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

With no annotations, the description carries the burden. It does state the data source (Yahoo Finance) and the allowed statement types, but it does not explicitly mention that this is a read-only operation, potential limitations, or any network/auth requirements. The 'get' verb implies safety, but the description adds limited behavioral insight beyond that.

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 clear and front-loaded, but the list of financial statement types is repeated verbatim in the first sentence and in the Args section, which is redundant. This wastes space and could be tightened without losing 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?

The tool is simple (2 parameters, no nested objects) and has an output schema, so return format is covered. The description provides the source, allowed values, and parameter explanations, which is sufficient for an agent to invoke it. Minor gaps like error handling or data format are not critical here.

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 0%, so the description fully compensates. It explains the ticker parameter with an example ('AAPL') and lists all valid values for financial_type, adding essential meaning beyond the bare string 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 fetches a financial statement for a ticker from Yahoo Finance, using a specific verb and resource. It distinguishes itself from siblings by listing specific statement types (income, balance, cashflow), which are unique among the sibling tools.

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 implied by the description and the tool name, but there is no explicit guidance on when to choose this over alternatives, nor any when-not-to-use conditions. The description lists allowed statement types, which helps, but no exclusions or alternative tool references are 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 behavioral disclosure burden. It discloses the output format (specific columns), the valid parameter values, and a key constraint (intraday data cannot extend last 60 days). The mention of 'start and end' not present in the schema is a minor transparency gap but does not materially mislead.

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 a front-loaded purpose sentence followed by a clear 'Args' block. The parameter enumerations are necessary and efficiently formatted. The 'Either Use period parameter or use start and end' line is slightly confusing given the schema lacks start/end, but overall there is no wasted text.

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 3-parameter tool with an output schema, the description provides thorough input semantics, defaults, and constraints. The existence of an output schema means return values need not be detailed further. The only notable gap is the ambiguous start/end mention, which does not significantly undermine completeness.

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 only describes the ticker parameter, leaving period and interval as bare strings. The description adds substantial meaning by listing all valid periods and intervals, defaults, and the interaction between period and start/end (even if start/end are not in the schema). This far exceeds the schema's 33% coverage and is essential for correct invocation.

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 gets historical stock prices for a ticker from Yahoo Finance, enumerates the exact columns returned (Date, Open, High, Low, Close, Volume, Adj Close), and distinguishes it from sibling tools like get_stock_info (current info) or get_stock_actions (corporate actions). The verb+resource+scope is specific and unambiguous.

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 detailed parameter usage guidance (valid periods, intervals, default values, intraday 60-day limit) but does not explicitly state when to choose this tool over alternatives like get_stock_info or get_financial_statement. Usage is implied by the clear purpose but lacks explicit when/when-not instructions.

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 data source (Yahoo Finance) and the available holder types, but does not mention any behavioral traits such as potential errors, rate limits, data availability, or whether the operation is read-only. The absence of caveats is a minor gap for a simple data-fetching 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 structured well with a leading purpose statement followed by an Args block. However, the full list of holder types is repeated twice (once in the opening sentence and once in the Args section), which is slightly redundant. Otherwise, it is concise and clear.

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 is a simple two-parameter data-fetcher, and an output schema exists to describe return values, so the description need not cover that. It effectively explains what data can be retrieved and the allowed parameters. It lacks any note about error conditions or prerequisites, but for this level of complexity the description is adequately 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 schema provides only bare string types with no descriptions (0% coverage), so the description's Args section compensates fully. It explains ticker with an example ("AAPL") and explicitly enumerates all valid values for holder_type, which the schema omits. This adds significant meaning 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's function: "Get holder information for a given ticker symbol from yahoo finance," which specifies the verb (get), resource (holder information), and input (ticker). It also lists the specific holder types, distinguishing it from sibling tools that handle prices, news, financials, or options.

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 (when you need holder information for a ticker) but does not explicitly mention alternatives or exclusions. There is no guidance on when not to use it or how it compares to sibling tools, so usage context is only inferred.

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 of behavioral disclosure. It only states the action 'Fetch' which implies a read-only operation, but it does not mention any special requirements, limitations, rate limits, or error conditions. The description adds little beyond what the input schema already provides.

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 and well-structured: a one-sentence summary followed by a clear list of args. Every sentence earns its place, and the structure makes it easy for an agent to quickly parse the tool's purpose and required inputs.

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 simple parameter set, complete schema descriptions, and the presence of an output schema, the description is largely sufficient for correct selection and invocation. However, it lacks any guidance on usage relative to sibling tools or any caveats about expiration date validity, preventing a perfect score.

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 fully documents all parameters. The description repeats the parameter meanings without adding extra context such as examples for the date format or clarifying option_type values. It meets the baseline for schema-heavy coverage but does not enhance understanding 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 starts with a specific verb 'Fetch' and clearly identifies the resource (option chain) with the key parameters (ticker, expiration date, option type). It distinguishes itself from sibling tools like get_option_expiration_dates, which deal with expiration dates rather than the chain itself.

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 (when you need an option chain for a specific ticker and expiration) but does not explicitly state when to use this tool versus alternatives, nor does it mention any prerequisites such as first fetching expiration dates via get_option_expiration_dates. This leaves the agent to infer usage from the purpose.

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 for behavioral disclosure. The word 'Fetch' clearly signals a read-only operation, which is a critical safety trait. However, it does not disclose any additional behaviors (e.g., data freshness, error handling, rate limits), leaving some depth missing.

