure timing, fallback to latest known) and gives ticker-resolution context (needs_disambiguation). It does not specify accepted ticker format or exchange suffix, but schema coverage is 0% and the description does real compensating work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening line defines an 'honest health check' of a stock covering growth, earnings quality, valuation, positioning, and red flags, and clarifies it is fact-gathering, not investment advice. This goes well beyond the title, but it does not compare itself to sibling tools, so it is clear without explicit differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: it is for fact-finding, explicitly not investment advice/prediction/target price, and says callers do not need to stage data with query_dataset first. It also states session identity requirements and that entitlement checks are unnecessary. It does not fully enumerate alternative tools for adjacent tasks.

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

A4/5.0

Disambiguation4/5

The tools are largely distinct: querying, searching, backtesting, risk reads, alerts, memory, and audit functions each have clear homes. A few adjacent pairs (risk_read vs risk_assess, company_health_check vs positioning_read) could be confused, but the descriptions draw explicit boundaries.

Naming Consistency3/5

There are strong consistent clusters like list_*, get_*, run_*, and memory_*, but the *_read suffix alternates with noun-first names like company_health_check, and bare-verb tools like ask, calendar, chart, and screen break the pattern. The naming is readable but not uniform.

Tool Count2/5

Forty tools is well past the 25+ threshold and makes the surface heavy for an agent to navigate, even though the breadth reflects a genuinely wide platform. Several clusters could plausibly be consolidated without losing capability.

Completeness4/5

The tool surface covers the main lifecycle well: discovery, point-in-time querying, filings search and full text, backtesting, research, risk assessment, alerts, memory, approvals, and provenance verification. Minor gaps exist—no strategy management tools, no memory deletion, no bulk export—but agents can work around them.

Resources

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