product_pricing_calculatorProduct Pricing Calculator — Set the right selling price for your product. Enter fully-loaded unit cost — materials, labor, overhead, and platform fees — then choose a target margin.
| Name | Required | Description | Default |
|---|---|---|---|
| feesPct | Yes | ||
| overhead | Yes | ||
| materials | Yes | ||
| hourlyRate | Yes | ||
| laborHours | Yes | ||
| targetMarginPct | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for disclosing behavior. It conveys that this is a pure calculation tool (read-only, no side effects) by explaining inputs and the goal of setting a price. However, it does not disclose the output format, whether it handles edge cases, or any limitations beyond the schema constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with two clauses that avoid redundancy and front-load the tool's purpose. The em-dash style effectively separates the goal from the input instructions, making it scannable. Minor redundancy with the title ('Product Pricing Calculator') could be trimmed, but it adds useful context rather than fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given it's a calculator with 6 required parameters and no output schema, the description adequately conveys the core purpose and input categories but lacks critical details about the output (e.g., what value is returned, whether it includes a cost breakdown). The absence of annotations and output schema would require a more explicit statement of the result to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description maps most inputs to conceptual categories: materials, labor, overhead, platform fees, and target margin. However, it does not clarify the split between laborHours and hourlyRate (both needed for labor cost) or that feesPct is a percentage, leaving ambiguity. With 0% schema description coverage, the description only partially compensates by providing a high-level mental model.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool sets a product's selling price based on fully-loaded unit cost and target margin, using a specific verb ('Set') and identifying the resource (pricing). It distinguishes from generic calculators by emphasizing fully-loaded cost components, though it doesn't explicitly name sibling tools or differentiate itself from similar pricing calculators like margin_markup_calculator.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many calculator siblings (e.g., pricing_strategy_calculator, ecommerce_profit_margin_calculator), the description does not clarify use cases, prerequisites, or scenarios where another tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.