Skip to main content
Glama

mac-cleaner-mcp

一个通过官方 Model Context Protocol TypeScript SDK 暴露的保守型 macOS 开发者/存储清理工具。它不是完整的磁盘优化器:它先报告、拒绝危险的广泛删除位置,并支持 dry run(试运行)。

工具

  • scan_junk:DerivedData、iOS DeviceSupport、Gradle/Android 缓存、Homebrew 缓存、Node 包缓存、Docker 可回收空间、~/Library/Caches、较大的 Downloads 文件(>100 MB)以及发现的 node_modules。

  • clean_junk:接受类别,默认 dryRun: true。只有 DerivedData、DeviceSupport、Android/Gradle、Homebrew 和 Docker 可以被清理。Downloads、node_modules 和广泛的缓存根目录会被有意拒绝。

  • get_disk_usage:对所选路径运行 df。

Related MCP server: mac-disk-cleaner

安装与运行

需要 macOS、Node.js 20+,以及可选的 Docker。不要以 root 身份运行。

git clone https://github.com/FFFames/mac-cleaner-mcp.git
cd mac-cleaner-mcp
npm install
npm run build
node dist/index.js

MCP 使用 stdio,因此该进程应由 MCP 客户端启动。清理可能会删除文件;在将 dryRun 设为 false 之前,请检查 dry-run 输出并保留备份。

Claude Desktop / Cursor

在客户端的 MCP 配置中添加 dist/index.js 的绝对路径:

{"mcpServers":{"mac-cleaner":{"command":"node","args":["/ABSOLUTE/PATH/mac-cleaner-mcp/dist/index.js"]}}}

重启客户端,调用 scan_junk,然后使用所选类别和 dryRun: true 调用 clean_junk。仅在查看结果后才使用 false。Cursor 通过其 MCP 设置支持相同的 stdio MCP 服务器配置。

Poke / 远程桥接

对于本地 Poke 兼容桥接,请将此服务器作为子进程运行,并通过经过身份验证的 HTTPS 端点代理 MCP JSON-RPC stdin/stdout。一个简单的选择是在你自己的桥接进程前使用 SSH/HTTP 桥接或 ngrok:

ngrok http 8787

将桥接的上游命令配置为 node /ABSOLUTE/PATH/mac-cleaner-mcp/dist/index.js,然后使用你的 Poke/MCP 连接注册 HTTPS ngrok URL。切勿暴露未经身份验证的端点;请使用 ngrok auth token、允许调用者列表,并轮换 URL/token。清理器本身仅限本地运行,没有网络监听器。

安全性

该实现使用明确的类别允许列表、无 shell 通配符展开、默认 dry-run,并拒绝 Downloads、node_modules 和整个用户缓存根目录。使用前请审查源代码并在非关键账户上测试。

Available Tools

3 tools
clean_junkA

Safely remove selected caches. Always dry-run first.

NameRequiredDescriptionDefault
dryRunNo
categoriesYes

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description must disclose behavior on its own. It hints at safety with 'Safely' and the dry-run instruction, which implies this is a destructive operation that can be previewed. But it does not state what gets permanently destroyed, whether confirmation is needed, or any permission requirements.

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 short, front-loaded sentences deliver purpose and the critical safety tip with zero waste. The instruction is immediately actionable.

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

Completeness3/5

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

The tool is a simple mutation with two parameters and no output schema. The description covers the essential safety guidance but omits what happens after running (e.g., return value, confirmation prompts) and does not specify any prerequisites or side effects beyond the dry-run caution. It is adequate but not exhaustive.

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?

Schema description coverage is 0%, so the description should compensate, but it adds no parameter-specific information. The schema's enum values (e.g., 'derivedData', 'brew') are self-explanatory, and the default for dryRun is documented in the schema, but the description does not clarify how parameters interact or what the consequence of each selection is.

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 states a clear verb ('remove') and resource ('selected caches'), and the qualifier 'selected' signals user-controlled scope. It distinguishes this from scan_junk (which likely scans) and get_disk_usage (which measures usage) via the explicit removal action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The instruction 'Always dry-run first' provides an important safety guideline for usage. However, it does not explicitly mention when to use this tool versus alternatives (e.g., 'run scan_junk first to identify caches'), leaving that routing implicit.

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

get_disk_usageC

Report disk capacity and free space.