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 and front-loads the purpose. The Args section is structured and efficient, with no redundant information. Every sentence 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 simple one-parameter fetch tool with an output schema, the description covers the essential aspects: what the tool does and what the parameter means. The return format is not described, but the existence of an output schema makes that 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?

The schema has 0% description coverage, but the description compensates with an Args section that explains the 'ticker' parameter fully, including its meaning and an example ('AAPL'). This adds significant value beyond the raw string type in 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 uses a specific verb 'Fetch' and resource 'options expiration dates' for a given ticker, clearly distinguishing it from sibling tools like get_option_chain (which likely returns full option chains) and get_historical_stock_prices. The purpose is unambiguous.

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 when option expiration dates are needed, but it provides no explicit alternatives or exclusions. It doesn't mention when to prefer this over get_option_chain or other related tools, so the guidance is only implicit.

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 the default for months_back and the two recommendation types, which is useful. However, it does not mention read-only status, rate limits, or error handling, leaving behavioral transparency partial.

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: a concise introductory sentence followed by an Args block. While redundant in repeating parameter names from the schema, the added explanations are essential given zero schema coverage. No unnecessary filler is present.

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 an output schema, so return values need no description. Input parameters are thoroughly covered. The only gap is ambiguity about whether months_back applies to both recommendation types or only upgrades_downgrades, but overall the tool is well specified.

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%, so the Args section fully compensates. Each parameter is explained with purpose and examples ('AAPL' for ticker), allowed values for recommendation_type are listed, and months_back has its default clarified.

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 fetches recommendations or upgrades/downgrades for a given ticker from Yahoo Finance, using the specific verb 'Get' and naming the resource. It explicitly lists two recommendation types, distinguishing the tool from siblings like get_stock_info or get_stock_actions.

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 whenever recommendation data is needed, but does not explicitly contrast with sibling tools or state when not to use it. It provides parameter-level guidance (e.g., months_back default) but lacks explicit 'use this when' or alternative tool recommendations.

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 discloses the operation type (getting dividends/splits) and the data source (Yahoo Finance). However, it does not mention return format, error handling, or any side effects, though the simple read-only nature is implied by 'get'.

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 compact purpose statement followed by a clear docstring for the argument. Every sentence earns its place, and the format is highly scannable.

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-parameter tool with an output schema available, the description fully covers what the tool does and how to invoke it. It doesn't need to explain return values because the output schema handles that.

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 0%, but the description explicitly defines ticker as the stock symbol and provides an example 'AAPL'. This adds meaningful semantic detail well beyond the schema's bare string type.

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 gets stock dividends and splits for a given ticker from Yahoo Finance. This is distinct from sibling tools that fetch prices, info, news, financial statements, holders, options, or recommendations.

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 indicates this tool should be used when needing dividend or split data for a stock ticker. It does not explicitly name alternative tools or exclusions, but the purpose and context are clear enough to guide 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the data categories returned; it does not mention error handling for invalid tickers, rate limits, data freshness, or any other behavioral traits. This is a significant gap for a tool with no annotation support.

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 lead sentence and a bullet-like list of information categories. The list is long but informative, and the Args section is clearly separated. It earns its length, though 'Other' is a slightly vague inclusion.

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?

An output schema exists (though not shown), so return structure is presumably covered. The description adequately explains the input and general content, but lacks usage context and behavior beyond listing categories. For a simple one-parameter tool, it is minimally viable but not rich enough for full autonomy.

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 coverage is 0%, so the description must compensate. The Args section explicitly defines 'ticker' as 'The ticker symbol of the stock to get information for, e.g., "AAPL"', adding clear meaning and a concrete example beyond the bare string type.

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 'Get stock information for a given ticker symbol from yahoo finance,' which specifies a verb, resource, and input. The long list of categories (e.g., 'Stock Price & Trading Info', 'Financial Metrics', 'Dividends') distinguishes it from specialized sibling tools like get_historical_stock_prices or get_yahoo_finance_news.

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 guidance on when to use this tool versus its siblings. The description lists what data is returned but does not say 'for a comprehensive overview use this' or 'for historical data use get_historical_stock_prices.' No exclusions or alternatives are mentioned.

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 of behavioral disclosure. It only states that it gets news, but does not mention return format, potential limitations (e.g., only recent news), pagination, or whether this is a read-only operation. The description adds minimal behavioral context beyond the basic action.

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 extremely concise, with one clear purpose sentence and parameter documentation. It is front-loaded with the core action and has no wasted words, making it easy to parse 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?

The tool is simple with a single parameter and includes an output schema, so the description does not need to explain return values in detail. It provides sufficient context for a straightforward news retrieval tool, though it could have added minor usage context such as the type of news returned or any time range constraints.

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 description repeats the parameter details already present in the schema. It does not add any new meaning or examples beyond what the schema provides. The baseline of 3 is appropriate since the schema fully documents the parameter.

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 the tool retrieves news for a given ticker symbol from Yahoo Finance. It uses a specific verb ('Get') and resource ('news for a ticker'), and it is distinct from sibling tools which focus on prices, info, actions, financial statements, holders, options, and recommendations.

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 the tool should be used when news about a stock is needed, but it does not explicitly discuss alternatives or when not to use it. There is no mention of sibling tools like get_historical_stock_prices or get_recommendations to differentiate usage scenarios.

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