retirement_monte_carlo_calculatorRetirement Monte Carlo Calculator — Estimate your retirement plan's success probability with 5,000 simulated Monte Carlo market paths, plus a fan chart of percentile balances at every age.
| Name | Required | Description | Default |
|---|---|---|---|
| retireAge | Yes | ||
| currentAge | Yes | ||
| planUntilAge | Yes | ||
| volatilityPct | Yes | ||
| currentSavings | Yes | ||
| monthlyContribution | Yes | ||
| expectedRealReturnPct | Yes | ||
| monthlyRetirementSpending | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It does so by stating the simulation count, the metric (success probability), and the visual output (fan chart). This gives the agent a concrete sense of what will be computed and returned.
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?
A single sentence that fronts the calculator's purpose, then elaborates with the two key output features. Every phrase contributes without 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 complex retirement simulation tool, the description covers the core output and simulation method but omits input semantics, assumptions, and any limitations. Given the rich schema (though undocumented) and complexity, a bit more context would improve completeness.
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 has 8 parameters with 0% description coverage, and the description provides no parameter-specific meaning beyond the self-explanatory names. It doesn't clarify units, scales, or assumptions, leaving the agent to rely solely on parameter names.
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?
Description specifies a concrete action ('Estimate retirement plan success probability') with distinguishing details (5,000 Monte Carlo paths, fan chart of percentile balances), clearly differentiating it from sibling retirement calculators.
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?
No explicit alternative comparisons or when-not-to-use guidance. The 'retirement plan' context implies use for probabilistic retirement projections, but the description doesn't mention simpler calculators or exclusions.
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.