MaCalculatriceEnLigne

calculate_discount_effective

Compute the effective discount when stacking multiple promotions. Use for promo design or shopping comparisons. Inputs: original price, discount % list. Returns final price and effective single-discount equivalent. See list_bundles for related 'finance-universal' calculators.

Input Schema

TableJSON Schema

Name	Required	Description
`discount_1_pct`	Yes	First discount %
`discount_2_pct`	No	Second discount %
`original_price`	Yes	Original price

Output Schema

TableJSON Schema

Name	Required	Description
`result`	No	Computed result. Object whose fields depend on the tool (e.g. {tax, marginal_rate, brackets} for tax tools, {volume_l, gallons} for volume tools).
`source`	No	Authoritative source for the rule or formula (e.g. "Article 197 CGI", "NF DTU 21").
`formula`	No	Human-readable formula or method used (e.g. "I=P·r·t", "Magnus formula").
`reference_url`	No	Link to a calcul2 page documenting the calculation in detail.

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 13, 2026

TDQS

A3.9/5.0

Behavior3/5

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

No annotations are provided, so the description bears full burden. It mentions returning final price and effective single-discount equivalent, which is adequate for a calculation tool. No additional behavioral traits like side effects or permissions are disclosed, but none are expected.

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?

The description is three sentences, each serving a distinct purpose: purpose, usage, inputs/outputs, and sibling reference. It is front-loaded with the most important information and avoids redundancy.

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 the tool's simplicity, the description, along with the full schema and output schema, provides sufficient information for correct invocation. The description covers the essential use case and result.

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 coverage is 100% with each parameter described. The description lists inputs generically (original price, discount % list) but adds no extra meaning beyond what the schema provides. Baseline 3 is appropriate.

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?

The description clearly states the tool computes effective discount when stacking multiple promotions, for promo design or shopping comparisons. It distinguishes itself from sibling tools by referencing list_bundles for related calculators.

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides usage context (promo design or shopping comparisons) and directs to list_bundles for further related tools. It does not explicitly state when not to use, but the guidance is sufficient.

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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