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Delete the room permanently

delete_room

Permanently delete a chat room and all its messages for every participant. Requires explicit approval from the person first to remove sensitive data from the server.

Instructions

Permanently delete the room and every message in it, for everyone. This cannot be undone and no backup is kept. Any participant can do this, not only whoever created the room. Never call it on your own judgement: ask the person first and wait for a clear yes, even when the conversation obviously looks finished. Worth offering when the room carried credentials, personal data or anything else that should not sit on a server for a month — otherwise leave_room is the usual way out.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
confirmYesMust be true, and only after the person has explicitly agreed. Not a formality.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.3.4

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so thoroughly. It reveals irreversibility, lack of backup, that any participant can perform the deletion, and that the action affects everyone in the room. This is exemplary transparency for a destructive operation.

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?

Every sentence earns its place. The description front-loads the critical destructive outcome, then covers authorization, consent, and alternative usage without unnecessary words. It is substantive yet compact.

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

Completeness5/5

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

For a one-parameter tool with no output schema and no annotations, the description is complete enough to enable correct invocation. It covers the action, its consequences, who can perform it, when to offer it, and when not to use it, leaving no critical gap for an agent.

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 coverage is 100%, so the baseline is 3, but the description adds meaningful context around the confirm parameter: it explains that confirm must follow explicit user agreement and cannot be assumed from context. This goes beyond the schema's 'Must be true' and reinforces the human-consent requirement.

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 clearly states the action: permanently delete the room and every message in it, for everyone. It goes beyond a generic verb by specifying the scope (all messages, all participants) and makes the destructive nature explicit, which differentiates it from siblings like leave_room.

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?

The description gives explicit usage guidance: never call it on your own judgment, ask first and wait for a clear yes, and prefer leave_room as the usual alternative. It also identifies a specific scenario where delete_room is worth offering, such as when the room contained credentials or personal data.

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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