global_hiring_cost_calculatorGlobal Hiring Cost Calculator — Compare the annual cost of a US W-2 employee against an international contractor or an EOR hire, including employer taxes, benefits, and platform fees.
| Name | Required | Description | Default |
|---|---|---|---|
| eorFeeMo | Yes | ||
| usSalary | Yes | ||
| usBenefitsAnnual | Yes | ||
| eorEmployerBurdenPct | Yes | ||
| eorGrossSalaryAnnual | Yes | ||
| usOtherEmployerTaxes | Yes | ||
| intlContractorMonthly | Yes | ||
| contractorPlatformFeeMo | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It discloses the scope of the calculation (employer taxes, benefits, platform fees), which is useful. However, it does not mention assumptions, limitations, or that this is a read-only estimator. It also fails to describe what the result represents or how it should be interpreted.
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. It front-loads the tool's name and purpose, then uses a dash to expand on the comparison components. There is no redundancy or filler, making it appropriately sized for the information it conveys.
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's complexity (8 required parameters, no output schema, no annotations), the description is insufficiently complete. It does not explain what output the agent should expect, how the comparison is presented, or any key assumptions. The agent is left without guidance on how to interpret the result, making it hard to invoke confidently for a non-trivial calculator.
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 vaguely references 'employer taxes, benefits, and platform fees.' It does not explain the meaning or units of parameters such as usSalary, intlContractorMonthly, eorEmployerBurdenPct, or eorFeeMo. The parameter names are somewhat self-explanatory, but the description fails to compensate for the lack of schema descriptions, leaving the agent to infer how to populate all 8 required fields.
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 uses the specific verb 'Compare' and clearly identifies the resource: annual cost of a US W-2 employee against an international contractor or EOR hire. This specificity distinguishes it from sibling tools like contractor_vs_employee_calculator, which likely focuses on domestic comparison. The mention of 'global' and 'EOR' further differentiates it.
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 usage context: comparing three hiring options for annual cost. However, it does not explicitly state when to use this tool over alternatives or provide exclusions. For example, it doesn't say 'use this when analyzing international hires' or 'for domestic comparisons use contractor_vs_employee_calculator.' This is clear context but lacks explicit when/when-not guidance.
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.