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 date handling (ISO format, default last 7 days) and optional sleep stages flag. However, it does not mention potential rate limits, auth requirements, or that it is a read-only operation, though the read-only nature is implicit.

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 paragraph that lists metrics and key behaviors efficiently. It is front-loaded with the main output and then adds date and flag details. No wasted words, but a bulleted format could improve scannability.

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 a simple schema with only 3 optional parameters and an output schema. The description covers the main output fields and the parameter semantics, making it adequate for an agent to call correctly. Minor gaps include not specifying the exact output structure, but the output schema likely covers 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 description coverage is 0%, so the description must compensate. It explains that dates are ISO YYYY-MM-DD and defaults to last 7 days, but does not clarify which parameter is 'from' vs 'to' (though names are self-explanatory). It also explains include_sleep_stages, but not the exact format of output.

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 per-day health and sleep data, listing specific metrics (steps, distance, calories, sleep stages). It distinguishes itself from siblings by focusing on daily summary, whereas siblings like get_workout_detail or get_health_metrics target other data 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 implies use for daily summaries and mentions date range defaults and optional sleep stages. It does not explicitly state when not to use it or name alternatives, but the context of siblings makes the distinction clear enough.

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 transparency burden. It discloses the output scope and fields, but does not explicitly state that the operation is read-only, whether authentication is required, or any rate-limit/error behavior. For a simple getter this is adequate but not fully transparent.

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?

A single, front-loaded sentence that conveys the resource, scope, and key output fields without any filler or repetition.

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 parameterless list operation with an output schema, the description is complete: it states the account-bound scope and the meaningful output fields. No additional invocation details are needed.

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, so there is no parameter burden. The schema coverage is 100% and the description adds helpful context about what the returned list contains, meeting the baseline for parameterless tools.

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 identifies the resource (devices bound to the account) and specifies the returned attributes (model name, firmware, active status). This distinguishes it from sibling tools focused on workouts, summaries, and health 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?

No guidance is given about when to use this tool versus alternatives such as zepp_status or get_health_metrics. The intended use is implied by the name and content, but there are no explicit selection criteria or 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?

With no annotations, the description carries the behavioral disclosure burden. It does well by stating daily granularity, what each metric category includes, the default date range, and the effect of include_stress_series. It omits error/rate-limit behavior, but the output schema likely covers return shape.

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 compact, front-loaded with the core concept, and uses bulleted lists and short parameter notes. Every sentence earns its place with no redundant filler.

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 no annotations and an existing output schema, the description covers the essential inputs and high-level outputs well. It could add an explicit example or state when not to use it, but nothing critical is missing for correctly invoking this read-style tool.

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%, yet the description compensates thoroughly: metrics are explained as a subset with a default, dates are specified as ISO YYYY-MM-DD with a 7-day default, and include_stress_series is explained as adding 5-minute stress readings. All four parameters receive meaningful context.

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 identifies a specific resource — daily recovery and health metrics — and enumerates the four metric families (readiness, pai, stress, spo2). It does not explicitly contrast with sibling tools like get_daily_summary or summarize_workouts, so it misses the top score for sibling differentiation.

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 operational context: one entry per date, a subset of metrics can be requested, ISO dates default to the last 7 days, and include_stress_series adds verbose data. It does not state when not to use the tool or mention alternatives, but the context is unambiguous.

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 carry the behavioral disclosure burden. It does reveal that the tool only reports which per-sample track fields exist and defers actual track fetching to get_workout_track, which is a valuable boundary. However, it never states that the operation is read-only, how expensive it might be, or what happens on invalid trackids, leaving some behavioral ambiguity for an agent.

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 dense sentence that front-loads the core purpose ('Full analysis of one workout') before enumerating contents and ending with a pointer to the sibling. Every clause adds information and there is no filler or repetition.

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 output schema and no annotations, the description must convey return semantics itself. It names the major result categories (HR zones, splits, drift, track-field existence) and clarifies the boundary with get_workout_track. It omits error handling, response shape, and any mention of performance, but for a single-parameter read/introspection tool the coverage is largely sufficient.

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 a bare 'trackid' name with zero description coverage, so the description needs to compensate. It does imply that trackid selects 'one workout', which adds meaning beyond the raw schema. Still, it never explicitly defines trackid as the workout identifier, nor gives format or validation rules, so compensation is partial rather than thorough.

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 opens with 'Full analysis of one workout' – a specific verb, resource, and scope. It enumerates concrete contents (HR zones, splits, drift, track-field existence) and explicitly distinguishes itself from get_workout_track by directing track-data fetches there, making sibling differentiation clear.

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 clearly implies this tool is for deep analysis of a single workout, as opposed to listing or summarizing. It also names get_workout_track as the alternative for fetching per-sample track fields. However, it does not explicitly state when not to use this tool versus the other siblings like list_workouts or summarize_workouts, so the guidance is clear but not exhaustive.

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 burdenasi. It discloses downsampling to at most max_points evenly spaced points, the meaning of t_s, that only recorded fields are returned, and that fields limits output. It does not cover errors or auth, but for a read-oriented tool this is strong 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?

