Korean Stock Market Data

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

With no annotations provided, the description carries the behavioral burden. It usefully discloses the date handling behavior (empty string means most recent business day) and the return contents (closing price, change rate, volume, market cap). However, it does not mention read-only nature, rate limits, or any limitations beyond the given scope.

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 well-structured docstring: a one-line summary followed by Args and Returns sections. It is concise, front-loaded, and contains no filler—every sentence contributes useful 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?

Given the tool's moderate complexity, absence of annotations, and minimal schema, the description is complete. It covers purpose, both parameters with formats and defaults, and the return structure. The output schema exists, so further details about return fields are unnecessary.

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%, but the description fully compensates by explaining both parameters in detail. It specifies stock_names as comma-separated with a concrete example, and date as YYYY-MM-DD with the special empty-string meaning, adding significant value beyond the bare 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?

The description clearly states it compares stock quotes across multiple stocks ('여러 종목의 시세를 비교합니다'), which is a specific verb + resource. This distinguishes it from siblings like get_stock_price or search_stock by explicitly emphasizing multi-stock comparison.

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 implies usage for comparing multiple stocks but does not explicitly state when to use this tool versus alternatives or when not to use it. It lacks explicit exclusions or references to sibling tools like get_stock_price for single-stock queries.

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 full responsibility. It discloses the return fields (배당률, 배당금, 배당기준일, 배당지급일) and parameter behavior, such as empty year meaning 'most recent'. However, it does not explicitly state that this is a read-only operation or mention error conditions, but '조회' implies retrieval.

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 well-structured with clear Args and Returns sections. Every line adds value, providing examples and defaults without unnecessary filler. It is appropriately sized for the tool's complexity.

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 three parameters, no annotations, and an output schema that is not shown but described in Returns, the description covers all necessary aspects: inputs, defaults, and output fields. No major gaps exist for an agent to invoke this tool correctly.

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%, but the description provides detailed, actionable semantics for each parameter: company_name with examples, year with 'empty string means recent', and num_results with default and max values. This fully compensates for the lack of schema 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 '주식 배당 정보를 조회합니다' (retrieves stock dividend information), using a specific verb and resource. This distinguishes it from sibling tools like get_stock_price, get_market_index, and search_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?

The description implies when to use the tool by focusing on dividend data with parameters for company name and fiscal year. It does not explicitly mention alternatives or exclusions, but the context is clear enough for correct selection.

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. It discloses parameter behavior (empty strings fall back to all indices or recent business day, num_results default 20 and max 100) and lists return fields. Missing details include error handling, data freshness, or explicit read-only confirmation, but for a simple retrieval tool it provides substantial behavioral 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 compact and well-structured, with a one-line purpose statement followed by organized Args and Returns sections. Every sentence delivers necessary information without redundancy.

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 straightforward retrieval tool with an output schema, the description covers all invocation essentials: every parameter's meaning and defaults, result count limits, and return fields. It is complete for an agent to select and correctly invoke the 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?

The schema has zero description coverage, but the description fully compensates by explaining each parameter: index_name with examples and all-indices behavior, date format and default, and num_results default/max. This goes well beyond the bare schema definitions.

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 'KOSPI, KOSDAQ 등 주요 시장 지수를 조회합니다' (retrieves major market indices such as KOSPI, KOSDAQ), specifying the verb 'retrieve' and the resource 'market indices'. This distinguishes it from sibling tools that handle individual stocks, dividends, and price trends.

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 implies usage for market index data but does not explicitly mention when to use this tool versus alternatives or provide exclusion criteria. Sibling tools are stock-focused, making the context clear, but there is no direct comparative guidance.

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 burden. It discloses the return format (list of major stock names and 6-digit codes) but does not elaborate on any other behavioral traits such as pagination or sorting. For a simple read-only operation, this is adequate, but not rich.

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 two concise lines, immediately stating purpose and return format without any 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?

Given the tool's simplicity (no parameters, output schema present), the description fully covers purpose, usage context, and return value. It is complete for the tool's 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 tool takes no parameters, so the description has no parameter burden. The lack of parameters is fully clear from 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?

