台股題材與高股息

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

With no annotations, the description carries the full burden, and it does disclose meaningful behavior: the estimate is derived mechanically from historical dividends, and the output is framed as a range rather than a point prediction. It also sets expectations that the result is not investment advice. It does not state permissions or return format, but for a read-only estimation tool this is solid disclosure.

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?

A single front-loaded sentence carries the core purpose, methodology and two metrics, followed by a short disclaimer. No padding, though the disclaimer sentence is marginal.

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?

With no output schema and no annotations, the description does describe what gets returned (dividend range plus estimated yield under two methodologies), which is the main thing an agent needs. Minor gaps remain on permissions and precise return structure, but it is largely complete for a single-optional-param read tool.

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% (the single 'code' param documents '不填回傳全部'), so the schema already does the work. The description adds nothing about the code parameter, making the baseline 3 correct.

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?

States a specific verb and resource: mechanically extrapolating a high-dividend ETF's 2027 dividend range and estimated yield from historical payouts. It goes beyond the name by naming the two yield methodologies (trailing 12-month sum vs latest-period annualized), which lets an agent distinguish it from the sibling get_etf_dividends, though it never routes explicitly against that sibling.

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?

There is no when-to-use guidance and no comparison to the alternative get_etf_dividends, which is the most likely source of confusion. The closing '不是投資建議' is a disclaimer, not usage guidance, so the agent must infer the use case entirely.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full behavioral burden, and it largely does not: nothing is said about permissions, rate limits, pagination, or the default behavior when code is omitted. Read-only-ness is only inferable from the verb 查詢. The only behavioral content is the implied set of returned fields.

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?

A single front-loaded sentence with no filler: the resource, the sample codes, and the returned fields in one pass. Every clause earns its place.

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?

There is no output schema, but the description compensates by naming the return fields (配息、除息日、發放日), and the single optional parameter is covered by the schema. Missing only the read-only/scope statement and any note on history depth or sorting.

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?

With 100% schema coverage on a single parameter, the baseline is 3, but the description adds genuine value by listing example valid codes (0056、00878、00919、00929、00713、00940), which is not in the schema. It does not restate the schema's fallback behavior ('不填回傳總表'), but the examples make valid inputs concrete.

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 names a specific verb (查詢) and resource (台股高股息 ETF 配息) and enumerates the returned fields (每期配息、除息日、發放日) plus concrete ETF codes, so an agent immediately knows what data comes back. It stops short of explicitly distinguishing this from the sibling estimate_etf_dividend_2027, leaving the actual-vs-estimated distinction to inference.

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?

Usage is only implied: the mention of 每期配息 and specific existing ETF tickers suggests this is for retrieving recorded distributions rather than projections. However, with estimate_etf_dividend_2027 as a sibling, the description never states when to prefer one over the other. No explicit exclusions or prerequisites are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full behavioral burden. It implies a read (查詢) and lists the returned data, but says nothing about freshness/latency, rate limits, permissions, or error behavior for an invalid code.

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?

A single front-loaded sentence that lists the returned fields with no filler. It is efficient, though it packs the return-value enumeration rather than leading with the core action.

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?

With no output schema, the description helpfully enumerates the returned fields (close, change, volume, theme, 60-day history), which partially compensates. However, with no annotations it omits any behavioral context an agent would want for correct invocation.

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 'code' parameter is fully documented with an example (2330). The description adds no extra format or constraint details beyond the schema, so the baseline of 3 for a fully covered schema 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?

States a specific verb (查詢) and resource (題材股), and enumerates the returned fields (最新收盤、漲跌幅、成交量、所屬題材、近 60 個交易日收盤), so an agent understands it retrieves a single theme stock's data. It does not explicitly differentiate itself from siblings like get_theme_stocks or list_stock_themes, which limits it below a 5.

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?

There is no explicit when-to-use guidance, no prerequisites, and no naming of alternatives among the four sibling tools. The agent must infer that it should be called with a single stock code, but nothing routes it away from get_theme_stocks or list_stock_themes.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 does disclose the shape of the returned data and notes that the classification is accompanied by its source (附分類依據出處), which is useful. It says nothing about data freshness, update cadence, coverage limits, or auth needs, which are the remaining behavioral gaps.

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?

A single dense sentence with the action front-loaded and the returned fields enumerated compactly. Nothing is redundant or padded.

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?

There is no output schema, and the description compensates well by listing exactly what comes back, including the supply-chain role and the classification source. Combined with the fully-covered enum parameter, an agent has enough to invoke it correctly; only freshness/scope caveats are absent.

