data_catalog列出本站「資料形狀 × 資料集」登錄表：有哪些 point（單值）／series（時間序列）／table（表格）／query（條件篩選＋群體聚合）資料集，各自的單位、識別碼種類、來源與深連結；query 母體另附可用欄位、運算子與可排序鍵。先呼叫本工具，即可組合出後續查詢。Catalog of datasets by shape (point/series/table/query) — call this first to compose queries.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It does this well by listing what the catalog returns: dataset shapes, units, identifier types, sources, deep links, and query-specific fields, operators, and sort keys. It does not mention edge cases or errors, but for a zero-parameter read-only catalog this is reasonably transparent.
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 front-loaded with the main purpose and spends only a few sentences on details. The bilingual text adds length through repetition, but each clause still carries meaningful information and there is no filler.
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 zero-parameter discovery tool with no output schema, the description is complete enough: it details the catalog contents and explicitly tells the agent to invoke it first. It does not describe how to consume the returned deep links or identifiers in sibling tools, but the 'compose queries' hint covers the essential workflow.
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?
The tool accepts zero parameters, so there is no parameter semantics to explain. The empty input schema and 100% schema coverage mean the description does not need to compensate for undocumented inputs.
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 uses a specific verb ('列出' / 'list') and a specific resource: the registry of datasets by shape (point/series/table/query). It clearly distinguishes this catalog tool from the data_* siblings, since its job is to describe available datasets rather than return data values.
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 description explicitly says 'call this first' ('先呼叫本工具') and explains that it is used to compose subsequent queries. This gives a clear context for when to use the tool, though it does not state explicit exclusions or alternative routing among siblings.
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.