NameRequiredDescriptionDefault
pathNo/

TDQS

C2.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read-only operation ('report') but doesn't describe the output format, whether it scans the entire disk or just a mount point, or any side effects. This minimal disclosure is insufficient for a tool with no annotation coverage.

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 a single, concise sentence that fronts the core action. There is no fluff. However, its brevity omits essential context, making it minimal rather than well-structured; it earns its place but doesn't provide enough value.

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

Completeness2/5

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

For a tool with one optional parameter and no output schema, the description is incomplete. It fails to clarify whether the report covers the whole disk, a specific path, or what the output looks like. It doesn't explain how the path parameter affects results, leaving the agent without enough information to use the tool correctly.

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

Parameters1/5

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

Schema description coverage is 0%, so the description must compensate for the undocumented 'path' parameter. However, the description makes no mention of 'path' whatsoever. An agent receives no explanation of what the parameter does, its default value, or how it affects the report.

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 clearly states the tool's purpose: 'Report disk capacity and free space.' It uses a specific verb (report) and resource (disk usage), and the subject matter distinctly separates it from siblings scan_junk and clean_junk, which deal with junk files. However, it doesn't explicitly mention the path parameter or differentiate itself by name, so it falls short of 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 Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention the siblings or any conditions for when checking disk usage would be appropriate, leaving the agent to infer when this tool should be invoked.

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

scan_junkD

Scan macOS developer/system caches and reclaimable space.

NameRequiredDescriptionDefault
categoriesNo

TDQS

D1.8/5.0
Behavior2/5

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

With no annotations, the description must cover behavior. It says 'Scan' which implies a read-only operation, but it does not explicitly state that it does not modify anything, nor does it disclose what it returns (e.g., a list of caches, sizes). No permissions or side effects are mentioned, leaving significant behavioral ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, which is concise, but it is under-specified rather than efficiently detailed. It front-loads the verb 'Scan' but provides no useful structure or breakdown of capabilities. It is more an under-specification than a model of conciseness.

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

Completeness1/5

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

Given no annotations, no output schema, and a parameter with zero schema descriptions, the description is grossly incomplete. An agent cannot determine what the tool returns, how to use the categories parameter, or how this tool relates to cleaning and disk usage. Essential information is entirely absent.

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

Parameters1/5

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

The only parameter, 'categories', is an array with enum values, but the schema lacks any descriptions (coverage 0%). The description does not mention the parameter at all, so the agent has no idea what values to pass or what they control. Description fails to compensate for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the verb 'Scan' and the resource 'macOS developer/system caches and reclaimable space', which conveys a broad intent but is vague. It does not distinguish from sibling tools like clean_junk or get_disk_usage, and the phrase 'reclaimable space' is not a concrete resource. It is not a tautology, but it lacks specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as clean_junk or get_disk_usage. There is no mention of prerequisites, typical use cases, or exclusions. The agent is left to infer the tool's role in the workflow.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv1.0.0
    • First observedclean_junk
    • First observedget_disk_usage
    • First observedscan_junk

TDQS

B3.1/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clear, distinct purpose: scanning for junk, cleaning junk, and reporting disk usage. No overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (scan_junk, clean_junk, get_disk_usage), making them predictable and readable.

Tool Count5/5

With only 3 tools, the set is minimal but well-scoped for its intended purpose. The count is within the ideal 3-15 range and each tool contributes to the core workflow.

Completeness4/5

The set covers the essential lifecycle: scan to find junk, clean to remove it, and get_disk_usage for context. Minor gaps like granular junk category selection or detailed scan reports are absent but not critical.

Maintenance

ActivityStale
ResponsivenessNo issues

Related MCP Connectors

Related MCP Servers

  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables LLM agents to safely reclaim disk space by deleting build artifact directories like node_modules, .venv, and target, with strong guardrails to prevent accidental or malicious deletion.
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables natural-language management of macOS disk cleanup by scanning large files, AI model weights, and caches, identifying duplicates, and safely moving files to Trash or permanently deleting them with confirmation.
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Enables monitoring Linux RAM/swap and /tmp tmpfs pressure, detecting and safely sweeping orphaned development workers behind a protected denylist with dry-run safeguards, and removing age-gated stale sockets and scratch caches.
    5
    MIT
  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables AI agents to scan a developer machine for disk space hogs, audit git worktrees for stale or orphaned ones, re-check individual findings, and run allowlisted cleanup actions as dry runs unless explicitly confirmed. Risky items are reported with warnings and refused by default.
    1
    MIT