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

No annotations are provided, so the description carries the full behavioral burden. It discloses the analysis dimensions and return scope, but says nothing about permissions, whether it falls back gracefully when calendars are empty, or any rate/volume constraints. For a read-only analysis tool this is adequate but leaves 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?

Front-loaded with a one-line summary, then structured Args/Returns sections with no filler. Every sentence carries information; the only minor redundancy is restating return contents given a dedicated output schema exists.

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 an output schema present, the description needn't detail return shapes, yet it summarizes them helpfully. The two optional parameters are fully covered, and the read-only analysis nature is clear; missing only explicit guidance on when to prefer it over siblings.

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, and it does: it documents both parameters with their expected format (YYYY-MM-DD), their defaults (next Monday, ['primary']), and their meaning. This meaningfully exceeds the bare schema, though it adds no validation or edge-case notes.

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?

States a specific verb and resource ('analyze' + 'week' of calendar events) and enumerates the concrete outputs: day-by-day breakdown, office days, meeting hours, gaps, back-to-back blocks. It is clearly distinct from the sibling tools (danger zones, workout slots, commute), though it never explicitly names or contrasts 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?

Usage is implied by 'upcoming week' and the default of next Monday, giving an agent a sense of the intended scope. However, there is no explicit when-to-use, when-not-to-use, or comparison to any alternative tool, so the agent must infer the trigger conditions.

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 behavioral burden. It discloses key outputs (office days, earliest time, early morning warnings) and that it analyzes calendar events, but omits permission needs, rate limits, and any caveats about detection reliability.

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 well-structured with a front-loaded summary, a bulleted list of provided information, and clear Args/Returns sections. It is slightly verbose in the bullet list but remains efficient and every part earns its place.

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 (calendar analysis, 4 parameters) and the presence of an output schema, the description is quite complete. It explains what the tool does, what inputs mean, and what the returns contain, though it could mention any prerequisite calendar access permissions.

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 does so by documenting all four parameters with expected formats, defaults, and meaning (e.g., 'YYYY-MM-DD' for dates, 'Defaults to config' for office_indicators). Only minor details like timezone handling are missing.

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?

States a specific verb (identify) and resource (days requiring office commute), and describes its analysis approach (analyzes calendar events to detect office days). It doesn't explicitly differentiate from siblings, which operate on different domains, but the purpose is crystal 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?

Usage context is implied by the description of the analysis and its output, but there is no explicit when-to-use, when-not-to-use, or alternative tool guidance (e.g., versus analyze_week). The agent can infer it's for commute planning, but no routing is provided.

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 burden, and it does add real behavioral detail: concrete thresholds (lunch window 12-2pm, back-to-back stretches over 3 hours) and that results carry severity levels. However, it never states whether the operation is read-only, whether any permissions are required, or how calendar_ids resolution behaves, leaving notable gaps for a zero-annotation 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 content is front-loaded and organized into Detects/Args/Returns sections, with the purpose stated first in one line. It is slightly verbose because the Args and Returns blocks partially restate the schema, but nothing is wasteful enough to hurt readability.

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?

An output schema exists, so the return values need not be fully explained, and the description appropriately covers detection rules, parameter formats, and the severity-bearing output. Combined with the detailed threshold list, an agent has enough to invoke it correctly, though read-only/permission semantics remain unstated.

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%, so the description must carry parameter meaning, and it does: date format (YYYY-MM-DD) for start_date and end_date, and that calendar_ids is a list defaulting to ["primary"]. That adds format and default semantics beyond the bare schema titles, only missing details like timezone handling.

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 gives a specific verb (identify/detect) and a concrete resource (scheduling problems and conflicts), then enumerates the exact conditions it flags (no-lunch days, >3h stretches, zero free time, overlapping events). This is far more specific than the tool name alone, though it never contrasts itself with the likely overlap with analyze_week, so sibling differentiation is absent.

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?

Usage is implied by the enumerated detection types, but there is no explicit when-to-use, when-not-to-use, or mention of the alternative analyze_week sibling. An agent can infer the scenario but must guess how it relates to the other analysis 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?

With no annotations, the description carries the full behavioral burden and does disclose real traits: the ranking heuristic (availability, preferred time of day, duration, commute patterns avoiding early mornings before office days) and that omitted parameters fall back to config settings. It does not state read-only/non-mutating behavior explicitly or mention auth or rate limits, so it stops short of full disclosure.

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?

Front-loads the purpose, then uses tight Args/Returns sections with no filler. Slightly list-heavy, and the Returns line restates what the output schema already conveys, but overall efficient and scannable.

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 5-parameter read-style recommendation tool with an output schema present, the description covers inputs, defaults, and the ranking behavior well enough to call it correctly. The only omissions are mutation/side-effect clarity and the enum values for preferred_times.

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, and its Args block documents all five parameters including date format (YYYY-MM-DD), the meaning of preferred_times, and the config-backed defaults for the three optional parameters. It doesn't enumerate the morning/lunch/evening enum values, but that gap is minor against the otherwise complete 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 opens with a specific verb+resource: 'Find optimal times for booking fitness classes,' and the name suggest_workout_slots matches. An agent can tell this is a slot-recommendation tool apart from analyze_week or find_danger_zones, though it never explicitly names or contrasts those siblings.

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?

Usage is implied by the framing ('for booking fitness classes') and the enumerated ranking factors, but there is no explicit statement of when to call this instead of analyze_week or check_commute_requirements, and no prerequisites or exclusions. Minimum-viable guidance only.

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