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Glama

Meeting Rooms

find-meeting-rooms
Read-onlyIdempotent

Search bookable meeting rooms in your organization by name, building, floor, or minimum capacity. Returns room details for adding as attendees to calendar events.

Instructions

Discover bookable meeting rooms in the user's organisation via the Graph rooms endpoint (read-only). Returns room resources with displayName, emailAddress, building, floor, capacity, and bookingType — suitable for piping into create-event as attendees. Filter by query (matches name/email), building, floor, or minimum capacity. Returns empty list on personal accounts (the rooms endpoint is M365-only). Use outputVerbosity to control field count.

Input Schema

NameRequiredDescriptionDefault
floorNoFilter by floor number
queryNoSearch query (room name, email)
buildingNoFilter by building name
capacityNoMinimum capacity required
outputVerbosityNoOutput detail level (default: standard)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv3.9.1
  2. Removedv3.9.0
  3. Changed1 schema field changedv3.7.4
    • addedInput schema / additionalProperties
      Added value: +false
  4. First observedv3.4.1

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/idempotentHint/non-destructive, so safety is covered. The description adds genuinely new behavioral context beyond the annotations: the M365-only limitation and empty-list return on personal accounts, plus the enumerated return fields and verbosity control.

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?

Three dense sentences, front-loaded with what the tool does and what it returns before the filter details. Slightly packed with backticked tokens, but each clause carries information and nothing is redundant padding.

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 compensates by enumerating the returned fields (displayName, emailAddress, building, floor, capacity, bookingType) and the edge case of personal accounts. Behavior and return shape are adequately covered for a zero-required-param read tool.

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 coverage is 100% and the schema descriptions already state the same semantics ('Search query (room name, email)', 'Minimum capacity required'). The description restates query-matches-name/email and capacity-as-minimum, adding little beyond what the schema carries, so baseline 3 applies.

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 specific verb ('Discover bookable meeting rooms'), names the underlying Graph rooms endpoint, and distinguishes itself from siblings by naming `create-event` as the downstream consumer. An agent can tell exactly what this returns and how it differs from people/event search tools.

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?

Gives clear filter semantics and an explicit when-not condition: 'Returns empty list on personal accounts (the rooms endpoint is M365-only).' It doesn't name a fallback tool for non-M365 users, but the usage context is otherwise unambiguous.

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