dividend_reinvestment_calculatorDRIP Dividend Reinvestment Calculator — See how reinvested dividends compound your portfolio. Enter dividend yield, price growth, and yearly contributions to project future value and total growth.
| Name | Required | Description | Default |
|---|---|---|---|
| years | Yes | ||
| principal | Yes | ||
| priceGrowthPct | Yes | ||
| dividendYieldPct | Yes | ||
| annualContribution | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits itself. It mentions the outcome ('project future value and total growth') but does not state assumptions like compounding frequency, whether the yield is annual, how contributions are timed, or any limitations. This leaves significant ambiguity for a financial projection tool.
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 two sentences and front-loaded with the tool name. It conveys essential information without unnecessary filler, though the opening phrase repeats the title. Overall it's efficiently structured.
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?
The tool has 5 required parameters, no output schema, and no annotations. The description does not explain the return format, assumptions, or edge cases. It provides only a high-level 'project future value' with insufficient detail for an agent to fully understand what the tool computes or returns.
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 three of the five parameters ('dividend yield, price growth, and yearly contributions') but doesn't clarify that yields and growth are percentages, what 'principal' means, or how the years parameter is used. The semantics are only partially explained.
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 what the tool does: it projects future value and total growth from reinvested dividends, using inputs like dividend yield, price growth, and yearly contributions. It is specific to DRIP scenarios and distinguishes from generic calculators like compound_interest_calculator, though it doesn't explicitly name alternatives.
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 establishes clear context: 'See how reinvested dividends compound your portfolio' tells the agent this is for modeling DRIP growth. It lists the key inputs to provide, which serves as guidance on how to use it, but it doesn't state exclusions or explicitly compare to sibling calculators.
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.