Skip to main content
Glama

get_availabilities

Read-only

Retrieve available appointment slots for a practitioner, by reason or practice, over N days, handling Doctolib's 15-day limit and providing booking links.

Instructions

Créneaux d'un praticien pour un motif, sur N jours (Doctolib limite à 15 j par appel : l'outil enchaîne).

Deux usages :

  • url_or_slug (+ motive_id / practice_id optionnels) : agendas résolus automatiquement ; sans motive_id, TOUS les motifs réservables sont interrogés.

  • motive_id + agenda_ids (+ practice_id) : appel direct (ids issus de search_practitioners). Renvoie slots, substitutes (créneaux assurés par un remplaçant), next_slot si rien dans la période, reason (agenda fermé / pas encore ouvert) et booking_url.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
motive_idNo
agenda_idsNo
start_dateNo
practice_idNo
url_or_slugNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover readOnly and openWorld, and the description adds genuinely non-structured behavior: the 15-day per-call API limit with automatic chaining, and the returned shape (slots, substitutes, next_slot, reason, booking_url). It omits any auth/rate-limit caveats beyond the chaining note, so it falls short of a 5.

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 one-line purpose, then a clean two-branch usage list and a return-value sentence. Dense but every clause carries information; the parenthetical 15-day note and return enumeration both earn their place.

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 tool with no output schema, the description supplies the return fields and the special cases (substitutes, next_slot, reason). It is self-sufficient for calling; the only shortfall is the undocumented start_date and unspecified date/number formats.

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?

Schema description coverage is 0%, so the description carries the full burden and largely delivers: it explains url_or_slug, motive_id, practice_id and agenda_ids roles and their interdependencies. start_date and the exact semantics of the days default are not addressed, leaving one gap in an otherwise strong compensation for the empty 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?

States a precise verb+resource: practitioner availability slots for a motive over N days, with the Doctolib 15-day chunking behavior spelled out. It also situates itself relative to search_practitioners (source of ids), so the agent can tell what it does and where its inputs come from.

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

Usage Guidelines5/5

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

Explicitly enumerates two invocation modes: url_or_slug-based auto-resolution (and the default of querying ALL bookable motives when motive_id is omitted) versus direct motive_id + agenda_ids calls. This is exactly when-to-use-which-pattern guidance with the branch conditions made explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.