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MaCalculatriceEnLigne

Calculate Caffeine Half Life

calculate_caffeine_half_life
Read-onlyIdempotent

Calculate remaining caffeine in body after time elapsed. Returns: {hours_to_below_25mg, safe_to_sleep}. See list_bundles for related 'sante' calculators.

Input Schema

NameRequiredDescriptionDefault
hours_sinceYesHours since consumption
mg_consumedYesCaffeine consumed mg

Output 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).
sourceNoAuthoritative source for the rule or formula (e.g. "Article 197 CGI", "NF DTU 21").
formulaNoHuman-readable formula or method used (e.g. "I=P·r·t", "Magnus formula").
reference_urlNoLink to a calcul2 page documenting the calculation in detail.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered by structured data. The description adds the output shape (hours_to_below_25mg, safe_to_sleep), which is useful behavioral context, but omits the underlying assumption (e.g. an average half-life, inter-individual variation) that determines the result.

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

Conciseness4/5

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

Two short sentences, with the core purpose front-loaded and no padding. The enumerated return fields partially duplicate the existing output schema, and the list_bundles pointer is mild promotional filler, but both are brief enough not to bloat the definition.

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

Completeness4/5

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

For a pure two-parameter calculator with a declared output schema and full annotation coverage, the definition supplies everything needed to invoke it correctly. The missing piece is sibling differentiation against the other caffeine calculators, which matters less for invocation than for selection.

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%, with both hours_since and mg_consumed explicitly documented in the schema, so the baseline is 3. The description adds nothing beyond the schema, such as units for hours or whether hours_since is capped, so it does not exceed the baseline.

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 gives a specific verb and resource ('Calculate remaining caffeine in body after time elapsed'), so the computing action is unambiguous. However, it does not distinguish itself from the two closely related sibling tools calculate_caffeine_clearance and calculate_caffeine_intake, leaving the agent to infer the boundary between them.

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?

The only usage-adjacent sentence is a pointer to 'list_bundles for related sante calculators', which is a directory cross-reference rather than when-to-use/when-not guidance. It never states when to pick this over calculate_caffeine_clearance, calculate_caffeine_intake, or calculate_sleep_cycles.

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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