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 'Get[s] company information and key metrics,' implying a read-only operation, but doesn't disclose any behavioral traits like rate limits, authentication needs, or what specific metrics are returned. This is a significant gap for a tool with no annotations.

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, efficient sentence with no wasted words, making it appropriately sized and front-loaded. It directly states the tool's purpose without unnecessary elaboration.

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 complexity (financial data retrieval), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'company information and key metrics' includes, how results are formatted, or any limitations, leaving gaps for an AI agent to understand the tool fully.

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 has 100% description coverage, with the 'symbol' parameter clearly documented. The description doesn't add any meaning beyond what the schema provides, such as examples or constraints, so it meets the baseline score of 3 for high schema coverage without extra value.

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 ('Get') and resource ('company information and key metrics'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_stock_price' or 'get_daily_time_series', which might provide overlapping or related financial data, so it falls short of a perfect score.

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 any context, exclusions, or comparisons to sibling tools such as 'get_stock_price' or 'get_technical_indicator', leaving the agent without clear usage 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 full burden of behavioral disclosure. 'Get cryptocurrency prices' implies a read-only operation but doesn't specify rate limits, data sources, error handling, or output format. For a tool with no annotations, this is inadequate as it lacks critical behavioral details.

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, efficient sentence with zero waste. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration. Every word earns its place, making it highly concise.

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 complexity of a price-fetching tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the return values are (e.g., price, timestamp, volume), potential errors, or data freshness. This leaves significant gaps for an agent to understand the tool fully.

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 has 100% description coverage, clearly documenting both parameters (symbol and market). The description adds no additional meaning beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.

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 'Get cryptocurrency prices' clearly states the verb ('Get') and resource ('cryptocurrency prices'), making the purpose immediately understandable. It distinguishes this from sibling tools like get_stock_price and get_forex_rate by specifying cryptocurrency. However, it doesn't specify whether this retrieves current prices, historical data, or other details, which prevents a perfect score.

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_price or get_forex_rate, nor does it specify contexts or exclusions (e.g., for real-time vs. historical data). This leaves the agent with minimal 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 full burden of behavioral disclosure. It states what the tool does but lacks critical details such as data freshness, rate limits, authentication requirements, or error handling. For a data-fetching tool, this omission leaves significant gaps in understanding its operational behavior.

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, clear sentence with zero wasted words. It is front-loaded with the core purpose and efficiently communicates the essential action without unnecessary elaboration, making it highly 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 complexity of fetching financial time series data, the description is incomplete. With no annotations and no output schema, it fails to address key aspects like return format (e.g., JSON structure, date ranges), data limitations, or error scenarios. This leaves the agent under-informed for effective tool invocation.

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 has 100% description coverage, clearly documenting both parameters ('symbol' and 'outputsize') with details like enum values and defaults. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline for adequate but not enhanced 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 verb ('Get') and resource ('daily time series data for a stock'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_stock_price' or 'get_weekly_time_series', which would require more specificity about what makes daily time series data unique.

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_price' for current prices or 'get_weekly_time_series' for weekly data, leaving the agent to infer usage context without explicit 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does ('Get exchange rate') without mentioning any behavioral traits such as data freshness, rate limits, authentication needs, error handling, or whether it's a read-only operation. For a tool with no annotation coverage, this is a significant gap in transparency.

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, efficient sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded and appropriately sized for a simple tool, making it easy for an agent 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?

Given the lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects like data sources, accuracy, or return format, which are important for a financial data tool. While the schema covers parameters well, the overall context for proper tool invocation is insufficient.

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 has 100% description coverage, with clear parameter descriptions (e.g., 'From currency (e.g., USD)'). The description adds no additional semantic information beyond what the schema provides, such as format details or examples. Given the high schema coverage, a baseline score of 3 is appropriate, as the schema does the heavy lifting.

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 as 'Get exchange rate for currency pairs', which specifies the verb 'Get' and the resource 'exchange rate'. It distinguishes this tool from its siblings (which focus on stocks, crypto, time series, etc.) by being specifically about forex rates. However, it doesn't explicitly mention the scope or limitations, keeping it from a perfect score.

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 any prerequisites, constraints, or comparisons with sibling tools (e.g., when to use get_forex_rate vs. get_crypto_price for currency-related queries). This lack of contextual guidance leaves the agent without clear usage 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 full burden of behavioral disclosure. It mentions 'real-time' stock price information, which adds some context about data freshness, but fails to address critical aspects such as rate limits, authentication needs, error handling, or data source reliability. This leaves significant gaps for a tool that likely involves external API calls.

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, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core purpose, 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?

Given the lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral traits like rate limits or error responses, and with siblings offering related financial data, more context on differentiation is needed. For a tool that fetches real-time data, this minimal description leaves too many operational questions unanswered.

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 has 100% description coverage, with the 'symbol' parameter clearly documented. The description doesn't add any semantic details beyond what the schema provides (e.g., it doesn't explain formatting constraints or provide examples beyond the schema's 'e.g., AAPL'). Baseline 3 is appropriate since the schema does the heavy lifting.

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 action ('Get') and resource ('real-time stock price information'), making the purpose understandable. However, it doesn't explicitly differentiate from siblings like get_daily_time_series or get_company_overview, which could also provide stock-related data, so it falls short of a perfect score.

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. With siblings like get_daily_time_series (for historical data) and get_crypto_price (for cryptocurrencies), the agent lacks explicit direction on selecting this tool for real-time stock prices over other options.

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 what the tool does but doesn't cover aspects like rate limits, authentication needs, error handling, or response format, which are critical for a 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 a single, efficient sentence that directly states the tool's function without any wasted words. It's appropriately sized and front-loaded, making it easy to understand at a glance.

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 complexity of fetching technical indicators and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, potential data formats, or any behavioral traits, leaving significant gaps 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?

The input schema has 100% description coverage, clearly documenting all parameters with examples and enums. The description doesn't add any extra meaning beyond this, such as explaining indicator types or interval implications, so it meets the baseline for high schema 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 the action ('Get') and resource ('technical indicators for a stock'), making the purpose understandable. However, it doesn't differentiate from sibling tools like get_stock_price or get_daily_time_series, which might also provide related financial data, so it's not fully specific.

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, such as get_stock_price for price data or get_daily_time_series for time series data. It lacks explicit context or exclusions, leaving usage ambiguous.

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 what the tool does without mentioning any behavioral traits such as rate limits, authentication needs, data freshness, or error handling. This is inadequate for a tool with no annotation coverage.

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, efficient sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded and appropriately sized for its simple function, making it highly 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 lack of annotations and output schema, the description is incomplete. It does not explain what the time series data includes (e.g., open, close, volume), the time range covered, or the return format. For a data retrieval tool with no structured output information, this leaves significant gaps in understanding.

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 has 100% description coverage, with the 'symbol' parameter well-documented. The description does not add any additional meaning beyond what the schema provides, such as format constraints or examples beyond 'AAPL'. Baseline 3 is appropriate since the schema handles the parameter documentation effectively.

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 verb 'Get' and the resource 'weekly time series data for a stock', making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'get_daily_time_series' or 'get_stock_price', which could cause confusion about when to use this specific tool versus others.

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. With sibling tools like 'get_daily_time_series' and 'get_stock_price' available, there is no indication of the specific context or scenarios where weekly time series data is preferred, leaving usage unclear.

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