Chrome 브라우저를 사용하여 Google 검색 및 웹페이지 콘텐츠 추출을 위한 MCP 도구입니다. Claude와 연동하여 Google 검색 및 콘텐츠 가져오기 기능을 활성화합니다.
Claude Desktop 구성
Mac에서 Claude Desktop 열기
Claude > 설정 > 개발자 > 구성 편집으로 이동하세요.
구성 파일에 다음을 추가하세요: GXP1
Claude Desktop을 다시 시작하세요
첫 번째 설정
접근성 권한 부여
첫 번째 실행 시 macOS 접근성 권한 승인 메시지가 표시됩니다.
시스템 환경 설정 > 보안 및 개인 정보 보호 > 개인 정보 보호 > 접근성으로 이동합니다.
터미널 앱에 대한 권한 추가 및 활성화
Apple 이벤트에서 Chrome JavaScript 활성화
크롬을 엽니다
이동: 보기 > 개발자 > Apple 이벤트에서 JavaScript 허용
한 번만 설정 가능
구성이 완료되면 Claude는 사용자가 요청할 때 Chrome을 통해 Google 검색을 수행하고 웹 페이지 콘텐츠를 추출할 수 있습니다.
Related MCP server: Google Search MCP
구글 검색 무료
작은 창을 열고 Chrome 브라우저를 사용하므로 차단되지 않습니다.
Chrome 창을 사용하므로 인증된 콘텐츠에 접근할 수 있습니다. Claude는 브라우저에서 URL을 열면 됩니다.
✅ 맥OS
❌ Windows (지원되지 않음)
❌ Linux (지원되지 않음)
맥OS
구글 크롬
Node.js 20 이상
npx mcp-chrome-google-searchgit에서 체크아웃
npm run build 실행하세요
Claude 구성에 추가(절대 경로 사용):
{
"google-tools": {
"command": "node",
"args": [
"/your/checkout/path/mcp/mcp-chrome-google-search/dist/index.js"
]
}
}로컬에서 변경 사항을 테스트하려면 package.json 버전을 올리고 편집 모드로 전환합니다.
npm install -g .그런 다음 npm run build 실행하면 파일이 claude가 모니터링하는 dist로 이동합니다.
그런 다음 Claude 데스크탑에서 Ctrl-R을 누르세요. 다시 시작할 필요가 없습니다.
# Follow logs in real-time
tail -n 20 -F ~/Library/Logs/Claude/mcp*.log개발자 설정 활성화:
echo '{"allowDevTools": true}' > ~/Library/Application\ Support/Claude/developer_settings.jsonDevTools 열기: Claude 데스크톱에서 Command-Option-Shift-i
더 나은 오류를 위해 꼬리를 달 때 Claude 데스크톱에서 ctrl-r을 사용하세요.
다음이 보이면:
execution error: Google Chrome got an error: Executing JavaScript through AppleScript
is turned off. For more information: https://support.google.com/chrome/?p=applescript (12)해결책:
크롬을 엽니다
보기 > 개발자 > Apple 이벤트에서 JavaScript 허용
Chrome 제어에 실패하는 경우:
시스템 환경 설정 열기
보안 및 개인정보 보호 > 개인정보 보호 > 접근성
터미널 앱이 나열되어 있고 활성화되어 있는지 확인하세요.
필요한 경우 잠금 아이콘을 사용하여 변경하세요.
Chrome 제어를 위해 AppleScript를 사용합니다.
눈에 보이는 자동화 - Chrome 창이 열리고 탐색됩니다.
각 요청은 새로운 Chrome 탭을 엽니다.
최적의 성능을 위해 사용하지 않는 탭을 주기적으로 닫으세요.
신뢰할 수 있는 Claude 인스턴스에서만 사용하세요(Chrome 제어 액세스 권한이 있음)
문제에 대한 GitHub 이슈를 생성합니다.
macOS 및 Chrome 버전 세부 정보 포함
MIT 라이센스 - 자세한 내용은 라이센스 파일을 참조하세요.
Extract readable text content from a webpage using Chrome browser automation.
Key Features:
Returns main content text and optionally links
| Name | Required | Description | Default |
|---|---|---|---|
| includeLinks | No | Whether to include extracted links in the output | |
| url | Yes | Webpage URL to fetch (must include http:// or https://) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions Chrome browser automation and returning main content text with optional links, but lacks critical behavioral details: it doesn't specify if this is a read-only operation, potential rate limits, authentication needs, error handling for inaccessible pages, or what 'readable text' entails (e.g., stripping HTML, handling dynamic content). The description adds some context but leaves significant gaps for a tool interacting with external webpages.
