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Destroy Experiment

destroy_experiment

Terminate a sandboxed experiment and reclaim CPU/memory, receiving a final report of changes for summarization.

Instructions

Destroy a sandbox and everything in it, returning a final report.

USE THIS as soon as an experiment has told you what you needed. Always call it -- a sandbox left running keeps consuming the developer's CPU and memory.

IDEMPOTENT: calling it on an already-destroyed experiment is safe and returns the stored report rather than an error.

RETURNS the final report, including what changed inside the sandbox, so you can summarise the experiment after it is gone.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
experiment_idYesThe experiment to destroy.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
reportNo
statusYes
experiment_idYes
jobs_cancelledNo
snapshot_removedNo
already_destroyedNo
container_removedNo

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the destructive nature ('destroy everything'), idempotency (safe on already-destroyed), and return behavior (final report). This adequately informs an agent of side effects and safety, though it does not mention reversibility or permissions, which are implied.

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?

The description is front-loaded with the primary action, then a clear usage directive, idempotency note, and return info. Each sentence adds value with no fluff, and the capitalized section labels make it easily scannable for an agent.

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?

With an output schema present, return format details are covered. The description provides purpose, usage trigger, idempotency, and return contents, which is fully sufficient for an agent to decide when and how to call the tool correctly. Nothing essential is missing.

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?

The schema already describes the single parameter (experiment_id) at 100% coverage. The description adds no additional parameter-specific semantics beyond what the schema provides—only idempotency context that is behavioral, not parameter-related. Thus, baseline 3 is appropriate.

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 specific action (destroy), the resource (a sandbox/experiment), and the effect ('everything in it'). It distinguishes from siblings like cancel_job and get_experiment by emphasizing final cleanup and returning a report, making its unique purpose unambiguous.

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?

The description explicitly instructs 'USE THIS as soon as an experiment has told you what you needed' and emphasizes 'Always call it' to free CPU/memory, giving a clear trigger condition. It does not mention specific alternatives or when not to use it, but the context is clear execution is for final cleanup.

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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