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

With no annotations provided, the description carries full burden and does well by specifying the return format (DataFrame), showing example output structure, and explaining parameter formats. However, it doesn't mention potential limitations like rate limits, authentication needs, or data availability constraints.

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 purpose statement, parameter documentation, return specification, and concrete example. Every sentence adds value - no redundant information. The example output is appropriately detailed to illustrate the DataFrame structure.

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 data retrieval tool with no annotations and no output schema, the description provides excellent context about parameters and return format. The example DataFrame shows exactly what to expect. Minor deduction because it doesn't address potential error conditions or data source limitations.

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?

Given 0% schema description coverage, the description compensates excellently by explaining all 4 parameters with clear semantics: date formats (YYYYMMDD), ticker examples (1001 for KOSPI), frequency options (d/m/y), and default values. The example call demonstrates proper parameter usage.

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 with specific verb ('Retrieves') and resource ('OHLCV data for a specific index'). It distinguishes from sibling tools like get_stock_ohlcv by specifying it's for indices rather than individual 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 implies usage context through parameter explanations and example, but doesn't explicitly state when to use this tool versus alternatives like get_stock_ohlcv. No explicit guidance on when-not-to-use or comparison with sibling tools is provided.

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 clearly indicates this is a read operation ('retrieves'), shows the return format with a detailed example, and implies date-range functionality. However, it doesn't disclose potential limitations like rate limits, authentication requirements, data freshness, or error conditions that would be important for an agent.

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 sections (purpose, Args, Returns, example). Every sentence earns its place, though the detailed example DataFrame takes significant space. The core information is front-loaded with the purpose statement first, making it easy to understand quickly.

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 read-only tool with 3 parameters and no output schema, the description provides substantial context. It clearly explains what data is returned (fundamental metrics), shows the exact return format with a realistic example, and documents all parameters. The main gap is lack of behavioral constraints that would normally come from annotations.

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 fully compensates by providing clear parameter documentation in the Args section. It explains what each parameter represents (start date, end date, ticker symbol), shows the expected format (YYYYMMDD), and provides a concrete usage example that demonstrates all three parameters in action.

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 fundamental data (PER/PBR/Dividend Yield) for a specific stock, providing a specific verb ('retrieves') and resource ('fundamental data'). It distinguishes from siblings like get_stock_market_cap or get_stock_ohlcv by specifying the type of financial data, though it doesn't explicitly contrast with them.

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 no guidance on when to use this tool versus alternatives like get_stock_market_cap or get_stock_ohlcv. It states what the tool does but offers no context about when it's appropriate or what problems it solves compared to sibling 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 provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves data (implying read-only) and shows an example return format, but doesn't mention rate limits, authentication requirements, data freshness, error conditions, or whether the date range is inclusive/exclusive. The example helps but leaves many behavioral aspects unspecified.

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 sections (Args, Returns) and uses an example effectively. It's appropriately sized for a 3-parameter tool with no annotations. The only minor inefficiency is repeating the tool name in the example call when it's already clear from context.

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 data retrieval tool with 3 parameters and no annotations, the description provides good coverage: clear purpose, full parameter documentation, and example output format. The main gap is lack of usage guidance relative to sibling tools. Without an output schema, the example return format is particularly valuable.

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 fully compensates by providing detailed parameter documentation. It clearly explains all three parameters (fromdate, todate, ticker) with their purposes, formats (YYYYMMDD for dates), and includes a concrete example showing valid values. This adds substantial 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's purpose with a specific verb ('Retrieves') and resource ('market capitalization data for a specific stock'), distinguishing it from siblings like get_stock_fundamental or get_stock_ohlcv which retrieve different types of financial 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like get_stock_fundamental or get_stock_ohlcv, nor does it explain what makes market capitalization data unique or when it's preferred over other financial metrics.

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 full burden. It clearly indicates this is a read operation ('Retrieves'), implies data retrieval from a source, and shows the return format with an example. However, it lacks details on rate limits, authentication needs, data freshness, or error conditions that would be important for an agent.

