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log_meat

Idempotent

Log a new meat processing batch and record the resulting cuts to freezer inventory. Requires farmId and apiKey authorization.

Input Schema

NameRequiredDescriptionDefault
dateNoDate processed in YYYY-MM-DD format (defaults to today)
partYesThe cut/part type (e.g., Whole Bird, Breast, Leg). Will be validated against configured farm parts if set.
notesNoOptional notes
unitsYesMeasurement units for quantity
farmIdYesThe unique farm ID
batchIdNoOptional unique batch/hatch ID associated with these birds
confirmNoSet to true to persist the write, otherwise returns a dry-run preview
varietyYesThe species or breed variety processed (e.g., Jumbo Coturnix)
quantityYesPositive integer quantity of units processed
totalWeightNoOptional total weight of the batch (used to calculate average weight)
weightPerUnitNoOptional average weight per processed unit
idempotencyKeyNoOptional unique client-provided key to prevent duplicate processing

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnly=false, idempotent=true, and destructive=false, so the safety profile is partly covered. The description usefully adds the side effect (cuts written to freezer inventory) and the authorization requirement. However, it omits the significant dry-run behavior driven by the 'confirm' parameter (false returns a preview), which is a key behavioral trait not conveyed by the annotations.

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 tight sentences with zero filler; the primary action and its inventory side effect are front-loaded ahead of the prerequisite note. Nothing is wasted or buried.

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 12-parameter, non-read-only mutation with no output schema, the description covers the core action, the inventory side effect, and auth, which is roughly adequate. Yet it leaves out the confirm/dry-run default, an important operational detail for invoking this write correctly, so it is only minimally complete.

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 12 parameters is documented in the schema itself, establishing the baseline of 3. The description adds no parameter-level detail and even references 'apiKey,' which is not an actual input parameter, so it does not meaningfully extend what the schema provides.

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 ('Log') and resource ('meat processing batch') plus a secondary effect ('record the resulting cuts to freezer inventory'), which clearly separates it from siblings like log_bird_batch, log_eggs, or get_meat_inventory. It stops short of explicitly naming an alternative sibling to disambiguate, so it lands at 4 rather than 5.

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 only guidance is the prerequisite note ('Requires farmId and apiKey authorization'). There is no when-to-use versus when-not-to-use, no mention of alternatives such as log_bird_batch, and no indication of when a dry-run preview versus a persisted write is appropriate. Usage must be entirely inferred.

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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