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tresor4k

macalc

calculate_logarithm

Compute logarithms in any base: natural log (base e), common log (base 10), or binary log (base 2). Input a positive number and optionally choose a base to get the logarithmic result.

Instructions

Calculate logarithm in any base (natural, common, binary). Returns: {result}. See list_bundles for related 'math' calculators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesValue to take log of
baseNoLog base: e=natural, 10=common, 2=binarye

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.
Behavior2/5

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

With no annotations, the description carries full burden. It only says 'Returns: {result}', omitting important details like input constraints (value must be positive), error handling, or output precision. The schema already provides the minimum, so the description adds minimal behavioral insight.

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 extremely concise (two sentences) with no wasted words. It front-loads the core purpose and includes a useful pointer to related tools.

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 tool's simplicity and full schema coverage, the description is minimally adequate. However, it lacks behavioral context like input restrictions (positive values) and does not leverage the output schema to explain the return format. Still meets basic needs.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. The description's mention of 'any base (natural, common, binary)' reinforces the enum but does not add new meaning beyond what the schema already describes.

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

Purpose4/5

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

The description clearly states it calculates logarithms in any base (natural, common, binary) and mentions the return format '{result}'. It provides a pointer to related calculators via list_bundles, helping differentiate from siblings.

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?

No explicit when-to-use or exclusions are given. The mention of list_bundles implies there are alternatives but does not specify when to prefer this tool over others.

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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