data_table取「一個標的的多列資料」，如 ETF 全成分股與權重、景氣對策信號 9 項構成指標、主要貨幣對一覽。回應含欄位定義（columns）與列（rows），截斷時標 truncated。Multi-row table for one security.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | 標的識別碼；母體級表格（如 fx.pairs）免填 | |
| limit | No | 回列上限，預設與上限皆為 200 | |
| dataset | Yes | 資料集，如 etf.holdings／fx.pairs／market.climate.components（見 data_catalog） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full disclosure weight. It explains the response shape (columns and rows) and the truncation flag, which are useful behavioral details beyond the schema. It does not mention side effects or permissions, but this is a read-style table lookup and no destructive behavior is implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded with the core behavior, followed by response details and a concise English summary. The English sentence somewhat repeats the Chinese opening, but this is minor and the overall structure is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the return format and truncation behavior, and the schema covers all parameter meanings, so the tool is callable without missing critical information. It could add a bit more about how to discover valid datasets, but the dataset parameter already references data_catalog.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents id, limit, and dataset. The description does not add significant parameter semantics beyond examples already present in the schema, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches multi-row data for one target, with concrete examples like ETF holdings, climate indicator components, and FX pairs. The closing English phrase 'Multi-row table for one security' distinguishes it from single-point or time-series siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The examples and the 'one security' phrasing give an agent a clear sense of when to use this tool, and the schema points to data_catalog for valid datasets. However, it does not explicitly state when to prefer data_point or data_series instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clear boundaries, but there is notable overlap between data_series and etf_price_history/fund_nav_history, since all three can provide time-series data. market_sentiment and taiwan_market_overview also both expose Taiwan sentiment, creating potential selection ambiguity.
The data_* tools follow a clear prefix pattern, but the rest mix noun-style names (fx_rates, market_sentiment), object-action names (etf_lookup, fund_lookup), and generic verbs (search, fetch). The names are readable and understandable, but the overall convention is inconsistent.
At 15 tools, the count is at the upper edge of the ideal range and mostly reasonable for a Taiwan finance data server. However, several domain-specific wrappers duplicate capabilities already available through the generic data_* tools, so the set feels slightly heavier than necessary.
The generic data catalog plus data_point/data_query/data_series/data_table provides broad coverage for read-only financial data, and the domain tools cover ETFs, funds, FX, market overview, sentiment, and climate indicators. Minor gaps remain, such as no explicit Taiwan stock-specific lookup or price history tool, but the search and generic data layers partially compensate.