Skip to main content
Glama
deenesjakoruzh
mcp-for-dev

MCP Server for Google Search

by mcp-for-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Google 検索用の MCP サーバー

Google カスタム検索 API とウェブページ コンテンツ抽出機能を使用してウェブ検索機能を提供するモデル コンテキスト プロトコル サーバー。

ツール

検索

Google カスタム検索 API を使用して Web 検索を実行します。

  • ウェブ全体または特定のサイトを検索する

  • 結果の制御番号（1～10）

  • タイトル、リンク、スニペットで構造化された結果を取得します

ウェブページリーダー

任意の Web ページからコンテンツを抽出します。

  • ウェブページのコンテンツを取得して解析する

  • ページのタイトルと本文を抽出

  • スクリプトとスタイルを削除してコンテンツを整理する

  • タイトル、テキスト、URLを含む構造化データを返す

Related MCP server: MCP Google Custom Search Server

インストール

Google APIキーと検索エンジンIDを取得する

  1. Google Cloud プロジェクトを作成します。

    • Google Cloud Consoleに移動

    • 新しいプロジェクトを作成するか、既存のプロジェクトを選択してください

    • プロジェクトの課金を有効にする

  2. カスタム検索 API を有効にする:

    • APIライブラリへ移動

    • 「カスタム検索API」を検索

    • 「有効にする」をクリックします

  3. APIキーを取得:

    • 資格情報へ移動

    • 「認証情報を作成」>「APIキー」をクリックします

    • APIキーをコピーする

    • （オプション）APIキーをカスタム検索APIのみに制限する

  4. カスタム検索エンジンを作成する:

    • プログラム可能な検索エンジンへ

    • 検索したいサイトを入力してください（一般的なウェブ検索にはwww.google.comを使用してください）

    • 「作成」をクリック

    • 次のページで「カスタマイズ」をクリックします

    • 設定で「ウェブ全体を検索」を有効にします

    • 検索エンジンID（cx）をコピーする

クライアント構成

Claude Desktop で使用するには、Google API 認証情報を使用してサーバー設定を追加します。

MacOS の場合: ~/Library/Application Support/Claude/claude_desktop_config.json Windows の場合: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "google-search": {
      "command": "npx",
      "args": ["-y", "@mcp-for-dev/mcp-google-search"],
      "env": {
        "GOOGLE_API_KEY": "your-api-key-here",
        "GOOGLE_SEARCH_ENGINE_ID": "your-search-engine-id-here"
      }
    }
  }
}

Available Tools

2 tools
read_webpageA

Fetch and extract text content from a webpage

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the webpage to read

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description must cover behavioral traits. It only states basic purpose without mentioning rate limits, authentication, dynamic content handling, or error responses.

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?

Single sentence, front-loaded with action, no unnecessary words. Perfectly concise.

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?

For a simple tool with one parameter and no output schema, the description is adequate but lacks details on handling of large pages, timeouts, or what 'text content' entails (e.g., stripping HTML).

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?

Schema coverage is 100% (single 'url' parameter described), so baseline is 3. The description adds no extra meaning beyond the schema, such as URL format or protocol support.

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 clearly states the action ('Fetch and extract') and the resource ('text content from a webpage'), distinguishing it from sibling tool 'search' which is for querying.

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?

No explicit guidance on when to use this tool versus alternatives. The sibling 'search' suggests a different purpose, but the description does not clarify contexts or exclusions.

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. 2 tool updatesv1.0.0
    • Addedgoogle_search
    • Addedread_webpage
Deploy Server
Give agents eyes on the webHosted screenshot MCP. Send a URL, get a pixel-perfect image. $0.002 per grab. Works with Claude, Cursor, and Codex.grabbit.liveAd

TDQS

A3.5/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one performs web searches, the other extracts text from a specific URL. There is no overlap or ambiguity.

Naming Consistency5/5

Both tool names follow the same verb_noun pattern with snake_case (google_search, read_webpage), making them predictable and consistent.

Tool Count4/5

With only two tools, the set is minimal but covers the core search workflow. It avoids unnecessary bloat, though additional search variants could be justified.

Completeness4/5

The surface covers the basic search-then-read workflow. Missing features like pagination or filtered searches are minor gaps, but the essential path is complete.

Resources

Related MCP Connectors

Related MCP Servers

  • MCP Google Server

    adenot
    A
    license
    B
    quality
    D
    maintenance
    Provides web search capabilities using Google Custom Search API, enabling users to perform searches through a Model Context Protocol server.
    2
    255 npm
    69
    MIT

  • MCP Google Custom Search

    limklister
    A
    license
    B
    quality
    D
    maintenance
    A Model Context Protocol server that enables LLMs to perform web searches using Google's Custom Search API through a standardized interface.
    1
    47
    MIT

Appeared in Searches