concrete_calculatorConcrete Calculator (Cubic Yards & Bags) — Calculate how much concrete you need for a slab, footing, or pad. Enter length, width, and thickness to get cubic yards, cubic feet, and the exact bag count.
| Name | Required | Description | Default |
|---|---|---|---|
| widthFt | Yes | ||
| lengthFt | Yes | ||
| wastePct | Yes | ||
| bagYieldFt3 | Yes | ||
| thicknessInches | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries some burden. It discloses the inputs and outputs but does not describe important behavioral details, such as how wastePct affects the calculation, whether bag count is rounded up to whole bags, or any underlying assumptions. The behavior is predictable for a simple calculator, but key nuances are missing.
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 and front-loaded with the title and purpose, but the first sentence largely repeats the title verbatim, creating slight redundancy. Still, it is two sentences with no unnecessary detail, making it easy to scan.
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 this is a 5-parameter calculator with no output schema and no annotations, the description provides a basic understanding of inputs and outputs but misses critical parameter semantics (waste and bag yield) and does not specify unit conventions beyond what parameter names imply. It is minimally sufficient but leaves room for agent confusion.
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 explain parameters. It mentions length, width, and thickness, but omits wastePct and bagYieldFt3, which are required inputs. The parameter names are somewhat self-explanatory, but the description does not clarify these advanced inputs, leaving a significant gap for the agent.
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 calculates concrete volume and bag count for slabs, footings, or pads, with a specific verb ('calculate') and resource ('how much concrete you need'). It distinguishes itself from sibling calculator tools by focusing on concrete-specific outputs (cubic yards, cubic feet, bag count).
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 implies when to use this tool: for any concrete slab, footing, or pad calculation. It clearly defines the input scenario (enter length, width, thickness) but does not explicitly exclude alternatives or mention when not to use it, which is acceptable given the calculator context.
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.
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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.