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Pricing Strategy Calculator

pricing_strategy_calculator

Pricing Strategy Calculator — Model a price change before you commit: see the maximum volume you can lose and stay profitable, plus predicted demand and profit at a chosen elasticity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
newPriceYes
elasticityYes
currentPriceYes
fixedCostsMoYes
monthlyUnitsYes
variableCostPerUnitYes

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries full responsibility for disclosing behavior. It explains that the tool simulates outcomes (max volume loss, demand, profit), implying a read-only computational nature. However, it does not explicitly state it is side-effect-free, nor does it mention any assumptions or limitations of the model. For a calculator, this is adequate but not rich.

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?

The description is a single, front-loaded sentence that efficiently communicates the core value. The only redundancy is starting with the tool name/title, which is unnecessary given the title field, but it does not detract significantly from conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters, no schema descriptions, no output schema, and no annotations, the description is too sparse. It provides high-level outputs but fails to explain required inputs or calculation assumptions. An agent cannot confidently invoke this tool without additional clarification.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and there are 6 parameters, so the description must compensate by explaining inputs. It mentions only 'chosen elasticity,' leaving currentPrice, monthlyUnits, variableCostPerUnit, fixedCostsMo, and newPrice unexplained. This is insufficient for an agent to correctly map user requests to 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 states a specific action ('Model a price change') and resource ('price change') with clear outputs ('maximum volume you can lose and stay profitable, plus predicted demand and profit at a chosen elasticity'). It distinguishes itself from sibling calculators by focusing on elasticity-based demand modeling and profit preservation, not just generic pricing.

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 phrase 'before you commit' clearly conveys when to use the tool (when deciding on a price change), but it does not mention alternatives or exclusions. While context is obvious, the lack of explicit 'when not to use' or naming sibling tools prevents a 5.

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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