deck_boards_calculatorDeck Board Calculator (Linear Feet & Boards) — Calculate how many deck boards you need. Enter deck length, width, board width, gap, and board length to get area, linear feet, board count, and total cost.
| Name | Required | Description | Default |
|---|---|---|---|
| gapIn | Yes | ||
| wastePct | Yes | ||
| deckWidth | Yes | ||
| deckLength | Yes | ||
| boardWidthIn | Yes | ||
| boardLengthFt | Yes | ||
| pricePerBoard | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations were provided, so the description carries the burden. It discloses the calculation behavior and outputs but omits the wastePct and pricePerBoard parameters as required inputs. There is no contradiction, but the behavior is not fully transparent regarding all inputs.
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 sentence that front-loads the tool's purpose and lists inputs/outputs. It is concise and readable, though omitting two required parameters is a notable gap that prevents a perfect score.
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?
With no output schema, the description lists expected outputs (area, linear feet, board count, total cost) but doesn't explain how waste influences results. The 7 required parameters are not fully documented; the description covers 5 of 7. This is a moderate level of completeness for a calculator with this complexity.
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 no parameter descriptions (0% coverage), so the description must compensate. It explains deck length, width, board width, gap, and board length, but omits wastePct and pricePerBoard, which are both required. This partial coverage adds some meaning but leaves key parameters undocumented.
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 deck board quantities, listing specific inputs and outputs. The verb 'calculate' and the resource 'deck boards' distinguish it from sibling calculators like flooring or fence calculators.
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?
No explicit when-to-use or alternative guidance is provided. However, the title and description make the tool's purpose unambiguous for deck board estimation, implying usage context. Sibling tools like flooring_calculator could overlap, but no exclusions are stated.
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.