mcp-get-installed-apps
インストールされたアプリを入手する MCP サーバー
コンピューターにインストールされているアプリケーションに関する情報を提供するモデル コンテキスト プロトコル (MCP) サーバー。MacOS と Windows をサポートします。
プロジェクト紹介
Get Installed Apps MCP Serverは、AIアシスタントがコンピュータにインストールされているアプリケーションを検出できるようにするシンプルなMCP実装です。このサーバーはモデルコンテキストプロトコル(MCP)仕様を実装しており、互換性のあるAIクライアントとのシームレスな統合を可能にします。
Related MCP server: XcodeBuildMCP
特徴
コンピュータにインストールされているアプリケーションの包括的なリストを返します
MCP 対応の AI クライアントとの簡単な統合
依存関係を最小限に抑えた軽量実装
プロジェクト構造
mcp-get-installed-apps
├── src
│ ├── index.ts # Application entry point
├── tsconfig.json # TypeScript configuration file
├── package.json # npm configuration file
└── README.md # Project documentationインストール
git clone https://github.com/Xutaotaotao/mcp-get-installed-apps.git
cd git-mcp-server
npm install
npm run buildMCP構成
インストール後、MCP JSON 構成で Get Installed Apps MCP を構成できます。
{
"mcpServers": {
"get-installed-apps": {
"command": "node",
"args": [
"mcp-get-installed-apps/build/index.js"
]
}
}
}AIクライアントでMCP JSONを構成する
クロード クライアント: https://modelcontextprotocol.io/quickstart/user
Raycast: MCPプラグインのインストールが必要です
カーソル: https://docs.cursor.com/context/model-context-protocol#configuring-mcp-servers
MCPツールの説明
インストールされたアプリの取得 MCP サーバーは、MCP プロトコルを通じて呼び出すことができる次のツールを提供します。
インストールされたアプリを取得する (get-installed-apps)
コンピューターにインストールされているすべてのアプリケーションのリストを返します。
パラメータ:
なし
戻り値:
成功: インストールされているすべてのアプリケーションのJSONデータを含むテキストコンテンツ
失敗: エラー情報を含むテキストコンテンツ
Available Tools
1 toolget-installed-appsB
Get my computer's installed apps
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
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 mentions 'Get' which implies a read operation, but fails to describe critical traits like whether this requires permissions, what format the output is in, if it's paginated, or any rate limits. For a tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It is front-loaded with the core purpose, making it easy for an AI agent to quickly understand the tool's function without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not explain what the return values are (e.g., list format, data structure) or address behavioral aspects like permissions or errors. For a tool that retrieves system data, this leaves significant gaps in understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the inputs. The description does not need to add parameter information, and it appropriately avoids redundancy. A baseline of 4 is applied since no parameters exist, and the description does not introduce confusion.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get my computer's installed apps' clearly states the action (get) and resource (installed apps) with a specific scope (my computer). It's not tautological with the tool name 'get-installed-apps' and provides a meaningful purpose. However, without sibling tools, the differentiation aspect is not applicable, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool, such as prerequisites, alternatives, or exclusions. It simply states what the tool does without context for usage, which is a significant gap in helping an AI agent make informed decisions.
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 tool update
v1.0.0- First observed
get-installed-apps
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as retrieving installed apps from the computer, making it distinct by default.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'get-installed-apps' follows a clear verb-noun pattern, and there are no other tools to compare it against for inconsistency.
A single tool is too few for most server purposes, as it limits functionality and suggests a thin or incomplete surface. While it might be appropriate for a very narrow scope, it generally feels insufficient for typical agent workflows.
The server's domain appears to be managing installed apps, but with only a 'get' operation, there are significant gaps. Missing operations like install, uninstall, update, or search apps make the surface incomplete and likely to cause agent failures for broader tasks.
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