AWS S3 버킷과 상호 작용하기 위한 도구를 제공하는 MCP 서버입니다. 이 서버를 사용하면 모델 컨텍스트 프로토콜(Model Context Protocol)을 통해 S3 버킷 작업에 직접 액세스할 수 있습니다.
현재 서버는 다음 도구를 구현하고 있습니다.
list-s3-buckets : AWS 계정의 모든 S3 버킷을 나열합니다.
AWS 지역을 지정하는 선택적 region 매개변수
버킷 이름의 형식화된 목록을 반환합니다.
Related MCP server: AWS S3 Control MCP Server
Python 3.13 이상
AWS 자격 증명이 구성됨( AWS 자격 증명 설정 참조)
uv 패키지 관리자
지엑스피1
저장소를 복제합니다
uv를 사용하여 설치:
uv pip install .이 서버에서는 S3 버킷에 액세스하기 위해 AWS 자격 증명이 필요합니다. 자격 증명은 여러 가지 방법으로 구성할 수 있습니다.
AWS CLI 구성 (권장)
aws configure이렇게 하면 ~/.aws/credentials 에 자격 증명이 생성/업데이트됩니다.
환경 변수
export AWS_ACCESS_KEY_ID="your_access_key"
export AWS_SECRET_ACCESS_KEY="your_secret_key"
export AWS_DEFAULT_REGION="your_preferred_region" # optionalIAM 역할 (AWS 인프라에서 실행되는 경우)
AWS 자격 증명에 대한 자세한 내용은 AWS 설명서를 참조하세요.
Claude Desktop 구성 파일에 서버 구성을 추가합니다.
MacOS : ~/Library/Application Support/Claude/claude_desktop_config.json
윈도우 : %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"s3-tools": {
"command": "uvx",
"args": ["s3-tools"]
}
}
}개발/테스트를 위해 소스에서 직접 서버를 실행할 수 있습니다.
{
"mcpServers": {
"s3-tools": {
"command": "uv",
"args": [
"--directory",
"/path/to/s3-tools",
"run",
"s3-tools"
]
}
}
}동기화 종속성:
uv sync패키지 빌드:
uv buildPyPI에 게시하려면:
uv publish참고: 다음을 통해 PyPI 자격 증명을 구성해야 합니다.
토큰: --token 또는 UV_PUBLISH_TOKEN
또는 사용자 이름/비밀번호: --username / UV_PUBLISH_USERNAME 및 --password / UV_PUBLISH_PASSWORD
MCP 서버는 stdio를 통해 통신하므로 디버깅이 어려울 수 있습니다. 개발에는 MCP Inspector를 사용하는 것이 좋습니다.
npx @modelcontextprotocol/inspector uv run s3-toolsMIT
기여를 환영합니다! 풀 리퀘스트를 제출해 주세요.
List all S3 buckets in your AWS account
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | AWS region (optional, defaults to configured region) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'all S3 buckets' which implies completeness, but doesn't address permissions needed, rate limits, pagination, error conditions, or what the output looks like. 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 zero wasted words. It's appropriately sized for a simple list operation and front-loads the core functionality.
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?
For a tool with no annotations, no output schema, and minimal behavioral disclosure, the description is incomplete. It should address authentication requirements, output format, and potential limitations given this is an AWS operation that could have permission constraints.
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 the single optional 'region' parameter. The description doesn't add any parameter-specific information beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.
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 action ('List') and resource ('all S3 buckets in your AWS account'), providing a specific verb+resource combination. It doesn't need to distinguish from siblings since none exist, so it earns a 4 rather than 5.
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, prerequisites, or contextual constraints. It simply states what the tool does without any usage instructions.
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.
list-s3-bucketsScored across 1 tool
With only one tool, there is no possibility for ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and distinct by default.
Since there is only one tool, naming consistency is inherently perfect with no deviations or mixed conventions to evaluate. The tool name 'list-s3-buckets' follows a clear verb_noun pattern.
A single tool for an S3 server is too few for the apparent scope, as S3 involves operations like uploading, downloading, deleting objects, and managing buckets beyond listing. This minimal set will likely cause agent failures due to significant functional gaps.
The tool surface is severely incomplete for an S3 domain, covering only bucket listing. There are obvious gaps for core operations such as creating/deleting buckets, managing objects (upload, get, delete), and other essential S3 functionalities, making it inadequate for typical use cases.
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The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
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