Three tight sentences front-load the core behavior, then enumerate possible fields and filtering. There is no filler, and every sentence adds usable 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 description covers the essential behavior, field semantics, and sampling rules. It lacks an explicit return-shape statement, which matters since no output schema exists, but the 'per-sample time series' framing plus the field list gives enough for an agent to invoke and interpret the response correctly.

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 clearly explains fields with allowed values and an example, and max_points with its downsampling behavior. The trackid parameter is not explicitly described, though its role is reasonably inferable from the tool name and 'one workout'.

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 states a specific verb and resource: 'Decoded per-sample time series for one workout'. This clearly distinguishes it from sibling tools like summarize_workouts and get_workout_detail, which by their names imply aggregate or summary data rather than raw time series.

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 by emphasizing per-sample time series and optional field filtering, but it does not explicitly state when to prefer this tool over get_workout_detail or list_workouts. There are no exclusions or alternative routing, so the guidance is inferred 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?

With no annotations provided, the description carries the full burden of behavioral disclosure, and it excels: it states ordering (newest first), output format (compact summaries with field list), filtering semantics (inclusive dates, local time, case-insensitive substring), default limit, and the special `part_of` attribute for multisport events. It also explains how to link to detail tools, leaving no hidden 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 front-loaded with the core action and output fields, then efficiently covers parameters in a compact block, followed by a special-case note and routing instruction. Every sentence adds value—there is no filler or redundancy. The structure is logical and easy to scan, making it both concise and informative.

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 four parameters, an output schema, and no annotations, the description is fully complete. It covers all parameter behaviors, output characteristics (including swim-specific fields), special cases (multisport legs), and cross-tool usage. The output schema already defines the return structure, so the description needn't repeat it; nothing an agent needs to call the tool correctly is missing.

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 zero descriptions, so the description must fully document parameters. It does so comprehensively: from_date/to_date are defined as optional ISO YYYY-MM-DD bounds (inclusive, local time), sport is a case-insensitive substring filter with examples, and limit is the max items with default 30. Every parameter's meaning, format, and default are spelled out, exceeding what the schema alone could convey.

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 opens with a precise verb and resource: 'List workouts, newest first, as compact summaries'. It enumerates the exact fields returned, making the tool's scope unmistakable and distinguishing it from detail tools like get_workout_detail or aggregation tools like summarize_workouts. The phrase 'compact summaries' also clarifies the level of detail, leaving no ambiguity about what the tool does.

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 explicitly instructs the agent to use the returned `trackid` with get_workout_detail / get_workout_track for further detail, effectively routing the user to the appropriate sibling tools. It also notes special handling for multisport legs, which guides correct usage. While it doesn't explicitly contrast with every sibling, the mention of detail tools and the emphasis on listing makes the intended use clear.

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 behavioral burden. It discloses a non-obvious aggregation rule ('Multisport parents are skipped; their individual legs are counted') and states the default 12-week date window. It could add a note about read-only guarantees or empty-result behavior, but the disclosed traits are valuable and non-obvious.

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 tightly structured: a summary line, one line per parameter, and one final behavioral note. No filler or redundant phrasing. It front-loads the core purpose before parameter 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?

The tool has 5 optional parameters and an output schema, so the description need not explain return values. It covers all parameters, defaults, date formats, the default range, and a critical edge case (multisport handling). Nothing an agent needs to call this tool correctly is missing.

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 fully compensate. It does: every parameter is explained with its allowed values (group_by options), default (by_sport=true), format (ISO dates), and meaning (sport substring filter). This is exemplary compensation for an empty 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 opens with a precise verb and resource ('Training totals per period') and enumerates the exact metrics computed (count, hours, km, calories, etc.), making it unmistakably an aggregation tool. This clearly separates it from sibling tools that list workouts or return details/tracks for individual activities.

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 very clear functional context: it summarizes workouts by period with grouping and sport-splitting options. It does not explicitly name alternatives or state when not to use it, but the aggregation purpose is so unambiguous that an agent can infer the right use case.

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 explains what state information is returned but does not explicitly state whether the tool performs network I/O, may trigger a fresh login, or is strictly read-only. The mention of 'token cache or a fresh login' raises ambiguity about side effects.

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?

A single front-loaded sentence that states the action and lists the exact outputs. No filler, no repetition, and every phrase adds meaning, making it highly scannable for an agent.

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 zero-parameter, low-complexity status tool without an output schema, the description covers the main return values. It lacks an explicit return shape, but the named fields (user id, session source, workout count) are sufficient for an agent to understand the result.

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 schema coverage is trivially 100%, so a baseline of 4 applies. The description adds no parameter details because there are none to document, which is appropriate.

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 opens with a specific verb and resource ('Check the Zepp cloud connection') and enumerates the exact data items it returns: user id, session source (token cache vs fresh login), and workout count. This clearly distinguishes it from sibling tools that handle workouts, devices, and 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?

The description implies when to use it: when you need to verify the Zepp cloud connection or see session status. However, it does not explicitly name alternatives or state conditions for when not to use this tool versus a sibling, leaving the routing implicit.

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