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cacheout-app

cacheout-mcp

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by cacheout-app

cacheout_smart_clean

Destructive

Frees disk space by clearing caches in priority order until target GB is met. Use for low disk space scenarios.

Instructions

Intelligently free disk space by clearing caches in priority order.

This is the PRIMARY tool for agents managing disk pressure. Specify how many GB you need freed, and the server clears the safest caches first:

  1. Build artifacts (Xcode DerivedData) — always regenerates

  2. Package manager caches (Homebrew, npm, pip) — re-downloads as needed

  3. Browser caches — rebuilds on browsing

  4. Docker (only if include_caution=true) — destructive, last resort

The server stops as soon as the target is met. Use dry_run=true to preview which categories would be cleaned and how much space would be freed.

Typical use: An agent detects low disk space (or needs room for swap/builds) and calls smart_clean(target_gb=10.0) to free 10 GB immediately.

Args: params: Target GB to free, dry_run flag, and caution inclusion.

Returns: str: JSON report with before/after disk state and what was cleaned. { "target_gb": 10.0, "target_met": true, "total_freed_human": "12.3 GB", "dry_run": false, "disk_before": {"free_gb": 5.2, ...}, "disk_after": {"free_gb": 17.5, ...}, "cleaned": [...], "skipped": [...] }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already mark destructiveHint=true. The description adds valuable context: the priority order of caches, that the server stops upon meeting the target, and the dry_run option. It does not contradict annotations and enhances understanding of side effects.

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?

The description is well-structured with a numbered priority list, example usage, and return format. It is front-loaded with the purpose. Minor redundancy could be trimmed, but overall efficient.

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?

The description explains the cleaning order, dry-run preview, and provides a sample return JSON. It covers most important aspects for an AI agent to use the tool effectively, though details about the 'cleaned' and 'skipped' fields are left to the output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description omits the 'free_memory' parameter, mentioning only target_gb, dry_run, and include_caution. The input schema has comprehensive descriptions for all four parameters, so the description adds little value and is misleading by omission.

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 tool frees disk space by clearing caches in priority order, with a specific verb and resource. It explicitly positions itself as the primary tool for disk pressure management, distinguishing it from siblings like cacheout_clear_cache and cacheout_scan_caches.

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 explains when to use the tool (low disk space, need room for builds/swap) and provides a typical use case. It does not explicitly state when not to use it or compare with alternatives, but the context is clear enough for an AI agent to decide.

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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