tw-market-data

Read institutional positioning

positioning_read

Read-onlyIdempotent

籌碼結構:法人分項、融資融券、借券使用率、大戶集中度、董監質押。

    ★ 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`。
    ★ POINT-IN-TIME:`as_of` 逐資料集尊重揭露時差;不給就用「現在可知的最新」。
    ★ 看不到的在 `cannot_see`,並分「查了沒有 / 查詢失敗 / 散佈權未取得」。
      ⚠️ 逐券商分點進出**永遠**在 cannot_see —— 散佈權未取得,不要替它估算。
    ★ PRECONDITIONS:自己取數,rows 不需要先用 `query_dataset` 備好;requires 一個
      已識別的 session。方案沒涵蓋的資料集以 not_licensed 進 cannot_see,不整支失敗。
    ★ Args:`ticker`(代號,例如 "2330")、`as_of`(選填的知識時間界線)。
    ★ Returns:一個信封,`concerns`/`observations` 各帶觸發它的數字與驗證連結,
      缺的東西 listed in `cannot_see`。
    ★ SIDE EFFECTS:read-only,does not 寫入任何資料表。

Input Schema

TableJSON Schema

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

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare read-only/idempotent, but the description adds substantial behavior: point-in-time semantics with as_of, a cannot_see taxonomy (not queried / query failed / distribution rights missing), a hard rule never to estimate broker-level positions, and license-failure handling. This goes well beyond the annotation hints and gives the agent a faithful model of the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but each bullet earns its place: purpose, point-in-time, cannot_see, preconditions, arguments, return envelope, and side effects. The structure uses visual anchors and front-loads the core purpose, making it scannable despite its length.

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 read-only tool with an output schema, the description is remarkably complete. It covers invocation preconditions, argument semantics, visibility limitations, licensing behavior, return shape, and side effects. There is no critical missing context an agent would need to call this tool correctly.

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 0% schema description coverage, the description must carry parameter meaning. It explains ticker with an example ('2330') and describes as_of as an optional knowledge time boundary with point-in-time lag semantics. The only minor gap is that no exact date format or validation rules for as_of are provided, but the semantics are clear enough for correct invocation.

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 opens with a concrete resource ('籌碼結構') and names the specific read operation, making it clear this tool surfaces institutional positioning data. It distinguishes itself from siblings like query_dataset by stating rows do not need to be prepared via query_dataset and by framing output as fact compilation with does_not_prove.

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?

The description gives explicit preconditions: an identified session is required, data is self-fetched and does not need query_dataset, and unlicensed datasets should be routed to cannot_see rather than failing the call. It also states what the tool is not (investment advice, prediction, target price), providing clear when-to-use and when-not-to-use guidance.

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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