topsoil_calculatorTopsoil Calculator (Yards & Weight) — Work out how much topsoil you need for a bed, lawn, or garden. Enter area and depth to get cubic yards, cubic feet, weight in tons, and the exact bag count.
| Name | Required | Description | Default |
|---|---|---|---|
| areaSqFt | Yes | ||
| bagSizeFt3 | Yes | ||
| depthInches | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently specifies what the calculator returns: 'cubic yards, cubic feet, weight in tons, and the exact bag count.' It does not mention potential edge cases or limitations, but for a straightforward calculator, this level of transparency is adequate.
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, efficient sentence. However, it redundantly repeats the title 'Topsoil Calculator (Yards & Weight)', which slightly wastes space. Overall, it is concise and front-loaded with the core purpose.
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?
The description lists the expected outputs but omits one of the three input parameters (bagSizeFt3). It also lacks explicit units for area and depth. Given the tool's simplicity and lack of output schema, the description is incomplete but not severely so, as the schema parameter names provide partial context.
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 description coverage is 0%, so the description must compensate for parameter meanings. It mentions 'area and depth' but does not specify units (sq ft, inches) or the required bag size parameter (bagSizeFt3), which is essential for 'exact bag count.' The parameter names in the schema provide some clarity, but the description adds little beyond that.
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 function: 'Work out how much topsoil you need' and specifies the resource (topsoil) plus the outputs (cubic yards, cubic feet, weight in tons, bag count). It is distinct from sibling calculators like mulch_calculator or gravel_calculator by explicitly focusing on topsoil, making its purpose unambiguous.
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 description provides clear context for when to use the tool ('for a bed, lawn, or garden') and outlines the input steps ('Enter area and depth'). However, it does not mention exclusions or alternative tools, such as using mulch_calculator for mulch, so it stops short of the highest score.
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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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.
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