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

With no annotations provided, the description carries full burden but offers minimal behavioral context. It implies this is a read operation ('Get'), but doesn't disclose permissions needed, rate limits, error conditions, or what happens when no data exists for the date. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 appropriately brief with two focused sentences. The first states the purpose and data structure, the second documents the parameter. There's no wasted text, though the structure could be slightly improved by integrating the parameter documentation more seamlessly rather than as a separate 'Args:' section.

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 no annotations, no output schema, and 0% schema description coverage, the description is insufficient. It doesn't explain the return format (structure of cardio/strength data), error handling, authentication requirements, or how it differs from summary tools. The agent would struggle to use this effectively without additional context.

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 description must compensate. It documents the single parameter's purpose (date filtering) and format (YYYY-MM-DD), and mentions the default behavior (today). However, it doesn't explain what 'null' means for the date parameter or how the default is applied, leaving some semantic gaps despite covering the basic parameter purpose.

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 exercise activities with specific data fields (cardio duration/calories, strength sets/reps/weight). It distinguishes from siblings like get_daily_macros or get_daily_meals by focusing on exercise rather than nutrition. However, it doesn't explicitly contrast with get_daily_summary which might also include exercise 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 like get_daily_summary or get_date_range_summary. It mentions the date parameter defaults to today, but offers no context about appropriate use cases, prerequisites, or exclusions 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get'), implying a read-only operation, but doesn't mention permissions, rate limits, data freshness, or error handling. For a tool with no annotations, this is insufficient to inform safe and effective use.

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 appropriately sized and front-loaded, with the main purpose stated first and parameter details in a separate 'Args' section. It avoids unnecessary words, but the structure could be slightly more integrated (e.g., merging the purpose and args more seamlessly). Overall, it's efficient with minimal waste.

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 low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the purpose and parameter semantics well but lacks usage guidelines and behavioral details. Without an output schema, it doesn't explain return values, which is a gap, but the simplicity of the tool keeps it from being severely incomplete.

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 meaningful semantics beyond the input schema. The schema has 0% description coverage and only defines a 'date' parameter with a default of null. The description clarifies that 'date' is in 'YYYY-MM-DD format' and 'defaults to today', providing crucial context that the schema lacks. Since there's only one parameter, this compensation is effective.

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: 'Get comprehensive macro and micronutrient breakdown with all tracked nutrients.' It specifies the verb ('Get') and resource ('macro and micronutrient breakdown'), making the function evident. However, it doesn't explicitly differentiate from sibling tools like 'get_daily_summary' or 'get_daily_meals', which might overlap in scope, so it doesn't reach a score of 5.

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 mentions the date parameter but doesn't explain why one would choose this over siblings like 'get_daily_summary' or 'get_daily_meals', nor does it specify prerequisites or exclusions. This lack of context leaves usage unclear.

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 states the tool retrieves data ('Get'), implying a read-only operation, but doesn't clarify permissions, rate limits, error handling, or response format. The description adds minimal behavioral context beyond the basic action, which is insufficient 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 appropriately sized and front-loaded, with the core purpose stated in the first sentence and parameter details in a clear 'Args:' section. Every sentence adds value: the first defines the tool's output, and the second explains the parameter. There's no redundancy or unnecessary 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 low complexity (1 parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the parameter semantics well but lacks usage guidelines and behavioral details like response structure or error cases. For a simple read tool, this is adequate but not fully comprehensive.

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 explains the 'date' parameter's purpose ('Date in YYYY-MM-DD format') and default behavior ('defaults to today'), compensating for the schema's lack of documentation. Since there's only one parameter, this effectively covers all inputs, making it highly valuable.

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: 'Get detailed meal-by-meal breakdown with all foods, servings, and calories.' This specifies the verb ('Get'), resource ('meal-by-meal breakdown'), and scope ('detailed'), distinguishing it from siblings like get_daily_macros or get_daily_summary. However, it doesn't explicitly differentiate from get_date_range_summary, which might offer similar breakdowns over multiple dates.

