roi_calculatorROI Calculator with Annualized Return — Calculate ROI and annualized return on any investment. Add fees and costs to see true net gain and the real annual growth rate behind the headline number.
| Name | Required | Description | Default |
|---|---|---|---|
| years | Yes | ||
| finalValue | Yes | ||
| additionalCosts | Yes | ||
| initialInvestment | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It adds context that fees/costs can be included to reveal net gain and real annual growth rate, which goes beyond the schema. However, it does not disclose edge cases, whether compounding is assumed, how additionalCosts are applied (e.g., deducted from final value or added to initial investment), or the output format. Some behavioral context is present but insufficient.
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 compact and front-loaded, with two sentences. However, the first sentence repeats the title ('ROI Calculator with Annualized Return') and the second sentence restates the same idea. It is concise but slightly redundant, so it loses a point for efficiency.
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 moderate complexity (4 numeric params, no output schema), the description provides a high-level purpose but omits critical details like calculation formula, output structure, and precise meaning of additionalCosts. It is adequate for a basic understanding but incomplete for reliable invocation without further inference. The lack of annotations and output schema increases the need for richer description, which is only partially met.
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 mentions 'fees and costs' which likely maps to additionalCosts, but it does not explain initialInvestment, finalValue, or years in any detail. The parameter names are self-explanatory, but the description does not clarify units, whether additionalCosts are cumulative or annual, or how they are used in the calculation. This leaves significant ambiguity for a 4-parameter tool.
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 states the tool calculates ROI and annualized return on any investment, with a specific focus on incorporating fees and costs to show net gain and true growth rate. This distinguishes it from sibling calculators like ai_roi_calculator (AI-specific) and roas_calculator (ad spend). The verb 'Calculate' and resource 'investment' are explicit.
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 guidance on when to use this tool versus alternatives. It says 'any investment,' which implies general applicability, but it does not mention sibling tools like ai_roi_calculator or break_even_calculator, nor does it state exclusions or preferred scenarios. The agent is left to infer usage from the generic description.
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.