Skip to main content
Glama
falkenslab
by falkenslab

Reset a lab

lab_reset
DestructiveIdempotent

Reset a practice lab by removing its containers, networks, and volumes, then restarting it; files in its folder remain. Confirm with the user first.

Instructions

Back to a clean start: the lab's containers, networks and volumes are removed (what was done inside them is lost; the files in its folder stay) and it starts again. Confirm with the person first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labYesThe lab's name, as list_labs gives it

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
labYes
servicesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior5/5

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

The annotations already flag destructiveHint and idempotentHint, and the description goes further by naming exactly what is destroyed (containers, networks, volumes), what data is permanently lost (what was done inside them), and what survives (files in the lab folder). That is precisely the extra context annotations cannot convey.

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?

Two compact sentences with the effect front-loaded ('Back to a clean start'), followed by the blast radius in a parenthetical and the confirmation requirement last. Every clause conveys information an agent or operator needs — none is filler.

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?

An output schema exists, so return values need no explanation, and the annotations cover the safety profile. The description covers the mutation's data-loss semantics thoroughly; the only omission is any indication of post-reset state for callers or failure modes, which is minor here.

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 single parameter's description already explains that it takes the lab name as returned by list_labs. The description adds no further detail about the parameter (no format, no validation notes), so the schema carries the load and the baseline of 3 applies.

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?

The description states a specific operation on a specific resource with precise scope: containers, networks and volumes are removed and the lab starts again. That level of detail makes the tool's effect unambiguous. It never names a sibling (lab_down, lab_up) to distinguish itself, which is the only thing keeping it from a 5.

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?

It gives an explicit operator prerequisite — 'Confirm with the person first' — which is genuine when-to-use guidance for a destructive tool. It does not, however, contrast this with alternatives such as lab_down or stopping the lab, so the agent must infer when a full reset is preferred over a plain stop.

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