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 clear sections (purpose, args, returns, example). The example is detailed but necessary to show the return format. Slightly verbose due to the full example table, but each section adds value. Could be more front-loaded by moving the example after a brief return description.

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 4-parameter tool with no annotations and no output schema, the description does an excellent job explaining parameters and showing the return format through example. It covers the core functionality well but lacks context about data sources, limitations, or error handling that would make it fully complete.

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 provides comprehensive parameter documentation beyond the schema's 0% coverage. It explains each parameter's purpose, format requirements (YYYYMMDD for dates), and the adjusted parameter's meaning and default value. The example demonstrates proper usage with concrete values.

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 specific action ('Retrieves OHLCV data') and resource ('for a specific stock'), distinguishing it from siblings like get_stock_fundamental or get_stock_trading_volume. It precisely identifies the data type (Open/High/Low/Close/Volume) and target resource (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?

No guidance is provided on when to use this tool versus alternatives like get_index_ohlcv or get_stock_trading_volume. The description mentions only what the tool does, not when it's appropriate relative to sibling tools or any prerequisites for use.

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 describes the retrieval action and output format (DataFrame with specific columns), but omits details like rate limits, authentication needs, error handling, or data freshness. It adds some context (e.g., breakdown by investor types) but lacks comprehensive behavioral traits.

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 and appropriately sized, with a clear purpose statement followed by parameter and return details. Every sentence adds value, though it could be slightly more front-loaded by emphasizing the investor type breakdown earlier. No wasted text, but minor room for optimization in flow.

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 (3 parameters, no output schema, no annotations), the description is largely complete. It covers purpose, parameters, and return format in detail. However, it lacks information on behavioral aspects like error cases or data limitations, which would enhance completeness for a tool with no annotations.

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 meaning beyond the input schema, which has 0% description coverage. It explicitly defines each parameter (fromdate, todate, ticker) with formats (YYYYMMDD for dates, ticker symbol) and clarifies their roles in date range and stock selection, fully compensating for the schema's lack of documentation.

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 with a specific verb ('Retrieves') and resource ('trading volume by investor type for a specific stock'), distinguishing it from siblings like get_stock_ohlcv (price data) or get_stock_fundamental (financial metrics). It precisely identifies what data is fetched and how it's categorized.

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 obtaining trading volume breakdowns by investor type, but lacks explicit guidance on when to use this tool versus alternatives like get_stock_ohlcv (which might include volume without investor breakdown) or other siblings. No exclusions or prerequisites are mentioned, leaving context somewhat open-ended.

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 describes the operation ('loads into memory') and return format, but lacks details on performance characteristics (e.g., loading time, memory usage), error handling, or data freshness. The description adds basic context but misses deeper behavioral traits.

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 efficiently structured with two sentences: one stating the purpose and scope, and another detailing the return format with a clear example. Every sentence adds essential value without redundancy, making it easy to parse and understand quickly.

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 (0 parameters, no output schema, no annotations), the description is reasonably complete. It covers purpose, scope, and return format with an example. However, it could benefit from additional context like data source or update frequency to fully compensate for the lack of annotations and output schema.

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 zero parameters with 100% schema description coverage, so the schema already fully documents the input structure. The description appropriately doesn't add parameter details, maintaining focus on the tool's purpose and output. Baseline 4 is applied as per rules for zero-parameter tools.

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 specific action ('Loads all ticker symbols and names') and resource ('for KOSPI and KOSDAQ into memory'), distinguishing it from sibling tools that focus on specific data like OHLCV, fundamentals, or market cap. It explicitly defines the scope as comprehensive ticker loading rather than filtered queries.

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 context by specifying it loads 'all' tickers for KOSPI and KOSDAQ, suggesting it should be used when a complete reference dataset is needed. However, it doesn't explicitly state when to use alternatives like sibling tools or provide exclusion criteria, leaving some ambiguity about optimal use cases.

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