pay_raise_calculatorPay Raise Calculator — Calculate your pay raise two ways: the nominal percentage increase and the real, inflation-adjusted raise, plus the annual and per-paycheck dollar gain.
| Name | Required | Description | Default |
|---|---|---|---|
| newSalary | Yes | ||
| oldSalary | Yes | ||
| inflationPct | Yes | ||
| payPeriodsPerYear | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the core calculation outputs and the inflation-adjusted aspect, which is useful. However, it does not detail assumptions, units, or limitations (e.g., how inflationPct is interpreted), leaving some opacity for a calculator tool.
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, focused sentence that conveys the primary outputs without unnecessary detail. It slightly wastes characters by repeating 'Pay Raise Calculator' from the title, but overall it is concise and front-loaded.
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 four-parameter calculator with no output schema, the description gives a reasonable overview but misses key context like parameter meanings and output formatting. It could be more complete by specifying how inputs map to the two calculations, especially since annotations are absent.
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 for the four required parameters. It only hints at inflationPct via 'inflation-adjusted' and does not explain oldSalary, newSalary, payPeriodsPerYear, or the units/formats. This is insufficient given the lack of schema descriptions.
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 pay raises in two distinct ways (nominal percentage and inflation-adjusted), plus annual and per-paycheck dollar gains. This specific verb+resource+scope distinguishes it from sibling calculators like salary_to_hourly_calculator or take_home_pay_calculator.
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 its usage for pay raise calculations but does not explicitly state when to use it versus alternative calculators. No alternatives are mentioned, but the purpose is clear enough for an agent to infer basic applicability, though exclusions or conditions are absent.
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.