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tresor4k

macalc

calculate_carbon_sequestration

Estimate CO2 sequestration by trees over their lifetime, returning annual and lifetime carbon capture, and equivalent cars off the road. Input tree species, age, and count.

Instructions

Estimate CO2 sequestration by trees over their lifetime. Returns: {age_factor, annual_kg_co2_per_tree, annual_kg_co2_total, lifetime_kg_co2, lifetime_tonnes_co2, equivalent_cars_off_road_1yr}. See list_bundles for related 'astronomie-nature' calculators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tree_typeYesSpecies of tree
age_yearsYesAge of the trees in years
countNoNumber of trees (default 1)

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?

No annotations are provided, so the description must disclose behavior. It states it estimates and returns values, implying a read-only calculation. However, it does not explicitly mention that it does not modify data or require permissions, which is acceptable for a calculator.

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?

Two sentences effectively cover purpose, output description, and pointer to related tools. No unnecessary words. Highly concise and well-structured.

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?

Given a simple 3-parameter calculator with an output schema partially described in the description, it is mostly complete. It could mention underlying assumptions or data sources, but the pointer to related calculators adds context.

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?

All parameters are described in the input schema (100% coverage). The description adds the return fields beyond the schema, but does not enhance parameter understanding further. Baseline 3.

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 the tool estimates CO2 sequestration by trees over their lifetime, specifying the return fields. This gives a solid purpose, but it does not explicitly differentiate from numerous sibling calculators beyond mentioning a related bundle.

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 guidelines on when to use this versus alternative tools. The only hint is a pointer to 'list_bundles' for related calculators, but no explicit context or exclusion criteria.

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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