search以一段查詢字串搜尋本站的 ETF、台股、基金與資料集，回傳可引用的清單（id／title／url）。id 可交給 fetch 取完整內容。A single query string; returns citable results for Taiwan ETFs, stocks, funds and datasets.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | A single query string. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries much of the behavioral burden. It usefully discloses that results are citable, include id/title/url, and that the id can be passed to fetch. It does not mention potential result limits, pagination, ordering, or any rate/access considerations, leaving some behavioral uncertainty for a search operation.
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, front-loaded with the core action, and conveys the important return format and fetch linkage in two sentences. The bilingual repetition is slightly redundant but not wasteful, and every key fact an agent needs fits in a compact space.
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?
For a one-parameter tool with no output schema and no annotations, this description covers the essential invocation context: what to search, what categories are included, what the result looks like, and how to proceed with fetch. It omits details like maximum result count or pagination, but the tool is simple enough that the missing pieces are not critical.
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 coverage is 100%, and the single query parameter already has a clear description: 'A single query string.' The tool description repeats this phrasing and adds the context that the query searches across the named asset/dataset categories, but does not add deeper semantics such as query syntax, supported operators, or length constraints.
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 states a specific verb and resource: a query string searches the site's ETFs, Taiwan stocks, funds, and datasets, returning a citable list with id/title/url. It also explains the follow-up relationship with fetch, which helps distinguish it from specialized lookup siblings such as etf_lookup and fund_lookup.
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 intended use is implied: use search when you have a free-text query spanning ETFs, stocks, funds, or datasets, and use the returned id with fetch for full content. However, it does not explicitly state when to prefer search over the sibling lookup tools or what conditions would make another tool more appropriate.
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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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.