fertilizer_calculatorFertilizer Calculator (N-P-K Rate) — Work out how much fertilizer to spread: enter your lawn size, target nitrogen rate, and the bag N number to get pounds of product per feeding and per season.
| Name | Required | Description | Default |
|---|---|---|---|
| areaSqFt | Yes | ||
| nitrogenPct | Yes | ||
| targetNper1000 | Yes | ||
| numApplications | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of explaining behavior. It states the calculation outputs (pounds per feeding and per season) and implies a calculation based on inputs, but it does not disclose the underlying formula, assumptions, or how numApplications affects the seasonal total. It is a read-only calculator, so risk is low, but behavioral detail is minimal beyond the output statement.
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. It front-loads the tool name and immediately explains the function and expected outputs, with no filler or repetition. Every part adds value, making it highly concise.
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 with no output schema and no annotations, the description is mostly adequate: it explains the purpose, inputs, and outputs. However, it misses the numApplications parameter and does not specify calculation assumptions or the formula, leaving some ambiguity about how the 'per season' output is derived. It is sufficient for a basic understanding but lacks 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?
The schema has 0% description coverage, so the description must compensate. It maps three of four parameters (lawn size, target nitrogen rate, bag N number) but omits numApplications entirely. It also does not explain units (e.g., areaSqFt is square feet, targetNper1000 is per 1000 sq ft) or provide the formula connecting parameters. The parameter names are self-explanatory to some degree, but the description fails to fully bridge the coverage gap.
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 calculate fertilizer application amounts based on lawn size, target nitrogen rate, and bag nitrogen percentage. It uses a specific verb ('Work out how much fertilizer to spread') and names inputs and outputs, distinguishing it from the many sibling calculators. It also clarifies the output ('pounds of product per feeding and per season') which fully defines the scope.
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 usage context by telling the user what to enter and what they will receive. However, it does not explicitly state when not to use this tool or mention alternative calculators, though the domain-specific wording implies the appropriate scenarios. The context is solid but lacks explicit exclusions or alternatives.
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.
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.