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log_time

Record hours an employee worked on a project for a specific day, with optional note. Logged time immediately updates project hours and utilization reports.

Instructions

Log hours that an employee worked on a project for a specific day. Use this when someone reports time worked or asks you to record effort. employee and project accept a name, a unique name fragment, or a numeric id; date is YYYY-MM-DD; hours must be greater than 0 and at most 24; note is an optional short description of the work. Returns the created time entry including its id. Logged time immediately shows up in get_project_hours and utilization_report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
employeeYes
projectYes
dateYes
hoursYes
noteNo
Behavior4/5

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

No annotations provided; the description carries the full burden. It discloses the mutation effect (logging hours), the return value (created entry with id), and the immediate impact on other tools (get_project_hours and utilization_report). It does not discuss potential side effects like overwriting existing entries, but the behavior is straightforward.

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

Conciseness5/5

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

The description is concise: just a few sentences without unnecessary detail. It is front-loaded with purpose, then usage, then parameter details, return value, and downstream effects. Every sentence earns its place.

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

Completeness4/5

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

Given moderate complexity (5 simple parameters, no output schema), the description covers purpose, usage, parameter semantics, and effects. It does not discuss error handling or edge cases, but those are generally beyond expectations. The inclusion of downstream tool visibility adds completeness.

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

Parameters5/5

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 adds meaning to all parameters: explains that employee and project accept names, fragments, or numeric IDs; date format YYYY-MM-DD; hours range (0 to 24); note optional. This is thorough and goes beyond the schema's type-only information.

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

Purpose5/5

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

The description clearly states the tool's action: 'Log hours that an employee worked on a project for a specific day.' It uses a specific verb (log) and resource (hours), and distinguishes itself from sibling tools like get_project_hours (read) and create_task (different domain).

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage context: 'Use this when someone reports time worked or asks you to record effort.' It does not list explicit alternatives or when-not-to-use, but the context is clear and sufficient given the sibling list.

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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