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Knowify: Log time

knowify_log_time

Knowify: Enter hours on a job phase in the Time Tracker and submit them for review. Enters hours on a job phase in the Time Tracker and submits them for review, where they appear in Review Time. The phase must have labor enabled. It reaches no client, so it runs without asking first. Needs the user's Cotlet API key with access to this app.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesDay worked.
hoursYesHours worked as a decimal, e.g. 7.5.
job_idYesJob ID from list_jobs.
phase_idYesPhase ID from get_job. The phase must have labor enabled.
descriptionNoWhat was done.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations carry the safety profile (readOnly=false, destructive=false, openWorld=true), and the description adds useful context beyond them: the labor-enabled precondition, the post-submission Review Time state, that no client is notified, and the required Cotlet API key scope. It stops short of describing failure modes or the response shape.

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

Conciseness3/5

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

The core action is front-loaded, which is good, but the opening two sentences are near-verbatim duplicates of one another, wasting a sentence. The tail sentences about labor enablement, confirmation, and auth key are each distinct and worthwhile.

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?

For a 5-parameter mutation with no output schema, the description covers prerequisites and auth but never explains what the call returns or what happens on a failed submission. It is adequate to call correctly but leaves the return contract to inference.

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?

Schema coverage is 100% and every parameter is already documented (job_id from list_jobs, phase_id from get_job, decimal hours, date). The description only re-touches the labor-enabled phase rule, adding minimal meaning beyond the schema, so the baseline 3 applies.

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

Purpose4/5

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

States a specific verb (enter hours) and resource (job phase in the Time Tracker) plus the outcome (submit for review), so it is distinguishable from siblings like knowify_list_time_entries. However, the first two sentences restate the same action, diluting the benefit.

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

Usage Guidelines3/5

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

Provides real prerequisites (phase must have labor enabled, submitted entries show in Review Time) and a confirmation note ('runs without asking first'). It does not, however, explicitly contrast with alternatives or state when a caller should pick this over, say, updating an existing entry.

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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