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Statistiques de l'organisation

ficiel_get_stats
Read-onlyIdempotent

AFFICHE UN ÉCRAN (tuiles et courbe). Les agrégats de la page Statistiques de Ficiel sur une période : conversations actives, avec message envoyé, avec réponse, qualifiées, en attente d'appel, rendez-vous pris, messages reçus et envoyés, suggestions utilisées, plus la série temporelle et les taux de réponse et de rendez-vous calculés comme dans l'interface. Qualifiées / en attente d'appel / rendez-vous pris comptent les contacts passés à l'étape SUR la période. La fenêtre ne remonte jamais avant l'inscription de l'organisation (activity_since) : l'historique importé plus ancien n'est pas de l'activité.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
granularityNoGranularité de la série temporelle.day
period_daysNoPériode en jours jusqu'à aujourd'hui (défaut 30, maximum 730 soit 24 mois).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), but the description adds genuinely non-obvious behavior: that a screen is rendered, that qualified/call-waiting/booked counts reflect contacts moved to the stage DURING the period, and that the window never extends before activity_since. Those counting and windowing rules materially affect how results must be interpreted.

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?

Front-loaded with the display action and the resource, followed by the metric inventory and then the interpretation rules. It is dense and only mildly redundant in enumerating ten metrics, but every clause carries meaning and nothing is buried at the end.

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?

With no output schema, the description carries the return-value burden and it does so by listing the aggregates and the computed rates. Combined with the annotation-profile and only two well-documented parameters, the definition is complete enough for correct invocation, though the exact return shape is still left implicit.

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%, so both parameters are already documented in the schema and the baseline is 3. The description references the period and time series but adds no syntax or format detail beyond what the schema provides.

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 verb+resource are clear ("AFFICHE UN ÉCRAN" / "Statistiques de l'organisation") and the description enumerates the exact aggregates returned, which distinguishes it well from the funnel, channel-specific and synthesis siblings. It stops short of naming any sibling explicitly, so it earns 4 rather than 5.

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?

Usage is implied (this is the org-wide statistics page over a period) but there is no explicit statement of when to prefer it over ficiel_get_stats_by_channel, ficiel_get_funnel or ficiel_synthese. The reader must infer the choice from the metric list.

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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