What makes this different from other AWS MCP servers?
The README cites three advantages: runs directly from a Docker image (no local clone), uses Python/boto3 for better stability on Mac, and is easy for Python developers to contribute back to.
@baryhuang
Basic information
A Python-based MCP server that lets Claude run boto3 code to query and manage AWS resources. Execute powerful AWS operations directly through Claude with proper sandboxing and containerization. No need for complex setups - just pass your AWS credentials and start interacting with
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-server-aws-resources-python": {
"command": "npx",
"args": [
"-y",
"@smithery/cli",
"install",
"mcp-server-aws-resources-python",
"--client",
"claude"
]
}
}
}Execute a boto3 code snippet to query or modify AWS resources
A Model Context Protocol server that runs Python code via boto3 to query or modify any AWS resources. Designed for Python developers who want natural-language-driven AWS management, it runs as a Docker container and supports both read and write operations based on the IAM user’s permissions.
Install the server via Docker (pull the image or build locally) or through Smithery. Configure AWS credentials using environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION) or an AWS profile mounted from ~/.aws. In Claude Desktop, add the server to your MCP configuration with the Docker run command. Invoke the tool aws_resources_query_or_modify by providing a Python code snippet that uses boto3 and sets a result variable.
The README cites three advantages: runs directly from a Docker image (no local clone), uses Python/boto3 for better stability on Mac, and is easy for Python developers to contribute back to.
The server is containerized in Docker. Inside the container, it uses Python with boto3, and allows imports of `boto3`, `operator`, `json`, `datetime`, `pytz`, `dateutil`, `re`, and `time`. No additional system dependencies are needed beyond Docker.
Credentials can be passed via environment variables (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, optional `AWS_SESSION_TOKEN`, `AWS_DEFAULT_REGION`) or by mounting a local `~/.aws` directory and setting the `AWS_PROFILE` environment variable.
No. The README explicitly states that operations are not limited to Read
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