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plan_flock

Read-onlyIdempotent

Calculate chicken/quail flock sizing, incubation guidelines, weekly feed, spaces, and costs for any of the 310 avian breeds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoMode to size by: "household" (by people size) or "target" (by targeted eggs per week). Defaults to "target" if positive targetEggs is given, otherwise "household".
climateNoOptional climate winters: "mild", "cold", or "hot"
eggUnitNoUnit of target eggs: "chicken" or "quail". Defaults to "chicken".
purposeNoFlock purpose: "eggs", "meat", or "both". Defaults to "eggs".
speciesNoDesired species: "chicken", "quail", or "mix". Defaults to "mix".
experienceNoOwner poultry experience level: "beginner", "some", or "experienced". Defaults to "beginner".
quailShareNoQuail percentage of the flock: 0 to 1 (e.g. 0.50 for 50%). Defaults to 0.50.
targetEggsNoTarget eggs per week (used when mode="target").
householdSizeNoHousehold size (range 1-50, used when mode="household"). Defaults to 4.
meatBirdsPerMonthNoOptional meat birds to raise/process per month
spaceAvailableSqFtNoOptional available square feet to validate if flock plan fits

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already establish that this is a safe, read-only, idempotent, open-world calculation with no destructive effects. The description adds domain scope and enumerates output categories, but it does not disclose assumptions, calculation limits, or how breed-level coverage works despite mentioning '310 avian breeds'.

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 a single front-loaded sentence with no wasted words. It states the calculation scope and output categories compactly.

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 an 11-parameter optional-input calculation tool, the schema and annotations already carry most operational detail, and the description broadly communicates what the tool returns even without an output schema. The main gap is that it mentions 310 avian breeds while the schema exposes no breed parameter, which could create a slight mismatch in expectations.

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 the schema already documents all 11 parameters, their enums, defaults, and mode-dependent behavior. The description adds no parameter-level meaning beyond the schema, which is the baseline 3 for fully covered parameters.

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 names a specific verb ('Calculate') and scope ('chicken/quail flock sizing, incubation guidelines, weekly feed, spaces, and costs'), clearly distinguishing it from sibling logging, retrieval, and pairing tools. An agent can tell this is a planning/calculation tool without opening the schema.

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 description explains what the tool computes but gives no when-to-use guidance, no conditions for choosing it over alternatives, and no exclusions. Unlike operational siblings such as log_eggs or get_bird_batches, the intended planning scenario is only implied by the tool name.

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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