
Amazon Bedrock MCP Server
Provides access to Amazon Bedrock's Nova Canvas model for AI image generation.
zxkane
Tools
generate_image
Generate image(s) using Amazon Nova Canvas model. The returned data is Base64-encoded string that represent each image that was generated.
README
Amazon Bedrock MCP Server
A Model Control Protocol (MCP) server that integrates with Amazon Bedrock's Nova Canvas model for AI image generation.
<a href="https://glama.ai/mcp/servers/9qw7dwpvj9"><img width="380" height="200" src="https://glama.ai/mcp/servers/9qw7dwpvj9/badge" alt="Amazon Bedrock Server MCP server" /></a>
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
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 region
-
AWS credentials file (
~/.aws/credentials
):[the_profile_name] aws_access_key_id = your_access_key aws_secret_access_key = your_secret_key
Environment variable for active profile:
export AWS_PROFILE=the_profile_name
-
IAM 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 build
Performance 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.
Recommended Servers
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
@kazuph/mcp-fetch
Model Context Protocol server for fetching web content and processing images. This allows Claude Desktop (or any MCP client) to fetch web content and handle images appropriately.
Claude Code MCP
An implementation of Claude Code as a Model Context Protocol server that enables using Claude's software engineering capabilities (code generation, editing, reviewing, and file operations) through the standardized MCP interface.
contentful-mcp
Update, create, delete content, content-models and assets in your Contentful Space

Supabase MCP Server
A Model Context Protocol (MCP) server that provides programmatic access to the Supabase Management API. This server allows AI models and other clients to manage Supabase projects and organizations through a standardized interface.
mermaid-mcp-server
A Model Context Protocol (MCP) server that converts Mermaid diagrams to PNG images.
@kazuph/mcp-gmail-gas
Model Context Protocol server for Gmail integration. This allows Claude Desktop (or any MCP client) to interact with your Gmail account through Google Apps Script.
Metabase MCP Server
Enables AI assistants to interact with Metabase databases and dashboards, allowing users to list and execute queries, access data visualizations, and interact with database resources through natural language.

Linear MCP Server
A Model Context Protocol server that integrates with Linear's issue tracking system, allowing LLMs to create, update, search, and comment on Linear issues through natural language interactions.

Airtable MCP Server
A Model Context Protocol server that provides tools for programmatically managing Airtable bases, tables, fields, and records through Claude Desktop or other MCP clients.