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 mentions the date parameter but doesn't explain when to choose this over siblings like get_daily_summary or get_date_range_summary. There's no context on prerequisites, exclusions, or typical use cases, leaving the agent to infer usage from tool names alone.

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 implies a read-only operation by using 'Get', but doesn't specify permissions, rate limits, error conditions, or what happens if data is unavailable for the date. This leaves significant gaps for a tool that likely queries user data.

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 appropriately sized and front-loaded, with the core purpose stated first followed by parameter details. Both sentences add value, though the structure could be slightly improved by integrating the parameter explanation more seamlessly.

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 explain the return format (e.g., structured data vs. text summary), error handling, or how it differs from sibling tools, making it inadequate for an agent to fully understand the tool's behavior and output.

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 compensates by explaining the single parameter 'date' with format and default behavior. However, it doesn't add meaning beyond basic syntax, such as how invalid dates are handled or timezone considerations, leaving some ambiguity.

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: retrieving a daily nutrition overview including calories, macros, water, and goals. It specifies the verb 'Get' and resource 'daily nutrition overview', though it doesn't explicitly differentiate from siblings like 'get_daily_macros' or 'get_water_intake' which might overlap in scope.

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_daily_macros' or 'get_water_intake', which seem to offer more specific data subsets. The description mentions a default date but doesn't clarify use cases or exclusions relative to siblings.

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 for behavioral disclosure. It states the tool 'gets' data (implying read-only) and mentions 'trends and insights' but doesn't clarify authentication needs, rate limits, data freshness, or what specific insights are returned. For a data retrieval tool with zero annotation coverage, this leaves significant behavioral gaps.

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 perfectly concise and well-structured. The first sentence states the complete purpose, followed by a clear 'Args:' section with parameter details. Every sentence earns its place with no wasted words, and information is front-loaded appropriately.

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 2 parameters with 0% schema coverage and no output schema, the description provides basic purpose and parameter format but lacks critical context. It doesn't explain the return structure (what 'aggregate nutrition data' includes), doesn't define 'trends and insights', and offers no behavioral guidance. For a data analysis tool, this leaves the agent guessing about output format and analytical depth.

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 parameter semantics beyond the schema, which has 0% description coverage. It specifies date format (YYYY-MM-DD) and clarifies start_date/end_date roles. However, it doesn't explain date range constraints (e.g., maximum range, future dates allowed), timezone handling, or what happens with invalid dates. With 2 parameters and low schema coverage, this provides basic but incomplete compensation.

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 aggregate nutrition data over a date range with trends and insights' - a specific verb ('Get') + resource ('aggregate nutrition data') + scope ('over a date range'). It distinguishes from siblings like get_daily_summary by specifying 'aggregate' data with 'trends and insights' rather than daily summaries, though the differentiation could be more explicit.

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_daily_summary or get_daily_macros, nor does it specify use cases, prerequisites, or exclusions. The agent must infer usage from the purpose alone.

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 information: whether this is a read-only operation, what permissions are required, how errors are handled, or what the return format looks like (e.g., numeric value, object structure). For a data retrieval tool with zero annotation coverage, this is a significant gap.

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 perfectly concise and well-structured: a clear purpose statement followed by parameter details in a formatted 'Args' section. Every sentence earns its place with no wasted words, 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 low complexity (single optional parameter) and lack of annotations/output schema, the description is minimally adequate. It covers the basic purpose and parameter format but misses behavioral context and sibling tool differentiation. For a simple read operation, this might suffice, but it doesn't provide complete guidance for reliable agent 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?

The description adds meaningful context for the single parameter 'date', specifying the expected format (YYYY-MM-DD) and default behavior (today), which compensates for the 0% schema description coverage. However, it doesn't explain edge cases like invalid dates or timezone handling, leaving some semantic gaps.

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 ('water consumption for a specific day'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_daily_summary' or 'get_date_range_summary', which might also provide water intake data in different contexts.

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_daily_summary' (which might include water intake) or 'get_date_range_summary' (which could provide aggregated 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.