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tresor4k

macalc

calculate_reading_time

Input word count and optional reading speed to get estimated reading time in hours and minutes.

Instructions

Estimate reading time for a text based on word count. Returns: {hours_minutes}. See list_bundles for related 'education' calculators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
word_countYesNumber of words in text
reading_speed_wpmNoReading speed words per minute

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?

No annotations are provided, so the description must fully disclose behavior. It only mentions the return format {hours_minutes} but omits details like rounding, maximum word count, or behavior for edge cases (e.g., very large word_count). The description is minimal.

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 with two sentences. The first sentence states the purpose directly, and the second provides a helpful cross-reference. No superfluous information.

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?

The tool is simple with an output schema, so the description's mention of {hours_minutes} is adequate. However, given the large number of sibling tools, the description lacks guidance on when this tool is the best choice, which reduces its completeness for an AI agent.

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?

Input schema has high description coverage (100%), so the schema already explains both parameters clearly. The description does not add any additional semantics beyond 'based on word count', which is already implied. A baseline score of 3 is appropriate.

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 estimates reading time based on word count, with a verb and resource. However, it does not differentiate itself from the many sibling 'calculate_*' tools, as it only suggests seeing list_bundles for related education calculators without specifying when to use this tool over others.

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

Usage Guidelines2/5

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. The mention of list_bundles is a minor hint but does not provide clear context or exclusion criteria for selecting this tool among similar calculate tools.

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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