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 but offers minimal operational context. While it notes filters are 'optional,' it fails to mention critical list-operation behaviors such as pagination, result limits, rate limiting, or the structure/format of returned activities.

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 concise and front-loaded, presenting the core purpose in the first sentence followed by an Args section detailing parameters. The structure is efficient with minimal redundancy, though the docstring-style Args format is slightly more verbose than inline prose.

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 minimally viable for a listing tool with three optional parameters and no output schema. It adequately covers parameter semantics but lacks essential completeness given the absence of an output schema—it should describe the return value structure (e.g., 'returns a list of activity objects') and pagination behavior expected for list endpoints.

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 effectively compensates by documenting all three parameters with useful semantics: it specifies date formats (YYYY-MM-DD) and inclusivity for date ranges, and provides concrete examples ('Ride', 'Run', 'Swim') for the sport_type filter, adding significant value beyond the raw 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 'List activities' with optional filters, providing a specific verb and resource. However, it does not explicitly differentiate from sibling tool 'get_activity_detail' (which likely retrieves a single activity versus listing multiple), leaving the distinction to be inferred from the tool names alone.

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_activity_detail' or other sibling tools. There are no stated prerequisites, exclusion criteria, or scenarios recommending this tool over others for specific use cases.

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 disclosure burden. It adds valuable context that 'full detail' specifically includes 'laps and intervals,' but omits safety characteristics (read-only status), error handling (404 behavior), or rate limiting that annotations would typically cover.

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?

Efficient two-part structure with the main sentence front-loaded with key behavioral details (laps/intervals), followed by a standard Args block. No redundant or 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?

Appropriate for a single-parameter tool without output schema. The description conceptually explains the return value (laps/intervals) and documents the required parameter. Minor gap regarding error cases or authentication, but sufficient for the complexity level.

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%, requiring description compensation. The Args section adds critical context beyond the schema: it defines activity_id and specifies it is 'visible in the intervals.icu URL,' helping users locate the correct value. Could be a 5 if it added format validation rules.

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?

Description clearly states the tool 'Get[s] full detail for a single activity' with specific scope including 'laps and intervals.' The 'single activity' focus effectively distinguishes it from sibling get_activities (likely a list operation).

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 explicit when-to-use guidance or comparison to alternatives (e.g., 'Use this instead of get_activities when you need lap data'). However, the 'single activity' phrasing implies usage for detailed retrieval versus bulk listing.

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 partially discloses what data is returned ('sport zones and thresholds'), but lacks information on caching behavior, authentication requirements, rate limits, or error conditions. It implies a read-only operation through the verb '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 single, efficient sentence of seven words. It is front-loaded with the action verb and contains no redundant or 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?

Without an output schema, the description partially compensates by mentioning specific return content ('sport zones and thresholds'), but lacks details on response structure, data types, or complete field enumeration. Given the tool's simplicity (no inputs) and lack of annotations, this is minimally adequate but has clear gaps.

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 contains zero parameters. According to the scoring rules, zero parameters warrant a baseline score of 4, as there are no parameter semantics to clarify beyond what the schema already provides.

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 provides a specific verb ('Get'), resource ('athlete profile'), and scope ('including sport zones and thresholds'). It implicitly distinguishes from siblings (get_activities, get_fitness, etc.) by focusing on static profile data rather than time-series or activity data, though it doesn't explicitly differentiate from 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?

No guidance provided on when to use this tool versus alternatives like get_fitness (which may overlap with 'thresholds') or get_wellness. No prerequisites or exclusion criteria 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds the context that events are 'planned' (suggesting future calendar items), but fails to disclose read-only status, pagination behavior, date range limits, or timezone handling. The agent must infer safety from the verb 'Get' without confirmation.

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 a clear purpose statement followed by an Args block. Every sentence earns its place—no filler text. The format is front-loaded with the core action and scoped appropriately for a two-parameter tool.

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 structure (two nullable strings) and lack of output schema, the description is minimally adequate. It covers parameter semantics sufficiently but leaves gaps regarding optional parameter behavior and return value structure. For a read-only retrieval tool with no annotations, additional context about data volume or filtering behavior would strengthen 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?

With 0% schema description coverage, the description effectively compensates by documenting both parameters in the Args block: it specifies that 'oldest' is the start date and 'newest' is the end date, both requiring 'YYYY-MM-DD' format and noting they are 'inclusive'. Deducted one point because it omits that both parameters are optional (nullable with defaults) per 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 retrieves 'planned workouts and races from the calendar'—specific verb (Get), resource (workouts/races), and scope (planned/from calendar). It implicitly distinguishes from sibling 'get_activities' (which implies completed/historical data) through the keyword 'planned', though it doesn't explicitly name the sibling comparison.

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 explicit guidance on when to use this tool versus siblings like 'get_activities' or 'get_activity_detail'. While the 'planned' qualifier implies usage for future/scheduled events, there are no explicit when-to-use constraints, prerequisites, or exclusions 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?

With no annotations provided, the description carries full behavioral burden but offers minimal disclosure. While it identifies the return as a 'time series,' it fails to specify the data structure, frequency of data points, or what occurs when optional date parameters are omitted (default behavior). No mention of calculation methodology or data availability limits.

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?

Extremely efficient with zero redundancy. Front-loads the core purpose in the first sentence, followed by concise Args documentation. Every sentence earns its place despite the brevity.

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?

Inadequate given the lack of output schema and annotations. While parameters are documented, the description omits the return value structure entirely—critical information for a time series endpoint. Should describe the temporal resolution, data fields returned (CTL/ATL/TSB values), and null-parameter behavior.

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?

Compensates effectively for 0% schema description coverage by providing critical format details (YYYY-MM-DD) and boundary semantics (inclusive) for both date parameters. Deducted one point because it does not clarify that both parameters are optional (default null) or explain the behavior when date ranges are not specified.

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?

Clearly states it retrieves CTL/ATL/TSB fitness time series with specific verb 'Get' and identifies the domain (fitness metrics vs activities/athletes in siblings). Deducted one point because it assumes familiarity with CTL/ATL/TSB acronyms without explaining they refer to Chronic Training Load, Acute Training Load, and Training Stress Balance metrics.

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?

Provides no guidance on when to use this versus sibling tools like get_activities or get_wellness. Does not explain what fitness insights this provides compared to raw activity data, nor does it mention prerequisites like requiring historical activity data to calculate these 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 the full burden. It discloses what data categories are returned but omits behavioral details such as whether results are aggregated or daily, pagination behavior, or default behavior when date parameters are null.

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 a clear purpose statement followed by a structured Args section. No sentences are wasted, though the Args format slightly duplicates schema structure that could have been in the schema itself.

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 2-parameter retrieval tool with no output schema, the description adequately covers inputs but only superficially covers output structure (listing metrics without indicating if returned as array, daily snapshots, or averages).

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 Args section adds crucial value by specifying the YYYY-MM-DD format and inclusivity of the date bounds. It could improve by noting that both parameters are optional/nullable per the schema defaults.

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 'Get[s] wellness data' and specifically enumerates the metrics returned (HRV, resting HR, sleep, fatigue scores), distinguishing it from sibling tools like get_activities (workouts) and get_athlete (profile).

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?

While the description implicitly guides usage by defining the specific data domain (wellness metrics vs fitness or activities), it lacks explicit guidance on when to use this versus get_fitness or whether date parameters are required for optimal use.

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