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Glama

watch_area

Monitors a whole area and notifies you when a new appointment slot opens with any practitioner in a chosen specialty, so you can book.

Instructions

Surveille TOUTE une zone : notification dès qu'un nouveau créneau apparaît chez n'importe quel praticien de la spécialité dans le rayon (via doctolib-mcp-watch, à planifier).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNo
lngNo
cityNo
daysNo
labelNo
radius_kmNo
specialityYes
sector_1_onlyNo
new_patients_onlyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior3/5

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

Annotations already disclose the mutation profile (readOnlyHint=false, destructiveHint=false, openWorldHint=false). The description adds useful behavioral context beyond that: the watch is area-wide and fires on newly appearing slots, and "à planifier" signals it is scheduled/asynchronous. It still omits what is required for the watch to function (a location source) and how notifications are delivered.

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?

A single tight sentence with the scope (area-wide watch) front-loaded before the trigger condition. The trailing parenthetical about doctolib-mcp-watch is slightly awkward but does not waste much space.

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

Completeness2/5

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

For a 9-parameter mutation tool with no output schema and an asynchronous mode, the description is thin: it never states the location requirement, the meaning of the scheduling parenthetical, or how the agent should follow up. An agent could invoke it, but only by guessing at most parameter semantics.

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

Parameters2/5

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

Schema description coverage is 0% for 9 parameters, so the description must carry the burden. It only indirectly signals speciality ("de la spécialité") and radius ("dans le rayon"); lat, lng, city, days, label, sector_1_only and new_patients_only are never mentioned, and no format or precedence (lat/lng vs city) is given.

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?

States a specific verb+resource ("Surveille TOUTE une zone") and the trigger event (notify on a new slot from any practitioner of the speciality in the radius). The capitalized "TOUTE" implicitly contrasts with the per-practitioner sibling watch_practitioner, but that sibling is never named, so the differentiation is inferred rather than explicit.

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

Usage Guidelines2/5

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

No when-to-use guidance, no prerequisites, and no routing to alternatives despite the presence of watch_practitioner, list_watches, run_watches_now and stop_watch. The parenthetical "(via doctolib-mcp-watch, à planifier)" hints at a deferred/scheduled mechanism but does not tell the agent when to pick this tool over its siblings.

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