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apply_leave

Submit employee leave requests for specific dates by providing employee ID and leave dates to manage time off.

Instructions

Apply leave for specific dates (e.g., ["2025-04-17", "2025-05-01"])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
employee_idYes
leave_datesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Apply leave' implies a write/mutation operation, the description doesn't specify whether this requires approval, what permissions are needed, whether it's reversible, or what happens with conflicting dates. The example format is helpful but doesn't address critical behavioral aspects of a leave application system.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise - just one sentence with a helpful example. There's no wasted text, and the information is front-loaded. However, the brevity comes at the cost of completeness, making it more under-specified than optimally concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given this is a mutation tool with no annotations, 2 parameters (0% schema coverage), but with an output schema present, the description is minimally adequate. The example format helps, but critical context is missing: no error conditions, no relationship to sibling tools, no behavioral constraints. The output schema existence means return values are documented elsewhere, but the description should still address more operational context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides an example format for 'leave_dates' parameter (array of date strings), which adds meaningful context beyond the schema's 0% description coverage. However, it doesn't explain the 'employee_id' parameter at all, nor does it clarify date format requirements, validation rules, or what constitutes valid leave dates. The schema coverage is 0%, so the description partially compensates but not fully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool's purpose ('Apply leave for specific dates') which is a clear verb+resource combination. However, it doesn't distinguish this from potential sibling tools like 'get_leave_balance' or 'get_leave_history' - it only describes what it does without contextual differentiation. The example dates help illustrate the format but don't enhance the core purpose statement.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. There's no mention of prerequisites, constraints, or relationships with sibling tools like 'get_leave_balance' (which might be needed before applying leave) or 'get_leave_history' (which might show past applications). The agent receives no help in determining appropriate usage contexts.

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

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