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MaCalculatriceEnLigne

Calculate Knitting Yarn

calculate_knitting_yarn
Read-onlyIdempotent

Calculate yarn needed for a knitting project (meters and number of 50g/100m balls). Returns: {meters_of_yarn, balls_50g_100m}. See list_bundles for related 'textile-mode' calculators.

Input Schema

NameRequiredDescriptionDefault
sizeYes
projectYes

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

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds that results are expressed in meters and 50g/100m balls, but since an output schema exists this largely restates the structured data rather than disclosing new behavior.

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 tightly written sentences, front-loaded with purpose before the cross-reference. The explicit 'Returns: {...}' clause is mildly redundant given the output schema, but costs little.

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 simple two-parameter, closed-enum calculator with an output schema, the definition is largely sufficient. The gaps are minor: no parameter explanation and a vague bundle reference.

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?

Schema description coverage is 0%, so the description carries the burden for the two parameters, and it says nothing about them. The enum values (scarf/hat/sweater/blanket/socks, S/M/L) are self-documenting, but the description adds no meaning about what size refers to or how it affects the calculation.

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?

States a specific verb and resource ('Calculate yarn needed for a knitting project') and pins down the output units (meters and 50g/100m balls), which separates it from nearby textile siblings like calculate_fabric_needed and calculate_fabric_yarnage-style tools. An agent can identify the domain without opening the schema.

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?

It points at list_bundles for 'related textile-mode calculators', which is a weak routing hint rather than a when-to-use rule. There is no guidance on when to prefer this over calculate_fabric_needed or calculate_recipe_scaling, and 'textile-mode' is unexplained jargon. Usage is only implied.

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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