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log_eggs

Log a new daily egg collection count. Supports confirm and idempotency validation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoOptional log date (YYYY-MM-DD)
typeNoOptional variety (e.g. Pharaoh, Celadon, Standard)
notesNoOptional notes
farmIdYesThe unique farm ID
confirmNoSet to true to commit, false for dry-run preview
quantityYesThe number of eggs collected (positive finite number <= 100000)
idempotencyKeyNoUnique request idempotency key

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already disclose readOnlyHint=false, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds the dry-run/commit behavior and idempotency validation as traits, which is useful context beyond the annotations, but the dry-run semantics are already spelled out in the confirm parameter's schema description, so the added value is modest.

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?

Two short sentences with the core action front-loaded and no filler. It is appropriately sized, though the second sentence is somewhat vague ('idempotency validation' without specifics).

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 7-parameter mutation tool with no output schema, the description covers the commit/dry-run and idempotency mechanics but omits required-field expectations, default date behavior, and differentiation from the egg-collection reading sibling. Adequate but leaves real gaps an agent must infer from the schema.

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 parameter (including date, type, confirm, and idempotencyKey) is documented in the schema itself. The description names only 'confirm' and 'idempotency' generically and adds no format, defaults, or constraints beyond what the schema already provides, 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 ('Log') and resource ('daily egg collection count'), so an agent knows exactly what operation it performs. It does not, however, distinguish itself from the sibling read tool get_egg_collections or mention how it relates to log_bird_batch/log_hatch_set style logging tools.

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?

There is no when-to-use guidance, no mention of prerequisites (farmId required), and no routing to alternatives such as get_egg_collections for reading existing counts. The only usage signal is an implicit dry-run/commit split, which is not framed as guidance.

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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