프로젝트 참고 : 이 MCP 서버 구현은 브라우저 사용/웹 UI 기반을 기반으로 합니다. 핵심 브라우저 자동화 로직 및 구성 패턴은 원래 프로젝트에서 가져온 것입니다.
자연어 브라우저 제어를 위한 모델 컨텍스트 프로토콜(MCP)을 구현하는 AI 기반 브라우저 자동화 서버입니다.
🧠 MCP 통합 - AI 에이전트 통신을 위한 전체 프로토콜 구현
🌐 브라우저 자동화 - 페이지 탐색, 양식 작성 및 요소 상호 작용
👁️ 시각적 이해 - 스크린샷 분석 및 비전 기반 상호 작용
🔄 상태 지속성 - 작업 간 브라우저 세션 유지
🔌 다중 LLM 지원 - OpenAI, Anthropic, Azure, DeepSeek 통합
Related MCP server: Cloudflare Playwright MCP
Python 3.11 이상
uv(빠른 Python 패키지 설치 프로그램)
크롬/크로미엄 브라우저
MacOS의 경우: ~/Library/Application\ Support/Claude/claude_desktop_config.json Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json
지엑스피1
"browser-use": {
"command": "uv",
"args": [
"--directory",
"/path/to/mcp-browser-use",
"run",
"mcp-server-browser-use"
],
"env": {
...
}
}# Install dev dependencies
uv sync
# Run with debugger
npx @modelcontextprotocol/inspector uv --directory . run mcp-server-browser-use브라우저 충돌 : 시작하기 전에 Chrome 인스턴스를 모두 닫으세요.
API 오류 : 환경 변수의 API 키가 LLM 공급자와 일치하는지 확인하세요.
시각 지원 : 스크린샷 분석을 위해 MCP_USE_VISION=true 설정합니다.
서버는 환경 변수를 통해 여러 LLM 공급자를 지원합니다. MCP_MODEL_PROVIDER 에 사용 가능한 옵션은 다음과 같습니다.
공급자 | 값 | 필수 환경 변수 |
인류학적 |
|
|
오픈AI |
|
|
Azure 오픈AI |
|
|
딥시크 |
|
|
쌍둥이자리 |
|
|
미스트랄 |
|
|
올라마 |
|
|
오픈라우터 |
|
|
선택 사항으로 표시된 엔드포인트의 경우 지정하지 않으면 기본값이 사용됩니다.
온도는 MCP_TEMPERATURE 사용하여 구성할 수 있습니다(기본값: 0.3)
모델은 MCP_MODEL_NAME 사용하여 지정할 수 있습니다.
Ollama 모델의 경우 num_ctx 및 num_predict 와 같은 추가 컨텍스트 설정을 구성할 수 있습니다.
이 프로젝트는 MIT 라이선스에 따라 브라우저 사용/웹 UI를 확장합니다. 브라우저 자동화 프레임워크를 개발한 원저자분들께 특별히 감사드립니다.
MIT - 자세한 내용은 라이센스를 참조하세요.
Handle run-browser-agent tool calls.
| Name | Required | Description | Default |
|---|---|---|---|
| add_infos | No | ||
| task | Yes |
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 but fails completely. It doesn't indicate whether this is a read or write operation, what side effects it might have, what permissions are required, or what the expected behavior is. The phrase 'Handle... tool calls' is too vague to convey any meaningful behavioral information.
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?
While the description is technically concise (only 5 words), this represents under-specification rather than effective brevity. The single sentence doesn't earn its place by providing meaningful information. A truly concise description would still convey essential purpose and usage information in minimal words, which this fails to do.
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 a tool with 2 parameters, no annotations, no output schema, and 0% schema description coverage, the description is completely inadequate. It provides no information about what the tool does, how to use it, what parameters mean, or what to expect from its operation. The description fails to compensate for any of the missing structured information.
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 schema description coverage is 0%, meaning neither parameter has any description in the schema. The tool description provides no information about what the 'task' or 'add_infos' parameters mean, what format they should take, or how they affect the tool's operation. For a tool with 2 parameters and zero schema documentation, this represents a complete failure to add parameter semantics.
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 'Handle run-browser-agent tool calls' is essentially a tautology that restates the tool name without explaining what the tool actually does. It doesn't specify what 'run-browser-agent' means, what resources it operates on, or what action it performs. While it mentions 'tool calls', this adds no meaningful information beyond the name itself.
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 absolutely no guidance on when to use this tool, what context it's appropriate for, or what alternatives might exist. There are no sibling tools mentioned, but even for a standalone tool, the description fails to give any indication of its intended use case or prerequisites.
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.
v1.0.0run_browser_agentScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'run_browser_agent' has a distinct purpose that cannot be confused with any other tool in this set.
The naming pattern cannot be inconsistent with only one tool. The tool name 'run_browser_agent' follows a clear verb_noun pattern, and there are no other tools to create naming conflicts or mixed conventions.
A single tool is generally too few for most server purposes, as it limits functionality and may not cover the domain adequately. For a browser automation server, one tool feels thin and restrictive compared to typical expectations of multiple actions like navigation, clicking, or form filling.
The tool surface is severely incomplete for browser automation. With only one tool to run an agent, there are obvious gaps in core operations such as navigating to URLs, interacting with page elements, extracting data, or managing browser sessions, which will likely cause agent failures in practical use.
AI-powered browser automation — navigate, click, fill forms, and extract data from any website.
AI-powered web automation. Navigate websites using AI agents for one page or a thousand
AI-powered web automation. Navigate websites using AI agents for one page or a thousand
Stealth web automation for AI agents. Login, signup, navigate, screenshot.
