llc_vs_sole_proprietor_calculatorLLC vs Sole Proprietor Calculator — Compare a sole proprietorship with an LLC: formation and annual state costs, self-employment tax on both, and the estimated savings of an S-corp election.
| Name | Required | Description | Default |
|---|---|---|---|
| netProfit | Yes | ||
| sCorpElection | Yes | ||
| annualStateFee | Yes | ||
| stateFilingFee | Yes | ||
| payrollAdminCost | Yes | ||
| reasonableSalary | Yes | ||
| registeredAgentFee | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the comparison scope (formation/annual costs, self-employment tax, S-corp savings), which is useful, but it does not describe the output format or computational assumptions/limitations, leaving some ambiguity about what the agent should expect.
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 with a title prefix; every phrase adds meaningful information. It is front-loaded and free of redundancy.
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 7-parameter calculator with no output schema and no annotations, the description offers a solid high-level overview but omits details on computation assumptions, output structure, and conditional parameter dependencies (e.g., when sCorpElection is false). It is adequate for initial selection but not fully complete for a complex financial tool.
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 coverage is 0%, and the description compensates by grouping parameters into conceptual categories (formation/annual costs, taxes, S-corp elements). It does not explicitly map each parameter or explain less obvious ones like reasonableSalary or payrollAdminCost, though parameter names are largely self-explanatory.
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 identifies the exact resources (sole proprietorship vs LLC) plus the key dimensions: formation/annual costs, self-employment tax, and S-corp savings. It clearly distinguishes this from sibling calculators like contractor_vs_employee_calculator by focusing on business structure comparison.
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 clearly sets the context for comparing two business structures, implying when an entrepreneur would use it. However, it does not explicitly mention alternative tools or exclusions, so it stops short of full usage guidance.
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.