take_home_pay_calculatorTake-Home Pay Calculator (US) — Estimate your US take-home pay for 2026. Enter gross salary and filing status to see federal income tax, Social Security, Medicare, and monthly net income.
| Name | Required | Description | Default |
|---|---|---|---|
| filingStatus | Yes | ||
| grossAnnualSalary | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description lists the specific tax components and net income, but it does not disclose that state taxes and pre-tax deductions are excluded. Since no annotations are provided, the description carries the full burden, and the omission of these limitations is a transparency gap.
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 with the purpose front-loaded. No redundant words or filler.
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?
The description covers the main purpose and outputs, but it fails to mention important limitations like state tax exclusions and does not provide an output schema. For a simple calculator, this is adequate but not fully complete without annotations.
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 mentions 'gross salary and filing status' but adds no extra meaning beyond the parameter names; it does not explain the filing status enum values, salary constraints, or that 'gross salary' is annual (though the parameter name implies it).
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 estimates US take-home pay for 2026, listing specific inputs (gross salary, filing status) and outputs (federal income tax, Social Security, Medicare, monthly net income). The verb 'Estimate' and resource 'US take-home pay' are specific, and the scope distinct from sibling calculators like salary_to_hourly_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 tells the user when to use it (to estimate US take-home pay for 2026) and what inputs are needed. It does not explicitly mention alternatives or exclusions, but the context is sufficiently clear that an agent could infer when this tool is appropriate.
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.