aquarium_stocking_calculatorAquarium Volume & Stocking Calculator — Work out your fish tank volume and stocking capacity. Enter length, width, and height in inches to get gallons, litres, inch-of-fish, and heater wattage.
| Name | Required | Description | Default |
|---|---|---|---|
| widthIn | Yes | ||
| heightIn | Yes | ||
| lengthIn | Yes | ||
| inchesPerGallon | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the outputs and inputs but does not explain the calculation method, assumptions, return format (e.g., JSON vs text), or any side effects. For a simple calculator, the behavior is mostly implied but not fully transparent.
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, well-structured sentence that front-loads the tool's name and purpose, lists inputs and outputs, and avoids unnecessary verbosity. Every element contributes to understanding the tool.
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 the simple calculator nature with 4 parameters and no output schema, the description is reasonably complete but misses key details: the meaning of 'inchesPerGallon' and the return format. It lists the outputs but doesn't specify how they are returned, which is important in the absence of an output schema.
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 description explains length, width, and height (in inches) but does not mention 'inchesPerGallon' at all. Since schema coverage is 0%, the description should compensate by explaining all parameters, but it leaves one key parameter (inchesPerGallon) unexplained, and its relationship to 'inch-of-fish' is only implicit.
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: calculating aquarium volume and stocking capacity. It specifies the input dimensions (length, width, height in inches) and the outputs (gallons, litres, inch-of-fish, heater wattage), making it distinct from other calculator tools.
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 the tool ('Work out your fish tank volume and stocking capacity') and provides clear context. It does not explicitly mention exclusions or alternatives, but given the unique domain among sibling calculators, the intended use is evident.
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.