mulch_calculatorMulch Calculator (Cubic Yards & Bags) — Calculate how much mulch you need for a garden bed or landscape. Enter area and depth to get cubic yards, cubic feet, and the exact number of bags to buy.
| Name | Required | Description | Default |
|---|---|---|---|
| areaSqFt | Yes | ||
| wastePct | Yes | ||
| bagSizeFt3 | Yes | ||
| depthInches | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of explaining behavior. It states the outputs (cubic yards, cubic feet, bags) but does not disclose assumptions about waste percentage, bag size interpretation, rounding of bag count, or formula details. This is adequate for a simple calculator but leaves notable gaps.
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 two sentences with no wasted words. It front-loads the tool's name and immediately states the purpose and expected outputs, 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?
For a tool with 4 required parameters, no output schema, and no annotations, the description is incomplete. It does not explain all input parameters, the calculation approach, or what 'exact number of bags' means in terms of rounding. The context is too sparse for reliable tool invocation.
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. It mentions area and depth, but entirely omits wastePct and bagSizeFt3, which are required parameters. It also doesn't clarify units or the role of bag size in the calculation, leaving agents unable to infer the full parameter semantics.
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 uses a specific verb ('calculate') and resource ('mulch') with clear outputs: cubic yards, cubic feet, and number of bags. It distinguishes itself from sibling calculators like topsoil or gravel by focusing on mulch for garden beds/landscapes.
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 clearly indicates the use case ('for a garden bed or landscape') and the required actions ('Enter area and depth'). It does not explicitly mention alternatives or exclusions, but the scope is well-defined and distinct among the sibling calculators.
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.