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Prime rib calculator

prime_rib_calculator
Read-onlyIdempotent

How big a prime rib (standing rib roast) to buy for a guest count, roasting time, the pull temperature for the doneness you want, and the rest. Classic 350°F roast or reverse sear.

Input Schema

NameRequiredDescriptionDefault
boneNo
guestsYesNumber of guests
methodNo
appetiteNolight = plenty else on the table
donenessNo
leftoversNo

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 readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds real value beyond that by disclosing that the tool computes several derived values (weight, time, pull temp, rest) rather than a single number, though it says nothing about units or output formatting.

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?

Front-loaded and tight: the outputs are listed first and the two cooking methods come last. The phrase 'and the rest' is vague and the sentence is fragmentary rather than clearly structured, which costs a point.

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 six-parameter tool with only a third of parameters described and no output schema, the description leaves gaps: bone-in vs boneless sizing implications and the effect of leftovers on quantity are unaddressed. It is adequate to know what the tool does but not complete enough to use every option confidently.

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 only 33%, so the description should carry more weight than it does. It touches guest count, doneness, and cooking method implicitly, but says nothing about bone-in vs boneless, appetite, or leftovers, which are the parameters most in need of added meaning.

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 the specific resource (prime rib / standing rib roast) and enumerates the concrete outputs: how much to buy, roasting time, pull temperature, and rest duration. It is unmistakable against siblings like turkey_calculator or ham_calculator, which handle different proteins.

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?

Usage is only implied: an agent can infer this is for planning a prime rib roast for a guest count. There is no explicit when-to-use/when-not framing and no pointer to alternatives such as party_food_calculator or thanksgiving_timeline for broader meal planning.

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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