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tresor4k

macalc

calculate_future_value

Compute the future value of a present sum for savings projections. Input present value, annual rate, and number of years to get future value and total interest.

Instructions

Compute the future value (FV) of a present sum at a given interest rate. Use for savings projections. Inputs: present value, annual rate %, years, compounding frequency. Returns FV and total interest. See list_bundles for related 'finance-universal' calculators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
present_valueYesPresent value EUR
rateYesAnnual rate percent
yearsYesNumber of years

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoComputed result. Object whose fields depend on the tool (e.g. {tax, marginal_rate, brackets} for tax tools, {volume_l, gallons} for volume tools).
formulaNoHuman-readable formula or method used (e.g. "I=P·r·t", "Magnus formula").
sourceNoAuthoritative source for the rule or formula (e.g. "Article 197 CGI", "NF DTU 21").
reference_urlNoLink to a calcul2 page documenting the calculation in detail.
Behavior3/5

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

With no annotations provided, the description carries full behavioral disclosure burden. It states 'Returns FV and total interest,' which gives output behavior but does not indicate side effects or other traits. No mention of read-only or destructive nature, but for a calculator tool, this is minimally adequate.

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

Conciseness3/5

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

The description is relatively concise—two sentences plus an input/output list. However, the inclusion of a false parameter ('compounding frequency') detracts from its conciseness and accuracy, making it less efficient.

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

Completeness3/5

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

Given the presence of an output schema (implied), the description's note on returns (FV and total interest) is sufficient. Parameters are explained in the schema. However, the misleading mention of a non-existent parameter leaves the description incomplete, missing other potential constraints or nuances.

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

Parameters2/5

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

The input schema already includes descriptions for all three parameters (present_value, rate, years) with 100% coverage. The description adds mention of 'compounding frequency,' which is not a parameter in the schema, creating confusion and contradicting the schema. This reduces clarity for the AI.

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 clearly states 'Compute the future value (FV) of a present sum at a given interest rate' with a specific verb and resource. It distinguishes itself from sibling tools by mentioning 'savings projections' and directs to related calculators via list_bundles, aiding differentiation among many financial tools.

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

Usage Guidelines4/5

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

The description advises 'Use for savings projections,' providing clear context. It references list_bundles for related calculators, suggesting alternatives. However, it lacks explicit when-not-to-use guidance or prerequisites.

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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