The description clearly states the function (retrieving a list of codes for major popular stocks) and includes a usage hint about when to reference it. This distinguishes it from siblings like search_stock, which searches for specific stocks.

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 advises using this tool when you do not know the stock code, providing a clear usage context. It does not explicitly exclude other tools, but the guidance is sufficient for a simple list tool.

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?

The description discloses key behavior: returns daily close, change rate, volume, period return; days limited to max 30; stock_name and stock_code are alternatives. However, it does not clarify behavior if both or neither are provided, or error handling. Since no annotations exist, the description carries this burden adequately but not exhaustively.

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?

Structured with a one-line purpose, then Args and Returns sections; every sentence adds information and avoids 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?

The description covers all parameters, return fields, and constraints (days default/max, input alternatives). It lacks explicit sibling differentiation and edge-case behavior, so it's slightly incomplete but sufficient for effective use.

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 has no descriptions (0% coverage); the description compensates fully: explains stock_name with examples, stock_code as 6-digit code, days with default and max, and specifies that either stock_name or stock_code is 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?

The description opens with '종목의 최근 N일간 주가 추이를 조회합니다' (queries the recent N-day stock price trend), which clearly states a specific verb and resource. The name and description align, and the trend focus separates it from single-price tools like get_stock_price.

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?

No guidance on when to use this tool versus alternatives; it does not mention comparison tools or single-price tools. The description only provides parameter details, not usage context or 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that data is based on previous day's close, that only one of stock_name/stock_code should be entered, and lists return fields. This is solid, though it lacks details like error handling 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 well-organized into a summary, Args list, and Returns list. Each sentence earns its place, providing essential details without extraneous content.

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 5 optional parameters and an output schema, the description covers all invocation aspects: search keys, date, market filter, result count, and return fields. It is complete for the tool's purpose, and the explicit output schema further covers return structure.

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 0% description coverage, so the description's detailed Args section compensates fully. It gives examples for stock names, explains the 6-digit code format, clarifies that only one of name/code should be used, and specifies defaults and constraints for date, market, and num_results.

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 that it retrieves Korean stock prices ('한국 주식 시세를 조회합니다') and can search by name or code. This specific verb-resource pair distinguishes it from sibling tools like get_price_trend (trends) and get_market_index (index).

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 guidance on how to use the tool (search by name/code, optional date and market) and notes that data is previous-day close, not real-time. However, it does not explicitly mention alternatives or when not to use this tool, so it falls 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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It transparently describes the search operation, parameter semantics (e.g., empty market means all, num_results default and max), and return fields. It does not mention potential edge cases like case sensitivity or error handling, but it is clear about the core behavior and scope, which is sufficient for a read-only search 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?

The description is well-structured with an overview, args, and returns sections. Each sentence is informative and contributes value—no fluff or redundancy. The bullet-like layout enhances readability and makes the essentials easy to scan.

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 search tool, the description covers all three parameters with defaults and constraints, lists return fields, and notes behavior like empty market meaning all. Since an output schema exists, return value details are already structured, and the description still provides a human-readable summary. It is complete enough for an agent to invoke the tool correctly.

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%, but the description fully compensates by providing detailed parameter explanations: 'keyword' with examples ('삼성', '바이오'), 'market' with allowed values (KOSPI, KOSDAQ, KONEX) and the meaning of empty string (all markets), and 'num_results' with default (20) and maximum (100). This adds significant meaning beyond the bare schema, making parameter usage clear.

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 explicitly states '종목명 키워드로 KRX 상장종목을 검색합니다' (Search KRX-listed stocks by stock name keyword), clearly identifying the action (search), resource (KRX-listed stocks), and scope (by keyword). This distinguishes it from sibling tools like get_stock_price or compare_stocks, focusing on keyword-based lookup rather than price or comparison.

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 implies usage for keyword-based stock search but does not explicitly state when to use it over alternatives or mention any exclusions. It documents the parameters and defaults, which implicitly guides usage (e.g., setting market to filter), but lacks direct comparison to sibling tools, so the guidance is only implied.

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