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Amazon Bedrock MCP Server

by zxkane

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

  1. Active AWS account with Amazon Bedrock and Nova Canvas model access

  2. Properly configured AWS credentials with required permissions

  3. 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:

  1. 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
  2. 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
  3. 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.

Available Tools

1 tool
generate_imageC

Generate image(s) using Amazon Nova Canvas model. The returned data is Base64-encoded string that represent each image that was generated.

ParametersJSON Schema
NameRequiredDescriptionDefault
cfg_scaleNoHow closely to follow the prompt (1.1-10, default: 6.5)
heightNoHeight of the generated image (default: 1024)
negativePromptNoOptional text description of what to avoid in the image (1-1024 characters)
numberOfImagesNoNumber of images to generate (1-5, default: 1)
promptYesText description of the image to generate (1-1024 characters)
qualityNoQuality of the generated image (default: standard)
seedNoSeed for reproducible generation (0-858993459, default: 12)
widthNoWidth of the generated image (default: 1024)

TDQS

C2.9/5.0
Behavior2/5

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.

Conciseness4/5

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.

Completeness3/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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

B3/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness1/5

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.

Maintenance

ActivityInactive
ResponsivenessNo issues

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