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limam-B
by limam-B

cleanup_processes

Kill orphaned ML-Agents and Unity processes blocking training ports after crashes or when force_training fails, freeing resources for new runs.

Instructions

Kill orphaned mlagents-learn and Unity build processes that are not tracked by any active run. Use this when force_training fails due to leftover processes occupying ports, or after a crash.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It reveals this is a destructive operation ('Kill') while reassuring safety via the 'not tracked by any active run' qualifier. It could add more about irreversibility or permissions, but the core behavior is transparent.

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 sentences, no filler. The destructive action and scope are front-loaded, followed by concrete trigger conditions. Every word earns its place.

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?

This is a zero-parameter tool with an output schema and a clear, self-contained description. The agent knows what the tool does, when to invoke it, and what safety condition ensures it will not affect active runs. Nothing essential is missing.

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?

The tool has zero parameters and schema coverage is trivially 100%. There is nothing for the description to add semantically, so the baseline of 4 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?

Description states a specific verb ('Kill') and resource ('orphaned mlagents-learn and Unity build processes'), with a precise scope ('not tracked by any active run'). It clearly differentiates this from the sibling training/run-management 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?

Explicitly states when to use the tool: when force_training fails due to leftover processes occupying ports, or after a crash. It does not mention when not to use it or list alternatives, but the provided usage context is clear and actionable.

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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