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

With no annotations provided, the description carries the full burden and clearly discloses return behavior (ready date vs capped=true when the sequence is too short) and error behavior (no_curing_stage). It also names the calculation method, which informs the agent of the model type.

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 and information-dense, with the main action, method, use trigger, return modes, and error case all front-loaded. There is no filler; every sentence 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?

The description explains the output conditions and failure mode, and an output schema exists, so the remaining gaps are acceptable. A brief note about which sibling tool to use instead would make it fully 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?

Top-level schema coverage is 0%, so the description must compensate; it does mention daily temperatures and work types in prose but does not clarify start_date semantics or the work_code enum/units. The nested schema does contain field descriptions, so this is adequate but not a strong contribution beyond 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?

States a specific action (estimate curing time), the resource class (concrete/coating work types), the input (daily temperature sequence), and the method (Nurse-Saul), which clearly distinguishes it from planning/lookup siblings. The use-case sentence also reinforces that this tool estimates curing duration rather than performing scheduling, validation, or requirements lookup.

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?

Explicitly says to use this when a work type has a curing stage and cold weather may extend duration beyond nominal, and notes that it fails with no_curing_stage for work types without one. It does not name sibling alternatives or provide a full when-not-to-use list, so it stops short of a 5.

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 disclose behavior. It does: it notes that an empty category returns an empty list with matched_count=0 and is a successful result, not an error. It also states the exclusive parameter requirement. It does not go into depth about authorization, reversible side effects, or detailed response structure, but for a read-only lookup this is adequate. The empty-result behavior is a valuable disclosure beyond what a schema would imply.

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 four sentences, front-loaded with the core purpose, then usage guidance, then a constraint, then a behavioral nuance. Every sentence contributes information; there is no fluff. The structure guides the agent from 'what' to 'how' to 'edge case' efficiently.

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?

This is a straightforward lookup tool with two optional parameters and an output schema (not shown). The description covers the trigger conditions, the parameter rule, and the successful empty-result case. Since an output schema exists, defining the return format is not necessary. All information needed to invoke the tool correctly and interpret a common edge case is present.

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 already provides rich descriptions for both parameters (enums with names and descriptions), so the baseline is 3 given high schema coverage. The description adds the critical mutual-exclusivity rule ('never both') which is also in the schema description, but the tool description reinforces it in a clearer, action-oriented way. It also mentions the two parameter names, which helps an agent map the rule to the fields. Slight added value over the schema, hence 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?

States a specific verb and resource: 'Return the normative weather constraints for construction work types.' It also explains the two call patterns (by work_code or category) and clearly distinguishes this tool from siblings like validate_work_window and estimate_curing_time, which address different concerns. The phrase 'Call this first' reinforces its role as a primary lookup.

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?

Explicitly states when to use it: 'Call this first when you need to know the temperature, wind, humidity or precipitation limits...' and also covers the alternate use case 'to enumerate all work types in a category.' It adds a strict usage constraint ('Provide either a single work_code or a category, never both') and clarifies the handling of empty results. No ambiguity remains about when to call this tool.

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 explains the scheduling algorithm ('earliest window where no day is prohibited'), the dependency handling, and what the return value contains (scheduled works and unplaced works, each with an explicit reason). It does not explicitly state side-effect behavior, but the tool's planning nature and return-focused wording make non-mutation reasonably clear.

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 four sentences and every sentence carries useful information: purpose, algorithm, return value, and usage guidance. It is not bloated, though the algorithm and return-value sentences could potentially be tightened without losing meaning.

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 a relatively complex input (multiple works, dependencies, weather windows) and an output schema, the description covers the essential behaviors: feasibility, dependency ordering, earliest placement, and the split between scheduled and unscheduled works with reasons. It does not mention input limits or detailed weather constraint logic, but those are already present in the schema and would not affect tool selection.

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 description coverage is 0% at the top level, and the description does not directly explain the `request` parameter structure. However, it does reference the core semantic pieces: 'several construction works', 'weather forecast', and 'dependencies', which map to `works`, `forecast`, and `depends_on`. The nested schema definitions are rich, so the description adds modest context without fully compensating for the top-level coverage gap.

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: 'Produce a feasible day-by-day schedule for several construction works across a weather forecast' while also naming the key constraint ('honouring declared dependencies'). It clearly distinguishes this tool from the sibling validate_work_window by framing it as the multi-work planning alternative, so an agent can identify its purpose immediately.

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 states when to use this tool instead of an alternative: 'Use this instead of repeated validate_work_window calls when planning more than one work.' This provides a clear condition and names the sibling tool it replaces, giving the agent actionable selection guidance.

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 discloses the output format (allowed, conditional, or prohibited verdict) and that it includes constraints breached and by how much. It also states the tool is deterministic and authoritative, setting clear expectations for behavior. It does not detail the exact conditions for each verdict, but for a validation tool this is adequate and is not a major 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 concise, consisting of two clear sentences with no redundant phrases. It efficiently conveys purpose, usage, and behavior without fluff, making it easy to parse.

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 fully covers the necessary context: what the tool does, when to use it, what it returns, and how it differs from a sibling. The existence of an output schema (even if not shown) is not a barrier because the description already specifies the expected output shape. It is self-contained and sufficient for an agent to decide when and how to invoke it.

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 provides rich descriptions for the nested fields (work_code, window, and weather properties), so the parameter semantics are already well-defined. The description text does not add explicit parameter details beyond implicitly referencing 'work type' and 'day of weather.' Since the schema coverage is strong, the description's minimal contribution is acceptable, but it does not elevate the score beyond the baseline.

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 function: evaluating whether a work type may proceed under given weather. It explicitly distinguishes it from the sibling tool plan_work_schedule, 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 explicit guidance on when to use this tool versus the alternative: 'Use this to check a single work-day; use plan_work_schedule when you need to place several works across a forecast.' This leaves no ambiguity about appropriate invocation scenarios.

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