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% and the single parameter is a fully enumerated 'theme' field. The description only restates that it takes a theme (某個台股題材) and adds no format or selection guidance beyond the schema, so the baseline 3 applies.

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 (取得) and resource (某個台股題材的概念股名單) and even enumerates the returned fields (代號、名稱、上市或上櫃、供應鏈角色、收盤價與漲跌幅). This clearly separates it from get_stock (single stock) and list_stock_themes (theme enumeration). It stops short of naming a sibling explicitly, so it does not reach the top tier.

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?

Usage is only implied: the single required 'theme' enum makes the intended call pattern apparent, and the description shows it retrieves the constituent list for one theme. There is no explicit when-to-use vs. list_stock_themes guidance, no prerequisite steps, and no exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description must carry the behavioral load. It discloses the upstream data source (TWSE OpenAPI) and the categories of data returned, which implies a safe read-only lookup, but it says nothing about return format, pagination, freshness/latency, or rate limits.

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?

A single compact sentence front-loads the resource and then enumerates the fields, with no filler. It could be marginally clearer by naming the implicit 'get' action or the sibling distinction, but it wastes nothing.

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?

There is no output schema, and the description covers the returned content well (dividend types, amounts, resolution status, key dates). For a single-optional-parameter read tool this is largely sufficient; only usage routing and return-format details remain missing.

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?

With one parameter, 100% schema description coverage, and a 0% required-parameter set, the schema already documents the code argument (example 2330, empty returns latest announcement). The description adds no parameter-level detail beyond that, so the baseline 3 applies.

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 resource (Taiwan listed-company dividend distribution) and enumerates the content returned: cash dividends, stock dividends in NT$/share, resolution progress, board and shareholder meeting dates, plus the data source (TWSE OpenAPI). This clearly separates it from ETF siblings like get_etf_dividends, though it never explicitly contrasts itself with the close sibling get_tw_ex_dividend.

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?

There is no statement of when to use this tool versus alternatives such as get_tw_ex_dividend or get_etf_dividends, and no prerequisites or exclusions are given. The only usage hint (leaving code empty returns the latest announcement) lives in the schema, not the description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are supplied, so the description carries the burden; it does disclose scope (a forward-looking 預告 of upcoming events) and a concrete data source (TWSE TWT48U), which is real added context. It stops short of covering read-only nature, pagination, or return shape.

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?

A single dense line that front-loads the resource and then lists the returned fields with zero filler. Every clause contributes information.

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?

With no output schema and no annotations, the description compensates by enumerating the returned fields and citing the source, which is enough to call a one-param read tool correctly. The main gap is the lack of differentiation from get_tw_dividend and get_etf_dividends.

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% and the single optional 'code' filter is fully documented in the schema itself ('不填回傳全部'). The description adds nothing beyond that, so the baseline of 3 applies.

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?

States a specific resource (Taiwan stock ex-rights/ex-dividend announcements) and enumerates the data it surfaces (ex-date, dividend type, cash dividend, stock ratio). It also flags ETF coverage, which loosely separates it from ETF-only siblings, but never names get_tw_dividend or get_etf_dividends to draw a clean boundary.

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?

There is no guidance on when to pick this over the very similar sibling get_tw_dividend, nor any note that it covers upcoming events only. The agent must infer usage purely from the tool name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full behavioral burden. It usefully discloses what the result contains (cash dividend, last close, estimated yield) and that it is a future ex-dividend calendar, but it never states the read-only nature, whether results are paginated, or thresholds/limits, and no output schema backs it up.

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?

A single front-loaded sentence that packs the resource, returned fields, and filter into one line with no filler. It is appropriately sized, though the comma-chained clauses are dense rather than clearly structured.

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 query tool with no output schema and no annotations, the description does convey the returned data and the yield filter, which is the core need. However, with zero required parameters it does not explain what happens if code is omitted (all stocks?) or how the default days window behaves, leaving gaps an agent must guess at.

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 each parameter (code, days with default 30/max 120, min_yield). The description's '可篩最低殖利率' merely restates the min_yield parameter and adds no format or syntax beyond the schema, so the baseline 3 applies.

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 single sentence gives a specific verb+resource (查詢 Taiwan-listed stocks/ETFs future ex-dividend calendar) and adds the distinguishing data payload (cash dividend, closing price, estimated yield, min-yield filter). It is clear what the tool does, but it never names or contrasts with the many sibling dividend tools (get_tw_ex_dividend, get_tw_dividend, get_etf_dividends), so differentiation is left to inference.

