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log_hatch_set

Start a new incubation batch, auto-calculating lockdown/hatch dates. ALWAYS ask for confirmation before writing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNoOptional notes
farmIdYesThe unique farm ID
sourceNoOptional source flock or vendor
confirmNoSet to true to commit, false for dry-run preview
eggsSetYesNumber of eggs set (positive finite integer)
setDateNoOptional set date (YYYY-MM-DD, defaults to today, cannot be in future)
varietyYesThe poultry/gamebird breed name (e.g. Pharaoh, Chicken)
incubatorNoOptional incubator name/identifier
idempotencyKeyNoUnique request idempotency key

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false, and idempotentHint=false. The description adds genuine behavioral context beyond them: that lockdown/hatch dates are derived automatically, and that a confirmation step is mandatory before the write commits. It stops short of explaining what the write creates or how the derived dates are computed.

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?

Two short sentences with zero filler, and the primary action is front-loaded ahead of the confirmation constraint. Nothing is redundant or buried.

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?

For a 9-parameter mutation with annotations covering the safety profile, full schema coverage, and no output schema, the description is nearly sufficient. The one notable gap is that it doesn't mention the dry-run preview path via the confirm parameter, which is central to how this tool is safely invoked.

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 description coverage is 100%, so every one of the 9 parameters is already documented, including the confirm dry-run flag, setDate constraints, and idempotencyKey. The description adds no parameter-level meaning beyond that, 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?

The description names a specific verb and resource ("Start a new incubation batch") and adds a distinctive scope detail (auto-calculating lockdown/hatch dates). An agent can distinguish it from log_hatch_result or get_hatches, though the description never names those siblings explicitly to sharpen the contrast.

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?

"ALWAYS ask for confirmation before writing" is a real usage directive that tells the agent how to sequence the call, but it says nothing about when to choose this tool over log_hatch_result, get_hatches, or log_eggs. Usage is implied by the purpose statement rather than explicitly framed against alternatives.

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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