TWSE Open Data (Taiwan Stock Exchange)

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

Annotations are not provided, so the description carries the full burden. It implies a read-only operation via '查看' (view) and places it 'before fetching data', which adequately communicates non-destructive behavior for a describe tool. However, it does not explicitly mention additional behavioral details like return format or potential errors, and there is no annotation context to supplement.

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, well-structured sentence in Chinese. It front-loads the core purpose and then adds usage context, with no redundant words or filler. It earns a perfect score for conciseness.

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 should clarify what the agent receives as a response. It states the tool shows '完整欄位定義' (complete field definitions), which is a reasonable hint, but it does not specify the format or structure of the returned definitions. For a simple describe operation with one fully documented parameter, this is an adequate but not rich description, so a 3 is justified.

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 schema has 100% coverage for the one parameter, dataset_id, with a helpful description (e.g., '來自 twse_search_datasets 的資料集代號'). The tool description itself does not add parameter-level detail beyond what the schema provides, 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's function: '查看某個資料集的完整欄位定義' (view the complete field definitions of a dataset). It uses a specific verb (view) and resource (field definitions), and the context '取資料前' (before fetching data) distinguishes it from the sibling tool twse_get_dataset that likely fetches data.

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 clear usage context: '取資料前用來確認要過濾／投影哪些欄位' (before fetching data, use it to confirm which fields to filter/project). It implies using this tool before twse_get_dataset, but does not explicitly name alternatives or state when not to use it, so it falls just short of a 5.

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 provided, the description carries the full burden. It discloses that the tool merges three TWSE datasets and queries in parallel, and explains the failure behavior: '任何一段查不到都會標成 null 並記在 caveats，不會整個失敗' (any missing segment is marked as null and recorded in caveats, not causing overall failure). This adds significant behavioral context beyond the schema.

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 three sentences long, front-loaded with the main purpose, then a key caveat, then behavioral details. Every sentence contributes distinct information without redundancy, making it appropriately concise and well-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?

Given the absence of an output schema and annotations, the description covers essential context: the high-level output contents, data freshness disclaimer, parallel querying, and error handling with nulls and caveats. This is sufficient for an agent to understand what to expect and when to use the tool, making it complete for its 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?

The input schema already provides 100% coverage for both parameters: 'code' includes examples and 'include_realtime' explains its default and extra call. The tool description itself adds no additional parameter details, so the baseline of 3 applies as per the rubric.

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's function: '一次取得單一上市 ETF 的完整概況' (obtain a complete overview of a single listed ETF), and lists the exact components (basic info, previous trading day price/volume, periodic investment popularity). It also distinguishes itself from a sibling tool by explicitly directing users to twse_realtime_quote for real-time prices.

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 provides explicit when-not-to-use guidance: '價量為前一交易日，不是盤中即時；要當下價格請用 twse_realtime_quote' (price/volume is from previous trading day, not real-time; use twse_realtime_quote for current price). This names the alternative tool and clarifies the scope, satisfying the requirement for explicit usage direction.

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 provided, the description carries the transparency burden. It discloses a key behavioral trait: every dataset returns the whole batch at once, potentially tens of thousands of records, and advises filtering. This is significant context beyond the schema. It doesn't mention output ordering or the hard 200-row limit, but that lives in the parameter schema, and the description's disclosure is valuable.

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 concise sentences. The first states purpose and features; the second delivers a critical usage warning. No fluff, front-loaded with the most important 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 6-parameter tool with no output schema and no annotations, the description covers the essential context: purpose, filtering capabilities, and a critical large-data behavior. The per-parameter schema descriptions handle the rest. It could mention return format or ordering, but the description is robust enough for agent use, especially with rich sibling tool names providing context.

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% with detailed parameter descriptions for each field. The tool description only collectively mentions code/match/fields as scope-reducing parameters, which adds a usage hint but does not add meaning beyond what the schema already provides. Baseline 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 retrieves dataset content from TWSE/TPEx with server-side filtering, field projection, and pagination. The verb '取得' and resource '資料集內容' are specific, and the feature list distinguishes it from sibling tools like twse_describe_dataset or twse_search_datasets.

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 provides clear context for when to use the tool: when fetching dataset content, and includes a strong directive to use code/match/fields to narrow scope because datasets return in full (up to tens of thousands of rows). It doesn't explicitly contrast with alternatives, but it implies this is the data-fetching tool versus metadata/search tools.

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 discloses key behaviors: ~5-second update interval and the distinction between real-time and previous-day data. It doesn't mention return fields or errors, but for a simple read-only quote tool, this is adequate context.

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 exactly two sentences, front-loaded with the main purpose, and each sentence contributes distinct value: the first states function and update frequency, the second covers data source comparison and parameter guidance. No fluff.

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 tool with only 2 parameters and no output schema, the description covers purpose, usage context, and parameter rules. It doesn't describe the response format, which is a minor omission but not critical for a quote-fetching tool with clear intent.

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 descriptions already cover both parameters at 100%. The description adds practical semantics beyond the schema by specifying that market should be 'tse' for ETFs and listed stocks, and 'otc' for OTC, enriching the enum's meaning.

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 it obtains real-time intraday quotes with a ~5-second refresh, explicitly distinguishing from OpenAPI's previous-day data. This is unique among sibling tools, which focus on dataset access, not live 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 gives explicit when-to-use guidance: use this tool when you need current prices, since OpenAPI only has prior trading day data. It also provides concrete market-selection rules for ETFs/listed stocks (tse) vs OTC (otc), covering the main usage decision.

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 of behavioral disclosure. It explains the search matching behavior (against dataset codes, Chinese descriptions, and field names) and the taifex/ prefix convention for futures exchange datasets. This adds meaningful context beyond just 'search', though it doesn't mention return format 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?

The description is three concise sentences in Chinese, front-loaded with the core purpose, followed by a usage instruction and a useful domain hint. Every sentence adds value and there is no redundant or filler wording.

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 tool has 3 optional params, no annotations, and no output schema. The description establishes the tool's role in the workflow (finding dataset_id before fetching), its search scope, and a naming convention. It could mention the return structure, but the purpose implicitly promises dataset IDs in the results, making it sufficiently complete for an AI agent.

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. The description enriches the parameter semantics by explaining that query matches dataset codes, Chinese descriptions, and field names, and by providing a specific tag example for futures/options. This goes beyond the schema's brief parameter descriptions.

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's purpose: searching available datasets from Taiwan Stock Exchange and Futures Exchange OpenAPI, specifically to find dataset_id before retrieving data. It distinguishes itself from siblings like twse_get_dataset (which fetches data) and twse_describe_dataset (which describes a dataset).

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?

Provides explicit usage context: '取資料前先用這個找 dataset_id' (use this before fetching data to find dataset_id), and gives a concrete search hint for futures/options using tag='期貨與選擇權'. It does not explicitly name alternative tools or state when not to use it, but the workflow guidance is clear and actionable.

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