基金淨值歷史
fund_nav_history取基金每日淨值序列(預設近 12 個月、可調 months 1-120;境內主級別最深約 10 年)。Daily NAV history for a fund.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| months | No |
fund_nav_history取基金每日淨值序列(預設近 12 個月、可調 months 1-120;境內主級別最深約 10 年)。Daily NAV history for a fund.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| months | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the default period, parameter range, and depth limit, which is helpful, but it does not describe the response format, data coverage nuances, or any access considerations.
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 a single compact sentence that front-loads the purpose and packs the key parameters and limits into a parenthetical. There is minimal waste; the bilingual phrasing is redundant but acceptable.
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 simple two-parameter tool, the definition covers the main behavior and months constraints. But with no output schema and no annotations, it leaves the code format and exact returned NAV fields underspecified, so an agent would still have to infer some details.
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 0%, and the description adds meaning for months ('1-120', default 12), which helps. However, the required code parameter is only inferable as a fund identifier, with no format or examples provided, so compensation is only partial.
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: '取基金每日淨值序列' / 'Daily NAV history for a fund.' It clearly identifies a fund NAV history operation, distinct from sibling tools like fund_lookup and etf_price_history.
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 gives useful usage context: default 12 months, adjustable 1-120 months, and ~10-year depth for domestic primary share classes. However, it does not explicitly mention when to use this tool instead of alternatives or any exclusions.
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.