Amazon Bedrock MCP Server
The Amazon Bedrock MCP Server allows you to generate high-quality images from text descriptions using Amazon's Nova Canvas model.
Generate images from text prompts: Create images based on detailed text descriptions
Negative prompts: Specify elements to exclude from the generated image
Customizable dimensions: Adjust image width and height (default: 1024x1024)
Quality options: Choose between 'standard' or 'premium' quality levels
Prompt adherence control: Adjust how closely the image follows the prompt using cfg_scale
Reproducible results: Use seed values for deterministic image generation
Batch generation: Generate multiple images (up to 5) in a single request
Base64-encoded output: Receive images as Base64 strings for easy handling
Integration: Works with Claude Desktop via MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Amazon Bedrock MCP Servergenerate a futuristic cityscape at night with flying cars"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Amazon Bedrock MCP Server
A Model Control Protocol (MCP) server that integrates with Amazon Bedrock's Nova Canvas model for AI image generation.
Features
High-quality image generation from text descriptions using Amazon's Nova Canvas model
Advanced control through negative prompts to refine image composition
Flexible configuration options for image dimensions and quality
Deterministic image generation with seed control
Robust input validation and error handling
Related MCP server: AWS Nova Canvas
Prerequisites
Active AWS account with Amazon Bedrock and Nova Canvas model access
Properly configured AWS credentials with required permissions
Node.js version 18 or later
Installation
AWS Credentials Configuration
The server requires AWS credentials with appropriate Amazon Bedrock permissions. Configure these using one of the following methods:
Environment variables:
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 credentials file (
~/.aws/credentials):[the_profile_name] aws_access_key_id = your_access_key aws_secret_access_key = your_secret_keyEnvironment variable for active profile:
export AWS_PROFILE=the_profile_nameIAM role (when deployed on AWS infrastructure)
Claude Desktop Integration
To integrate with Claude Desktop, add the following configuration to your settings file:
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'
}
}
}
}Available Tools
generate_image
Creates images from text descriptions using Amazon Bedrock's Nova Canvas model.
Parameters
prompt(required): Descriptive text for the desired image (1-1024 characters)negativePrompt(optional): Elements to exclude from the image (1-1024 characters)width(optional): Image width in pixels (default: 1024)height(optional): Image height in pixels (default: 1024)quality(optional): Image quality level - "standard" or "premium" (default: "standard")cfg_scale(optional): Prompt adherence strength (1.1-10, default: 6.5)seed(optional): Generation seed for reproducibility (0-858993459, default: 12)numberOfImages(optional): Batch size for generation (1-5, default: 1)
Example Implementation
const result = await callTool('generate_image', {
prompt: "A serene mountain landscape at sunset",
negativePrompt: "people, buildings, vehicles",
quality: "premium",
cfg_scale: 8,
numberOfImages: 2
});Prompt Guidelines
For optimal results, avoid negative phrasing ("no", "not", "without") in the main prompt. Instead, move these elements to the negativePrompt parameter. For example, rather than using "a landscape without buildings" in the prompt, use "buildings" in the negativePrompt.
For detailed usage guidelines, refer to the Nova Canvas documentation.
Development
To set up and run the server in a local environment:
git clone https://github.com/zxkane/mcp-server-amazon-bedrock.git
cd mcp-server-amazon-bedrock
npm install
npm run buildPerformance Considerations
Generation time is influenced by resolution (width and height), numberOfImages, and quality settings. When using higher values, be mindful of potential timeout implications in your implementation.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Available Tools
1 toolgenerate_imageC
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) |
TDQS
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.
TDQS
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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