AI 이미지 생성을 위한 Amazon Bedrock의 Nova Canvas 모델과 통합되는 모델 제어 프로토콜(MCP) 서버입니다.
Amazon의 Nova Canvas 모델을 사용하여 텍스트 설명에서 고품질 이미지 생성
부정적인 프롬프트를 통한 고급 제어로 이미지 구성을 개선합니다.
이미지 크기 및 품질에 대한 유연한 구성 옵션
시드 제어를 통한 결정론적 이미지 생성
강력한 입력 검증 및 오류 처리
Related MCP server: AWS Nova Canvas
Amazon Bedrock 및 Nova Canvas 모델 액세스가 가능한 활성 AWS 계정
필요한 권한이 있는 AWS 자격 증명을 올바르게 구성했습니다.
Node.js 버전 18 이상
서버에는 적절한 Amazon Bedrock 권한이 있는 AWS 자격 증명이 필요합니다. 다음 방법 중 하나를 사용하여 이를 구성하세요.
환경 변수:
지엑스피1
AWS 자격 증명 파일( ~/.aws/credentials ):
[the_profile_name]
aws_access_key_id = your_access_key
aws_secret_access_key = your_secret_key활성 프로필에 대한 환경 변수:
export AWS_PROFILE=the_profile_nameIAM 역할(AWS 인프라에 배포된 경우)
Claude Desktop과 통합하려면 설정 파일에 다음 구성을 추가하세요.
MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"amazon-bedrock": {
"command": "npx",
"args": [
"-y",
"@zxkane/mcp-server-amazon-bedrock"
],
"env": {
"AWS_PROFILE": "your_profile_name", // Optional, only if you want to use a specific profile
"AWS_ACCESS_KEY_ID": "your_access_key", // Optional if using AWS credentials file or IAM role
"AWS_SECRET_ACCESS_KEY": "your_secret_key", // Optional if using AWS credentials file or IAM role
"AWS_REGION": "us-east-1" // Optional, defaults to 'us-east-1'
}
}
}
}Amazon Bedrock의 Nova Canvas 모델을 사용하여 텍스트 설명에서 이미지를 생성합니다.
prompt (필수): 원하는 이미지에 대한 설명 텍스트(1~1024자)
negativePrompt (선택 사항): 이미지에서 제외할 요소(1~1024자)
width (선택 사항): 픽셀 단위의 이미지 너비(기본값: 1024)
height (선택 사항): 픽셀 단위의 이미지 높이(기본값: 1024)
quality (선택 사항): 이미지 품질 수준 - "표준" 또는 "프리미엄"(기본값: "표준")
cfg_scale (선택 사항): 신속한 준수 강도(1.1-10, 기본값: 6.5)
seed (선택 사항): 재현성을 위한 생성 시드(0-858993459, 기본값: 12)
numberOfImages (선택 사항): 생성을 위한 배치 크기(1-5, 기본값: 1)
const result = await callTool('generate_image', {
prompt: "A serene mountain landscape at sunset",
negativePrompt: "people, buildings, vehicles",
quality: "premium",
cfg_scale: 8,
numberOfImages: 2
});최적의 결과를 얻으려면 기본 프롬프트에서 부정적인 표현("no", "not", "without")을 사용하지 마세요. 대신 이러한 요소를 negativePrompt 매개변수로 옮기세요. 예를 들어, 프롬프트에 "건물이 없는 풍경" 대신 negativePrompt 매개변수에 "건물"을 사용하세요.
자세한 사용 지침은 Nova Canvas 설명서 를 참조하세요.
로컬 환경에서 서버를 설정하고 실행하려면:
git clone https://github.com/zxkane/mcp-server-amazon-bedrock.git
cd mcp-server-amazon-bedrock
npm install
npm run build생성 시간은 해상도( width 와 height ), numberOfImages , 그리고 quality 설정의 영향을 받습니다. 더 높은 값을 사용할 때는 구현 과정에서 발생할 수 있는 시간 초과 문제를 염두에 두세요.
이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.
Generate image(s) using Amazon Nova Canvas model. The returned data is Base64-encoded string that represent each image that was generated.
| Name | Required | Description | Default |
|---|---|---|---|
| cfg_scale | No | How closely to follow the prompt (1.1-10, default: 6.5) | |
| height | No | Height of the generated image (default: 1024) | |
| negativePrompt | No | Optional text description of what to avoid in the image (1-1024 characters) | |
| numberOfImages | No | Number of images to generate (1-5, default: 1) | |
| prompt | Yes | Text description of the image to generate (1-1024 characters) | |
| quality | No | Quality of the generated image (default: standard) | |
| seed | No | Seed for reproducible generation (0-858993459, default: 12) | |
| width | No | Width of the generated image (default: 1024) |
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 mentions the return format (Base64-encoded string) but lacks critical details such as rate limits, authentication requirements, error handling, or whether this is a read/write operation. For a complex image generation tool, this is insufficient.
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 front-loaded, consisting of two clear sentences. However, it could be slightly more efficient by combining ideas, but it avoids unnecessary verbosity.
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 an image generation tool with 8 parameters and no output schema or annotations, the description is incomplete. It covers the basic purpose and return format but misses behavioral context, usage guidelines, and output details, leaving significant gaps for an AI agent.
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 fully documents all 8 parameters. The description adds no parameter-specific information beyond what's in the schema, resulting in a baseline score of 3.
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: 'Generate image(s) using Amazon Nova Canvas model.' It specifies the verb ('generate') and resource ('image(s)'), and identifies the specific model. However, without sibling tools, we cannot assess differentiation, so it cannot achieve a perfect 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 typical use cases. It simply states what the tool does without context for selection.
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.
v1.0.0generate_imageScored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool 'generate_image' has a clear and distinct purpose that cannot be mistaken for any other tool in the set.
The single tool name 'generate_image' follows a verb_noun pattern, and with only one tool, consistency is inherently perfect. There are no other tools to compare against, so no inconsistencies can arise.
A single tool is too few for a server named 'Amazon Bedrock MCP Server', which suggests a broader scope related to Amazon Bedrock services. This minimal toolset feels thin and incomplete for the implied domain, limiting functionality to just image generation.
The tool surface is severely incomplete for the server's purpose. Amazon Bedrock includes multiple models and capabilities beyond image generation, such as text generation, embeddings, and model management. The single tool fails to cover these core aspects, leaving significant gaps that will cause agent failures.
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