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?

There is no statement of when to use this tool versus the surrounding siblings that all deal with dividends or ex-dividend data. The mention that it 'can filter minimum yield' hints at a use case but is not framed as guidance, and no exclusions or prerequisites are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full behavioral burden. It does disclose useful traits: the data is 盤後 (post-market), covers 上市股票 only, and uses 張 as the unit. However, it omits auth needs, rate limits, and return structure.

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?

A single dense sentence covering the key content categories with no filler. The list format is compact and front-loaded, though slightly listy rather than prose-narrative.

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?

With no output schema and no annotations, the description does reasonably well by enumerating what is returned (balances, changes, rankings, top-20 foreign holdings). For a read-only data tool this is close to complete, with only response format details left implicit.

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 single 'code' parameter and its fallback behavior are already documented in the schema. The description adds no parameter detail beyond that, so the baseline 3 applies.

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 names the specific resource and contents: 融資融券餘額與增減, 全市場融資增減排行, and 外資持股比率前 20. An agent can tell this is margin/short-selling chip data for Taiwan listed stocks, though it does not explicitly differentiate itself from siblings like get_tw_market_quotes or get_stock.

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?

There is no explicit statement of when to use this tool versus alternatives, nor any exclusions. The scope note (上市股票) implies usage, but the agent must infer it from the content list rather than being guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full behavioral burden. It does usefully disclose the data source (TWSE basic market report) and the freshness trait (intraday real-time, possibly slightly delayed), which is real context beyond the schema. However it says nothing about auth requirements, rate limits, or response shape, so gaps remain for a no-annotation tool.

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?

Two sentences, front-loaded with the returned content and followed by the code-scope note; no filler. It could be marginally tighter but every clause carries information.

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 single optional-parameter, no-output-schema, no-annotation tool, the description covers what is returned, the data source, freshness, and the code scope. Only operational details (auth, rate limits, pagination) are missing, which is a minor gap at this complexity.

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% (baseline 3), and the description adds meaning on top: it clarifies the accepted code scope explicitly includes OTC-listed and ETF symbols, which the schema only hints at via the '0050' example. That clarification earns an above-baseline score.

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 names a specific resource (Taiwan market quotes) and enumerates exactly what it returns: weighted index, OTC index, heavyweight stocks, and popular ETFs, plus arbitrary TW codes. It is clearly distinguishable in content from dividend/chip/news siblings, though it never explicitly contrasts with the overlapping get_stock or the parallel get_us_quotes.

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?

It conveys implied usage (leave codes empty for the market overview, pass codes for individual names) but gives no when-to-use vs when-not guidance and never names an alternative such as get_stock for single-stock lookups or get_us_quotes for the US market. An agent must infer routing from content alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose a non-obvious default (unfiltered calls prioritize AI-theme stocks) and that results are announcement subjects (主旨), not full text. It says nothing about permissions, result ordering beyond the default, or pagination behavior, leaving meaningful gaps for a no-annotation tool.

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?

A single sentence that front-loads the resource, then the data source, then the filtering behavior. Every clause earns its place with no filler, though the parenthetical source code is more for provenance than for invocation.

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 two-parameter, no-annotation read tool with no output schema, the description covers the resource, source, filtering, and default behavior, and clarifies that the returned data is headline subjects. What remains missing (return shape details, auth) is minor for this tool class.

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%, so the baseline is 3, but the description adds real meaning beyond the schema by clarifying that `code` is optional and defining what happens when it is omitted — the schema only labels it '股票代號' with no optionality semantics. The `limit` bounds are left to the schema, which already documents them.

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?

States a specific resource (台股上市公司每日重大訊息主旨) and even names the upstream data source (證交所 t187ap04_L), which clearly distinguishes it from sibling tools like get_tw_dividend or get_tw_market_quotes. It is not tautological and an agent can identify the target data without opening the schema.

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 clause '可依代號篩選；不填則 AI 題材股優先' gives a usage hint and the default behavior when the filter is omitted, which is genuinely useful. However, it never states when to prefer this tool over alternatives (e.g. general stock news vs. theme tools), so usage is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations exist, so the description carries the behavioral burden. It helpfully flags that the last-buy date is 估算 (estimated), which is real behavioral context, but says nothing about return format, pagination, or data source currency for a tool returning multiple fields.

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?

Two sentences, front-loaded with the capability then the usage trigger, with no filler. The field enumeration is dense but each item earns its place by telling the agent what comes back.

