contractor_vs_employee_calculator1099 vs W-2 Rate Calculator — Compare a W-2 job to 1099 contract work: enter salary, employer FICA, benefits, and PTO to see the equivalent 1099 gross rate you need to break even fairly.
| Name | Required | Description | Default |
|---|---|---|---|
| ptoValue | Yes | ||
| w2Salary | Yes | ||
| benefitsValue | Yes | ||
| contractorGross | Yes | ||
| employerFicaPct | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral details. It mentions four of five required inputs but omits contractorGross entirely and does not describe the output or calculation assumptions, leaving a significant gap in transparency.
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 that front-loads the purpose and lists the main inputs. It is concise with no redundant content.
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?
Despite being a moderately complex calculator with five required parameters and no output schema or annotations, the description is too brief. It fails to document the contractorGross parameter or the returned value, making it insufficient for an agent to invoke correctly without further inference.
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 parameter descriptions are absent (0% coverage), so the description must compensate. It explains salary, employer FICA, benefits, and PTO, but not contractorGross, and does not specify whether employerFicaPct is a percentage or decimal, leaving key ambiguity.
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 identifies the tool as comparing W-2 and 1099 work and computing the equivalent break-even 1099 gross rate. This specific verb+resource combination distinguishes it from sibling calculators like salary_to_hourly_calculator or employee_cost_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 provides clear usage context: use when comparing a W-2 job to 1099 contract work, with specific inputs. It does not explicitly name alternatives or exclusions, but the intended scenario is unambiguous.
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.