This project is a Model Context Protocol server implementation for AWS operations, written in Python and listed as a community server in the official modelcontextprotocol/servers repository. It currently covers two services, S3 and DynamoDB, and gives an MCP client a set of named tools that map directly onto the operations of those services. The S3 tools create a bucket, list all buckets, delete a bucket, and upload, delete, list and read objects. The DynamoDB coverage is wider: table level tools create, describe, update and delete tables; item level tools put, get, update, delete, query and scan items; batch tools perform a batch get, a batch write of puts and deletes, and batch execution of PartiQL statements; and two further tools read and update the time to live settings of a table. Every operation the server performs is logged automatically, and the log is exposed back to the client as an MCP resource at audit://aws-operations, so an agent or a human can review afterwards what was done. Credentials come from the usual AWS sources, either the environment variables AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_REGION, which defaults to us-east-1, or the default credential chain configured with aws configure. Because the tool set includes create, update and delete operations, the server needs an IAM user with read and write permissions for S3 and DynamoDB, which makes credential scoping the main operational concern. Installation is either automatic through Smithery, which writes the client configuration, or manual by cloning the repository and adding an entry to claude_desktop_config.json that runs the server with uv. The intended users are developers who want an LLM client to inspect and manipulate their own AWS storage and table resources during development or operations work, with an audit trail of what the model did.
https://github.com/rishikavikondala/mcp-server-aws
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