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?

With no output schema and no annotations, the description compensates by listing the returned fields and usage triggers, which is enough to call the tool correctly. It could still say more about the estimated-date caveat or data freshness, but overall it is complete enough.

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 all five parameters (code, days, from, kind, buy_window) are already documented in the schema. The description adds no parameter-level syntax or defaults beyond that, so the baseline 3 applies.

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?

States a specific verb (查詢/query) and resource (台灣上市公司股東會) and enumerates the returned fields (日期、地點、停止過戶日、最後買進日、股務代理、電子投票). No sibling tool covers shareholder meetings, so the agent can distinguish it immediately.

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?

Gives concrete trigger examples (「股東會紀念品最後買進日」「台積電股東會哪天」) that map user intent to this tool. It stops short of naming alternatives or when-not-to-use conditions, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations the description carries the full burden, and it does disclose the key behavioral trait: data is delayed ~15 minutes. It says nothing about auth needs, rate limits, or return format, so it is adequate but incomplete.

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?

One sentence with the purpose front-loaded, which is good. The long trailing catalog of sectors and example ETFs reads as filler that does not help an agent select or invoke the tool.

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 1-parameter, no-output-schema quote tool the description covers delay and the covered universe, but leaves return shape and edge cases unstated. Adequate for a simple read tool, with clear gaps.

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% for the single 'symbols' parameter, so the baseline is 3. The description's catalog of covered themes and ETFs adds a little domain context but no syntax or format detail beyond the schema.

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?

States a specific verb and resource — '美股延遲報價' (US stock delayed quotes) — with a delay qualifier, so an agent knows what it fetches. However, it never distinguishes itself from the sibling get_us_stock or get_stock, so sibling differentiation is missing.

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 no when-to-use guidance and names no alternative tool; the only routing hint ('不填回傳本站追蹤清單') lives in the schema, not the description. An agent must infer when to prefer this over get_us_stock.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses one behavioral trait — quotes are 延遲 (delayed), not real-time — and reveals the breadth of data returned. However, it does not state that the tool is read-only, whether results are cached or how fresh the financial/13F data is, which matter given zero annotation coverage.

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?

A single dense sentence that front-loads the resource ('美股個股完整資料') before listing contents. The enumeration is long but each item informs the agent about available data categories, so it largely earns its place; only slight trimming would improve it.

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?

With no output schema, the description appropriately substitutes by listing return content. But for a tool with near-identical siblings, the absence of routing guidance (vs get_us_quotes / get_stock) and any freshness/rate caveats leaves gaps. Adequate but with clear holes.

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% and the single `symbol` parameter is fully documented in the schema with examples (NVDA, AAPL, TSM). The description adds no meaning beyond the schema, so the baseline 3 for high coverage applies.

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+resource (get complete US individual stock data) and enumerates the exact content domains returned: delayed quotes, SEC financials, dividends/ex-dividend, 13F holdings, Form 4 insider filings, 8-K events. It clearly conveys what the tool fetches, but it does not explicitly differentiate itself from the close sibling get_us_quotes (quotes) or get_stock, so it stops short of a 5.

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?

There is no when-to-use, when-not-to-use, or alternative guidance. With get_us_quotes and get_stock among the siblings, an agent must infer on its own that this is the comprehensive option and the others are narrower. The enumerated content implies usage but never states it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full behavioral burden. It usefully scopes the tool to exactly four named AI themes and states it returns constituent counts plus today's change summary, which sets freshness and granularity expectations. It omits ordering, data source, and how the counts relate to per-stock data in sibling tools.

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?

A single front-loaded sentence with zero waste; the theme enumeration is the payload definition rather than filler. Every clause earns its place.

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 list tool with no annotations and no output schema, the description does say what comes back. However, the presence of get_theme_stocks leaves a real routing ambiguity that the description should resolve, and it says nothing about output shape beyond counts and a summary.

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 takes zero parameters, so per the rubric the baseline is 4. There are no parameter semantics to clarify and none are needed.

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?

States a specific verb (列出/list) and resource (台股四個 AI 題材) and even enumerates the four themes and the returned fields (成分股數與今日漲跌摘要). A sibling named get_theme_stocks exists, yet the description never clarifies how this differs, so it stops short of full sibling differentiation.

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?

There is no explicit when-to-use guidance and no mention of alternatives such as get_theme_stocks or get_stock. A reader can infer it is a discovery/overview call, but nothing tells them when to prefer this over the closely named sibling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.