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 concise and well-structured with a clear opening sentence followed by a bullet point. Every sentence earns its place by stating the core purpose and a key feature. However, the bullet point format is slightly redundant with the main sentence, and it could be more front-loaded by integrating the optional links feature into the initial statement.
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 complexity of web scraping (external interactions, potential failures) and lack of annotations or output schema, the description is incomplete. It doesn't explain return values beyond 'main content text and optionally links', leaving the agent uncertain about output structure, error responses, or behavioral constraints like timeouts or permissions. For a tool with no structured safety hints, this is inadequate.
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?
Schema description coverage is 100%, with clear descriptions for both parameters in the schema itself. The description adds minimal value beyond the schema: it mentions 'optionally links' which aligns with the 'includeLinks' parameter but doesn't provide additional semantic context. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.
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 clearly states the tool's purpose: 'Extract readable text content from a webpage using Chrome browser automation.' It specifies the verb (extract), resource (text content from a webpage), and method (Chrome browser automation). However, it doesn't explicitly differentiate from its sibling 'web-search' tool, which likely searches rather than extracts content from a specific URL.
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 versus alternatives. It mentions 'optionally links' as a feature but doesn't clarify scenarios where including links is beneficial or when to choose this over 'web-search'. There's no mention of prerequisites, limitations, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search webpages and get a specific page of results (each page has ~10 results). Optionally filter by site and timeframe.
| Name | Required | Description | Default |
|---|---|---|---|
| pageNumber | No | Which page of results to fetch (1-5). Each page contains ~10 results | |
| query_text | Yes | Plain text to search for (no Google operators plain text only - use other parameters for site/date filtering) | |
| site | No | Limit search to specific domain (e.g. 'github.com' or 'docs.python.org') | |
| timeframe | No | Time range filter (h=hour, d=day, w=week, m=month, y=year) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context beyond basic functionality: it mentions pagination ('each page has ~10 results') and optional filtering capabilities. However, it doesn't cover important aspects like rate limits, authentication needs, error handling, or what the output looks like (e.g., result format), which are significant gaps for a search tool.
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 extremely concise and front-loaded: it states the core purpose in the first clause and adds key details in a second sentence. Every word earns its place, with no redundancy or fluff, making it highly efficient for quick understanding.
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 tool's moderate complexity (4 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the basic operation and filtering options but lacks details on output format, error cases, or integration with the sibling tool. Without annotations or output schema, more behavioral context would be beneficial for full completeness.
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?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by hinting at the optional nature of site and timeframe filters, but it doesn't provide additional semantic context or usage examples. This meets the baseline for high schema coverage.
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 clearly states the tool's purpose: 'Search webpages and get a specific page of results.' It specifies the verb ('search') and resource ('webpages'), and distinguishes it from the sibling tool 'web_fetch' by implying this is for search results rather than fetching specific pages. However, it doesn't explicitly contrast with the sibling, keeping it from 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 implied usage context by mentioning optional filters ('Optionally filter by site and timeframe'), which suggests when to use these parameters. However, it lacks explicit guidance on when to choose this tool over the sibling 'web_fetch' or any other alternatives, and doesn't specify prerequisites or exclusions, leaving room for ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0web_fetchweb-searchScored across 2 tools
The two tools have clearly distinct purposes: web_fetch extracts content from a specific webpage, while web_search performs web searches and returns result pages. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the task.
Both tool names follow a consistent snake_case pattern with a clear verb-noun structure: web_fetch and web_search. The naming is predictable and readable, with no deviations in style or convention.
With only 2 tools, the server feels thin for a Google Search domain, which typically involves more operations like advanced filtering, image search, or history management. While the tools cover basic fetch and search, the scope is limited and could benefit from additional functionality to fully represent the domain.
The tools cover core search and content extraction, but there are notable gaps for a Google Search server, such as no tools for image search, news search, or handling search settings. Agents can perform basic tasks but may encounter dead ends for more advanced operations, indicating incomplete coverage of the domain.
Google search, news, maps, scholar and public webpages for AI agents, with Markdown results.
Real Chrome for agents: start a browser, read pages as numbered markdown, click, type, hand off.
Web search, browser automation, scraping, crawling and CAPTCHA solving for AI agents.
Web search and page-reading for AI agents. One-click OAuth connect, or a Caesar API key.
