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

With no annotations, the description carries the full burden. It discloses a key behavioral trait: the maximum date range of 92 days, which is not evident from the schema. The verb 'Return' implies a read-only operation, and no side effects are suggested, which is adequate for a simple data retrieval 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 one concise sentence of 12 words, front-loaded with the action and resource. It wastes no words and every element earns its place, including the critical range limit.

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 low complexity (two optional parameters), full schema coverage, and the presence of an output schema, the description is largely complete. It adds the essential 92-day constraint but does not explicitly guide tool selection among siblings, though the name and description already make the use case clear.

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 already provides 100% coverage for each parameter's format and defaults, so the baseline is 3. The description adds extra meaning by constraining the combined date range to a maximum of 92 days, a relationship between the parameters not present 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 the specific verb 'Return' and identifies the resource as 'body composition data' with a scoped date range constraint (max 92 days). This clearly distinguishes it from sibling tools like get_latest_body_composition, which targets a single latest measurement, and get_measurements_range, which likely covers other metric types.

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 clear context that this tool is for retrieving body composition data over a date range, and the explicit max 92-day limit is a practical usage constraint. It does not name specific alternatives or exclusions, but the scope is unambiguous for the agent to infer when this tool is appropriate.

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 must convey safety and behavior. 'Return' implies a read-only operation and the 30-day filter is disclosed, but edge cases like no records found, data availability, or ordering are not addressed. It is not misleading but leaves room for assumptions.

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 sentence that leads with the verb, states the object, and includes a temporal constraint. Every word earns its place with no redundancy.

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 is a simple zero-parameter retrieval and an output schema exists, the description covers the essential behavior. However, the 30-day window is a notable limitation that could spark questions about why that specific window, but this is a minor gap and does not hinder 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?

There are zero parameters, so the schema covers everything trivially. The baseline for 0 params is 4, and the description adds no parameter confusion. No additional semantics are needed.

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 ('Return'), the resource ('body composition record'), and a specific constraint ('most recent within last 30 days'). This distinguishes it from siblings like get_measurements_range (range-based) and get_body_composition (likely general retrieval).

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 use when a recent body composition record is needed within a 30-day window, but it does not explicitly mention when to avoid this tool or cite alternatives such as get_body_composition or get_measurements_range. Context is implied rather than stated.

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 transparency burden. It does add the context that data is 'cached', implying a read-only operation on stored data, but it does not disclose behaviors such as handling of empty data or performance characteristics. Minimal but not misleading.

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, well-structured sentence that begins with the verb and fully conveys the tool's purpose. No superfluous words or redundant details.

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?

With no parameters and an output schema present, the description is almost entirely sufficient. It clearly states what is returned (date range and count). A slight gap is that it does not specify whether the count represents all cached records or a subset, but this is minor given the tool's simplicity and the presence of an 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, making schema coverage trivially 100%. The description correctly adds no parameter-specific information since none exist, meeting the baseline for a no-parameter tool.

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 a specific action ('Return') and resource ('date range and total count of cached body composition data'). It is distinct from sibling tools that return actual measurement values or profile information, making its purpose 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 provides no guidance on when to use this tool versus alternatives like get_latest_body_composition or get_body_composition. It is clear what it returns, but not under what circumstances an agent should choose it.

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 burden. 'From cache or API' discloses a caching/fallback behavior, but lacks details on privacy, error handling, or data freshness. This is minimal behavioral transparency, adequate for a read-only profile fetch but not enriched.

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 concise sentence, front-loaded with the main action and resource, and includes only essential details (fields and source). No wasted words.

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 (no params, no side effects, no complex behaviors). The output schema covers return values, and the description adds the cache/API sourcing context. This is complete for the tool's complexity level, especially compared to siblings which are more specialized.

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?

There are zero parameters, so schema coverage is trivially 100%. The description adds no parameter-specific info because none exist, but it does list the returned fields in parentheses, which helps the agent know what the profile contains. Baseline 4 is appropriate for a no-parameter tool.

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 'Return user profile' with specific fields (birth_date, height_cm, sex), distinguishing it from sibling tools that focus on body composition or measurements. The verb 'Return' and resource 'user profile' are 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 implies this tool is for fetching profile data as opposed to sibling tools (which handle body composition/measurements). However, there is no explicit 'when to use this vs alternatives' guidance, only the source note 'from cache or API' which gives context but not exclusions.

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?

The description accurately conveys a read-only list operation, but with no annotations it carries the full burden. It does not mention any side effects or special behaviors, though the simplicity of the operation makes this acceptable.

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, front-loaded sentence with no wasted words, directly stating the tool's 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?

The description is adequate for a simple tool with an output schema, but it lacks usage guidelines and contextual differentiation from sibling tools, making it only moderately 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 tool has zero parameters, and the schema is trivially complete. According to the rubric, 0 params earns a baseline score of 4.

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 returns the list of available metrics and their descriptions, using a specific verb 'Return' and resource. It implicitly distinguishes from sibling tools that retrieve body composition data, but does not explicitly name alternatives.

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 other metric-related tools. The description lacks any contextual cues or exclusions, leaving the agent to infer usage.

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