flooring_calculatorFlooring Calculator (Boxes & Cost) — Figure out how many boxes of flooring to buy and what it costs. Enter room length, width, waste percentage, box coverage, and price per box for an estimate.
| Name | Required | Description | Default |
|---|---|---|---|
| widthFt | Yes | ||
| lengthFt | Yes | ||
| wastePct | Yes | ||
| pricePerBox | Yes | ||
| boxCoverageSqFt | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states it produces an 'estimate' but does not disclose whether it rounds up to whole boxes, how cost is calculated, or any limitations. The title hints at boxes and cost, but the description lacks behavioral specifics.
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 a single, front-loaded sentence that states the purpose and then lists the required inputs. Every word earns its place; no unnecessary detail.
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?
For a simple calculator, the description covers the main purpose and inputs, but it omits output details (e.g., rounded box count, total cost calculation) and does not differentiate usage from similar tools. With no output schema or annotations, a bit more context would be needed for full completeness.
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?
Schema property descriptions are empty (0% coverage), so the description must compensate. It lists all five inputs in natural language, which helps mapping, but adds no units or clarifications beyond what the parameter names imply. For example, it doesn't explain that wastePct is a percentage or that boxCoverageSqFt is square feet per box.
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's purpose: to figure out how many boxes of flooring to buy and the cost. It names the specific resource (flooring boxes and cost) and lists the inputs, distinguishing it from other generic calculators.
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?
The intended use is implied by the calculator name and purpose, but there is no explicit guidance on when to use this tool versus alternatives like tile_calculator. No exclusions or alternative references are provided.
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.