pto_accrual_calculatorPTO Accrual Calculator — Calculate how much paid time off you accrue each pay period from your annual PTO days, then project your future balance by pay date after the hours you've used.
| Name | Required | Description | Default |
|---|---|---|---|
| usedHours | Yes | ||
| annualDays | Yes | ||
| hoursPerDay | Yes | ||
| periodsElapsed | Yes | ||
| periodsPerYear | Yes | ||
| currentBalanceHours | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavior disclosure. It states the calculation and projection functions but does not disclose details like rounding, handling of partial periods, or what happens with invalid inputs. Basic behavior is clear, but no extra context is provided.
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 that front-loads the tool's name and purpose. No extraneous content; it conveys the core functionality without wasteful prose.
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 tool has 6 required parameters and no output schema, the description only provides high-level context for some parameters and omits the project mechanics entirely. It does not explain the relationship between inputs or what the output will look like, leaving gaps for the user.
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%, and the description only explicitly references 'annual PTO days' (annualDays), 'pay period' (periodsPerYear), and 'hours you've used' (usedHours). It fails to explain currentBalanceHours, hoursPerDay, and periodsElapsed, leaving users to infer their roles.
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 a specific verb ('Calculate') and resource ('PTO accrual'), and further describes projecting future balance. It is unambiguous and distinct from the many other calculators in the sibling list, as it specifically addresses PTO accrual.
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 ('how much paid time off you accrue each pay period') but does not explicitly state alternatives or exclusions. The context is clear enough for common use, but no direct comparison to sibling tools is provided.
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.