与 Amazon Bedrock 的 Nova Canvas 模型集成的模型控制协议 (MCP) 服务器，用于 AI 图像生成。
使用亚马逊的 Nova Canvas 模型根据文本描述生成高质量图像
通过负面提示进行高级控制以改进图像构图
图像尺寸和质量的灵活配置选项
通过种子控制生成确定性图像
强大的输入验证和错误处理
Related MCP server: AWS Nova Canvas
具有 Amazon Bedrock 和 Nova Canvas 模型访问权限的有效 AWS 账户
正确配置具有所需权限的 AWS 凭证
Node.js 版本 18 或更高版本
该服务器需要具有适当 Amazon Bedrock 权限的 AWS 凭证。请使用以下方法之一进行配置：
环境变量：
export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key
export AWS_REGION=us-east-1 # or your preferred regionAWS 凭证文件（ ~/.aws/credentials ）：
[the_profile_name]
aws_access_key_id = your_access_key
aws_secret_access_key = your_secret_key活动配置文件的环境变量：
export AWS_PROFILE=the_profile_nameIAM 角色（部署在 AWS 基础设施上时）
要与 Claude Desktop 集成，请将以下配置添加到您的设置文件中：
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'
}
}
}
}使用 Amazon Bedrock 的 Nova Canvas 模型根据文本描述创建图像。
prompt （必需）：所需图像的描述性文字（1-1024 个字符）
negativePrompt （可选）：要从图像中排除的元素（1-1024 个字符）
width （可选）：图像宽度（以像素为单位）（默认值：1024）
height （可选）：图像高度（以像素为单位）（默认值：1024）
quality （可选）：图像质量级别 - “标准”或“高级”（默认：“标准”）
cfg_scale （可选）：提示遵守强度（1.1-10，默认值：6.5）
seed （可选）：用于可重复性的生成种子（0-858993459，默认值：12）
numberOfImages （可选）：生成的批次大小（1-5，默认值：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
});为了获得最佳效果，请避免在主提示符中使用否定措辞（“no”、“not”、“without”）。相反，请将这些元素移至negativePrompt参数。例如，与其在提示符中使用“a landscape without houses”，不如在negativePrompt中使用“buildings”。
有关详细的使用指南，请参阅Nova Canvas 文档。
要在本地环境中设置并运行服务器：
git clone https://github.com/zxkane/mcp-server-amazon-bedrock.git
cd mcp-server-amazon-bedrock
npm install
npm run build生成时间受分辨率（ width和height ）、 numberOfImages和quality设置的影响。使用较高值时，请注意实现过程中可能出现的超时问题。
该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅 LICENSE 文件。
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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