paint_calculatorPaint Calculator: How Much Paint Do I Need? — Calculate exactly how much paint you need for any room. Enter room dimensions, number of coats, doors, windows, and paint coverage to get total liters and cans.
| Name | Required | Description | Default |
|---|---|---|---|
| coats | Yes | ||
| doors | Yes | ||
| windows | Yes | ||
| roomWidthM | Yes | ||
| roomLengthM | Yes | ||
| wallHeightM | Yes | ||
| includeCeiling | Yes | ||
| coveragePerLiterM2 | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the basic input/output behavior ('get total liters and cans') but lacks specifics about assumptions (e.g., how doors/windows are accounted for, whether includeCeiling is applied by default, rounding rules, or limitations).
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, two sentences, and front-loaded with the title phrase. The opening clause repeats the title ('Paint Calculator: How Much Paint Do I Need?') which is slightly redundant, but the rest is efficiently written and directly informative.
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 8 required parameters, no annotations, and no output schema, the description offers a basic overview of inputs and outputs but omits the includeCeiling parameter and any assumptions about area calculations. It is adequate for a simple calculator but not fully comprehensive.
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 groups parameters into 'room dimensions, number of coats, doors, windows, and paint coverage,' which adds meaning beyond raw property names. However, it fails to mention the includeCeiling parameter explicitly and does not clarify units or formulas for any parameter.
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 with an active verb and resource: 'Calculate exactly how much paint you need for any room.' It also specifies the scope ('for any room') and lists the inputs and outputs, making it unambiguous among sibling 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 gives clear context for when to use the tool by enumerating the required inputs and the expected output ('Enter room dimensions, number of coats, doors, windows, and paint coverage to get total liters and cans'). It does not explicitly provide exclusions or alternatives, but the context is sufficient.
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.