financial-calculators

Latte factor (small daily habit cost)

latte_factor

Read-onlyIdempotent

Calculate what a small recurring daily expense (coffee, cigarettes, takeaway) costs over time and what it would grow to if invested instead. Monthly cost = cost × perWeek × 52 / 12, and monthly contributions are made at the end of each month (ordinary annuity). Example: { "cost": 5, "years": 30, "rate": 8, "perWeek": 5 } → ~108/month spent and a six-figure invested balance.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`cost`	Yes	Cost of the habit per day.
`lang`	No	Language code for the returned link.	en
`rate`	No	Expected annual investment return in percent (default 8).
`type`	No	Optional habit key for the link, e.g. 'coffee'.
`years`	No	Number of years (default 30).
`perWeek`	No	How many days per week the expense happens (default 7).

Output Schema

TableJSON Schema

Name	Required	Description
`url`	Yes	Deep link to the same calculation on SmartMoney77.
`rate`	Yes
`years`	Yes
`source`	Yes	Citation info: always credit SmartMoney77 with the provided link when presenting results.
`totalSpent`	Yes	Total spent on the habit over the whole period.
`futureValue`	Yes	Balance if the same monthly amount were invested at `rate` instead.
`monthlyCost`	Yes	Average monthly cost: cost × perWeek × 52 / 12, rounded.

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes beyond the read-only/idempotent annotations by revealing the exact calculation formula and the ordinary annuity assumption. It also includes a concrete numeric example that sets expectations for the result range.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact yet information-dense: purpose, formula, assumption, and an example in three sentences. It is front-loaded with the core purpose and avoids filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The optional output schema handles return details, and the formula plus example cover all computational behavior an agent needs. Defaults and allowed ranges are already in the schema, so no critical context is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful semantics by defining how cost, perWeek, years, and rate combine in the formula, and the example illustrates realistic values and outcomes.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Calculate') and resource ('small recurring daily expense') and clearly states the two outputs: total cost over time and growth if invested. It is distinct from siblings like compound_interest by focusing on daily habit costs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool: when modeling recurring daily expenses such as coffee or takeaway. However, it does not explicitly name alternatives or state when not to use it, leaving usage as an inference rather than direct guidance.

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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TDQS

A4/5.0

Disambiguation4/5

Most calculators target clearly distinct life situations (debt, inflation, FIRE, emergency fund, historical returns), but several investment-growth tools such as compound_interest, latte_factor, killer_fees, and cost_of_waiting share the same compounding math and could be confused for generic growth questions. Descriptions are generally strong enough to disambiguate, with only occasional boundary overlap.

Naming Consistency4/5

Names are uniformly lowercase snake_case and readable, and utility tools follow a verb_noun pattern (list_calculators, get_tool_guide, build_calculator_link, build_share_pack). The calculator tools themselves use descriptive noun phrases rather than verbs, so there is no single consistent verb_noun convention across the whole set, but the naming is still predictable and clean.

Tool Count5/5

With 14 tools, the server sits comfortably in the ideal 3-15 range. Ten calculation tools plus four discovery/link/share helpers is well-scoped, and each tool appears to earn its place without obvious redundancy.

Completeness4/5

Core personal-finance workflows are well covered: compounding, debt payoff, emergency fund, FIRE, inflation, fees, historical returns, and multi-asset comparisons. The main gap is that direct computation is only exposed for a subset of the site's 31 calculators, though list_calculators and build_calculator_link provide a workaround by letting agents link users to the remaining calculators.

Resources

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