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Recipe Scaler / Serving Size Calculator

recipe_scaler_calculator

Recipe Scaler / Serving Size Calculator — Scale any recipe up or down: enter the original and desired servings, and this tool multiplies every ingredient by the exact ratio so amounts stay balanced.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ingredientsYes
desiredServingsYes
originalServingsYes

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description must disclose behavior itself. It states that every ingredient is multiplied by the exact ratio, which covers the core behavior. However, it does not mention rounding, precision, or how the output list is presented, leaving some behavioral details undisclosed.

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, information-dense sentence that immediately communicates the tool's purpose and mechanism. Every phrase adds value, with no filler or redundancy.

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?

The tool has no output schema and moderate complexity. The description explains inputs and the scaling algorithm but does not describe the return format or any edge-case behavior (e.g., rounding, unit handling). For a calculator, the output is fairly predictable, but the absence of explicit return information is a minor gap.

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 0%, but the description names 'original and desired servings' and refers to 'every ingredient', directly mapping to all three parameters. It does not explain the ingredient object structure (qty, unit, name), but the schema already defines those fields clearly.

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 uses the specific verb 'Scale' with the resource 'any recipe' and explicitly states the core function: multiplying every ingredient by the ratio of desired to original servings. This clearly distinguishes it from sibling tools like baking_pan_conversion_calculator or recipe_cost_calculator.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: enter original and desired servings, and the tool scales all ingredients. It does not explicitly mention alternatives or exclusions, but the use case is unambiguous for a serving-size scaling tool.

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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TDQS

B3.1/5.0
Disambiguation2/5

Many calculators occupy overlapping conceptual spaces, such as 'ai_roi_calculator' vs 'ai_automation_payback_calculator' and 'llm_self_host_vs_api_calculator' vs 'ai_build_vs_buy_calculator'. The boundaries between debt payoff, savings goal, and drawdown tools are also fuzzy, making it easy for an agent to select the wrong tool despite detailed descriptions.

Naming Consistency5/5

Every tool follows the same <topic>_calculator pattern with lowercase snake_case, making the naming highly predictable and consistent. Even acronyms and numbers fit the pattern, so there is no mixing of conventions.

Tool Count1/5

122 tools is an extreme number for a single MCP server, far exceeding the 50+ threshold for a severe mismatch. The tools span unrelated domains like AI costs, pet food, concrete, pizza dough, and turkey cooking, creating an unfocused kitchen-sink surface that overwhelms an agent's selection process.

Completeness3/5

The set covers many common calculator categories such as finance, construction, health, and AI costs, but several staple calculators are missing (e.g., BMI, tip, discount, simple interest, currency conversion). The AI cost cluster is over-saturated while other everyday calculations are absent, leaving minor but noticeable gaps.

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