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Capucine

Je lis le bulletin

get_weather_context
Read-onlyIdempotent

Faits météo seulement. Ville/CP OU lat+lon. question= peut porter la ville. Pas de repli silencieux sur Paris.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
atNoISO 8601 or natural French time. Unreadable values return invalid_datetime.
latitudeNoLatitude, -90 to 90. Requires longitude.
locationNo
occasionNo
questionNo
longitudeNoLongitude, -180 to 180. Requires latitude.
outdoor_minutesNoMinutes outside, 0 to 180.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNo
limitsNo
sourceNo
messageNo
privacyNo
successYes
weatherNo
locationNo
timezoneNo
suggestionsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The description adds behavioral constraints beyond the annotations: 'Pas de repli silencieux sur Paris' (no silent fallback to Paris) and the 'OU' (OR) requirement between city/ZIP and lat/lon. Since annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, these extras meaningfully increase transparency.

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?

The description is extremely concise (two short sentences) and front-loaded with the core restriction ('Faits météo seulement'). Every phrase adds value, and there is no fluff or 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 that an output schema exists and annotations cover the safety profile, the description hits the essential constraints (facts only, location OR pairing, no fallback). It leaves out details on optional parameters like occasion and outdoor_minutes, but these have defaults and are not central to the tool's primary function, so the description is sufficiently complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With only 57% schema coverage, the description compensates by clarifying key parameters: 'Ville/CP' defines location, 'lat+lon' indicates the pairing requirement for latitude/longitude, and 'question= peut porter la ville' explains how the question parameter can carry location context. It does not cover 'at', 'occasion', or 'outdoor_minutes', but those are partially documented in the schema.

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 states a clear, specific purpose: 'Faits météo seulement' (weather facts only) and specifies the input constraints ('Ville/CP OU lat+lon'). This distinguishes it from sibling tools like recommend_weather_look, which imply recommendation logic, and makes its scope unambiguous.

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?

The description implies usage for weather facts only, but it does not explicitly name alternatives or state when not to use it. An agent can infer from 'Faits météo seulement' that recommendation tools are out of scope, but there is no direct pointer to a sibling, unlike the get_calls example that names search_calls_extensive.

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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