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Combien de données mobiles en Suisse

flytel_estimer_donnees
Read-onlyIdempotent

Estime la consommation de données mobiles en Suisse par mois à partir des usages (visioconférence, musique, vidéo, appels par application) et du nombre de jours par semaine en Suisse, avec les valeurs publiées par les éditeurs, et le forfait Flytel qui la couvre. Même calcul que l’estimateur de flytel.fr. À utiliser quand la personne décrit ses usages sans connaître sa consommation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
heures_par_jourYesHeures par jour en Suisse hors Wi-Fi, par usage. Clés possibles : appel-audio, visio-teams, visio-zoom, visio-zoom-groupe, musique, musique-haute, video-standard, video-hd.
jours_par_semaineYesJours passés en Suisse par semaine.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare this a safe, read-only, idempotent, non-open-world computation, so the safety profile is covered. The description usefully adds that the calculation matches flytel.fr and that a covering forfait is part of the result (valuable since there is no output schema), but it says nothing about the computation's assumptions elsewhere or precision/limits.

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 purpose is front-loaded in the opening clause and the routing condition closes the text; every sentence carries information. The first sentence is long and dense, stacking inputs and outputs together, which costs some readability but not correctness.

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

Completeness5/5

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

With only two fully documented parameters, strong annotations and no output schema, the definition covers what an agent needs: what it computes, from which inputs, and that the answer includes a recommended Flytel forfait. Nothing material is missing for correct invocation.

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%, including the nested per-usage hour keys, so the baseline is 3. The description's mention of visioconférence, musique, vidéo and appels par application loosely maps to those keys but adds no syntax, units or defaults beyond what the schema already states.

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 names a specific verb and resource ("Estime la consommation de données mobiles en Suisse par mois") and enumerates the drivers of the estimate (usages, jours par semaine). It goes further by promising the Flytel forfait that covers the estimate. It never names a sibling (e.g. calculate_monthly_price or check_usage_compatibility) to differentiate itself, so it stops short of a 5.

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?

"À utiliser quand la personne décrit ses usages sans connaître sa consommation" is an explicit when-to-use condition tied to the user's state of knowledge. There is no when-not guidance and no named alternative for the case where the user already knows their consumption, so it is clear context without exclusions.

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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