roth_ira_calculatorRoth IRA Growth Calculator — See how a Roth IRA grows tax-free. Enter your annual contribution, expected return, and time horizon to project future value, total paid in, and growth.
| Name | Required | Description | Default |
|---|---|---|---|
| years | Yes | ||
| annualReturnPct | Yes | ||
| annualContribution | Yes | ||
| capitalGainsTaxPct | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It clearly discloses the tool's behavior by stating it projects future value, total paid in, and growth, and emphasizes tax-free growth. However, it doesn't mention limitations like assumptions or inflation, which could be noted.
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 efficient sentence that leads with the purpose. It wastes no words, but could be slightly clearer about all required inputs.
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 4 required parameters, no output schema, and no annotations, the description is under-specified. It omits one required parameter, does not describe the output structure, and provides no assumptions or limitations for a financial projection.
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 gives meaning to annual contribution, expected return, and years, but completely omits the required capitalGainsTaxPct parameter. Units are also unclear (e.g., whether annualReturnPct is a percent or decimal).
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 a specific verb+resource: 'See how a Roth IRA grows tax-free' and identifies the tool as a calculator. It clearly differentiates this from siblings by naming the Roth IRA and its unique tax-free growth feature.
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 by listing required inputs ('annual contribution, expected return, and time horizon'), but it does not explicitly state when to use this calculator versus other retirement calculators or mention any alternatives or exclusions.
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.