Korean Stock Market Data

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

No annotations are provided, so the description bears full burden. It mentions that empty date uses latest business day, but does not disclose limitations (e.g., max stocks), data freshness, permissions, or side effects. As a read tool, safety is implied but not explicitly stated.

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 concise with a clear one-sentence summary followed by Args and Returns sections. No unnecessary information, though the Returns section could be integrated into the summary.

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 output schema exists, the description adequately covers parameters and basic return fields. However, it lacks context like sorting, data source, or limits on number of stocks. Adequate but not exhaustive.

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 description adds full semantics for both parameters: stock_names specifies comma-separated names with examples, date explains format and behavior of empty string. This compensates for the schema's 0% description coverage.

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 prices of multiple stocks and lists the comparison fields (close, change rate, volume, market cap) in Returns. This distinguishes it from single-stock tools like get_stock_price, but could be more explicit about the unique comparison aspect.

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 lacks explicit guidance on when to use this tool over siblings like get_stock_price or get_price_trend. No when-not-to-use or alternative mentions.

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 provides the behavioral clue '조회' (query), indicating a read operation. It lists return fields but does not discuss side effects, authentication requirements, rate limits, or idempotency beyond inference.

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 concise, using a clear docstring format with Args and Returns sections. Every sentence provides value, and there is no extraneous 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 presence of an output schema (unshown), the description covers the essential input parameters and lists return fields. It explains defaults but does not mention sorting or pagination behavior, though num_results limits the count. Overall adequate for a simple query 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?

Since schema description coverage is 0%, the description fully compensates by explaining each parameter: company_name with example, year as fiscal year with empty string meaning recent, num_results with default and max. This adds significant meaning 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 the tool retrieves dividend information for a specific company and year. It uses a specific verb-resource combination ('주식 배당 정보를 조회'), but does not explicitly differentiate from sibling tools beyond the subject matter.

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 parameter details and defaults, implying usage context. However, it lacks explicit guidance on when to use this tool versus siblings (e.g., when only dividend data is needed), and no when-not-to or alternative suggestions.

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 fully convey behavioral traits. It mentions return fields but does not disclose any side effects, auth requirements, rate limits, or that it is read-only.

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 reasonably concise with a clear structure: a purpose sentence followed by an Args list and a Returns note. Every sentence adds value, though it could be slightly more compact.

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 presence of an output schema, the description provides adequate return field context. However, it lacks details on error handling, pagination, or edge cases like empty results.

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 0% schema description coverage, the description compensates by detailing each parameter: index_name with examples, date format, and num_results with default/max. This adds significant value 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?

The description clearly states the tool retrieves major market indices (KOSPI, KOSDAQ) with a specific verb '조회합니다' (retrieves/queries). It distinguishes from siblings that focus on individual stocks or dividends.

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 specifies the tool is for market indices, implicitly separating it from stock-specific sibling tools. However, it lacks explicit guidance on when not to use or direct alternatives.

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, so description carries full burden. It states the return format (names and 6-digit codes) but does not disclose aspects like data freshness, ordering, or any side effects. Adequate for a simple read 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 succinct sentences: first states the action, second describes the return value. No wasted words, front-loaded with purpose.

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 parameterless tool with an output schema, the description sufficiently covers what the tool returns (major stock names and codes). No additional info is necessary.

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 has 0 parameters and schema coverage is 100%, so no parameter explanation needed. Baseline score of 4 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?

Clearly states it retrieves a list of major popular stock codes. The purpose is distinct from sibling tools like search_stock, which suggests searching-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?

Explicitly advises referencing when the user doesn't know the stock code, providing clear usage context. It doesn't specify when not to use, but the guidance is sufficient.

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 fully informs about return values (daily close, change rate, volume, period return) and tool behavior (querying recent N-day trend). It does not mention rate limits or authentication, but the read-only nature is implied.

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 Args and Returns sections. It is slightly verbose but all information is relevant. No wasted words, though it could be more concise by removing 'Args:' and 'Returns:' labels if not needed.

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 has an output schema (not shown but present), the description covers the essential behavioral aspects and return values. It lacks edge-case instructions (e.g., if both stock_name and stock_code provided) but is complete for typical usage.

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 description adds significant semantic value beyond the input schema (which has 0% schema coverage). It explains stock_name as '종목명 (예: 삼성전자, NAVER)', stock_code as '6자리 단축코드', and days with default and max, plus the mutual exclusivity of stock_name and stock_code.

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?

Description clearly states the tool retrieves '주가 추이' (price trend) for a stock over recent N days. The verb '조회' (query) and resource '종목의 최근 N일간 주가 추이' are specific and distinguish it from sibling tools like get_stock_price (single price) and compare_stocks (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 explains that either stock_name or stock_code can be used, and days has a default and max. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide conditions like 'use for trend analysis, not for single quotes'.

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 provided, but the description discloses that data is based on previous close (not real-time), and lists return fields such as close, open, high, low, volume, etc. This is adequate behavioral context for a read-only query 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?

Well-structured with Args and Returns sections, moderate length. Could be slightly more concise, but every sentence provides value. No front-loading issues.

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 5 parameters (none required) and an output schema (existence noted but not detailed), the description covers key return fields and parameter constraints. It is sufficient for effective tool 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 0% coverage (no descriptions), but the description fully compensates by explaining each parameter: examples for stock_name and stock_code, date format YYYY-MM-DD, market options, and num_results default and max. This adds essential 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 retrieves Korean stock prices ('한국 주식 시세를 조회합니다') and specifies search by name or code. It distinguishes from sibling tools like compare_stocks, get_dividend_info, etc., which cover different functionalities.

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 guidelines on searching by name or code (only one needed), default values, and data being previous close. However, it lacks explicit when-to-use vs alternatives, no mention of prerequisites or limitations beyond real-time data.

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 full burden. It does not disclose behavioral traits such as rate limits, pagination behavior, error handling, or idempotency. Only default values and max results for num_results are noted, which is insufficient.

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 concise with one sentence for the purpose, followed by structured Args and Returns sections. No redundant information, but the parameter explanations could be slightly more compact.

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 purpose, parameters, and return fields. However, it lacks examples, error scenarios, sorting/ordering, and differentiation from sibling tools. With no output schema, the return description is helpful but basic.

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 adds meaningful context for all three parameters: keyword (search key), market (with allowed values), and num_results (default 20, max 100). This compensates well for the missing 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 the tool searches KRX listed stocks by keyword. It specifies the resource (KRX listed stocks) and the method (keyword search), which distinguishes it from siblings like get_stock_price or compare_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?

No explicit guidance on when to use this tool versus siblings. The description only explains parameters and returns, lacking context for when this search is appropriate compared to other tools like get_popular_stocks or get_stock_price.

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