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AWS DynamoDB MCP Server

Official AWS Labs developer-experience MCP server for Amazon DynamoDB that provides expert data-modeling guidance, model validation against DynamoDB Local, source-database analysis, schema conversion, and CDK generation.

by AWS Labs·added 2026-06-21·
Claude CodeCodexCursorClaude Desktop
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Open the source and read safety notes before installing.

Citation facts

Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.

Canonical URL
https://heyclau.de/entry/mcp/aws-dynamodb-mcp-server
Source URLs
https://github.com/awslabs/mcp/blob/main/src/dynamodb-mcp-server/README.md, https://github.com/awslabs/mcp, https://awslabs.github.io/mcp/
Brand
AWS Labs
Brand domain
aws.amazon.com
Brand asset source
brandfetch
Safety notes
The core tools provide data-modeling guidance and are advisory; review all generated models, schemas, and CDK output before deploying anything to AWS., Model validation sets up a local DynamoDB instance and creates test tables; the source-database analyzer connects to a database you point it at (RDS Data API or a direct connection)., Generated CDK apps and `dynamodb_data_model.json` files are written to your workspace; inspect them before running or committing.
Privacy notes
Access patterns, entity definitions, and any source-database schema you share are processed to produce the model and validation artifacts., The source-database analyzer can read schema structure and access-pattern data from the database you connect; keep database credentials out of public prompts, issues, and screenshots.
Author
AWS Labs
Submitted by
jaso0n0818
Claim status
unclaimed
Last verified
2026-06-21

Safety notes

  • The core tools provide data-modeling guidance and are advisory; review all generated models, schemas, and CDK output before deploying anything to AWS.
  • Model validation sets up a local DynamoDB instance and creates test tables; the source-database analyzer connects to a database you point it at (RDS Data API or a direct connection).
  • Generated CDK apps and `dynamodb_data_model.json` files are written to your workspace; inspect them before running or committing.

Privacy notes

  • Access patterns, entity definitions, and any source-database schema you share are processed to produce the model and validation artifacts.
  • The source-database analyzer can read schema structure and access-pattern data from the database you connect; keep database credentials out of public prompts, issues, and screenshots.

Prerequisites

  • Python 3.10 or newer and `uv` / `uvx` installed (Astral) to run the package.
  • An MCP client that supports stdio servers (Claude Code, Claude Desktop, Cursor, Kiro, or VS Code).
  • Docker or a local DynamoDB Local setup if you want to use the model-validation tool, which creates tables and runs your access patterns.
  • Connection details/credentials for any source database (MySQL, PostgreSQL, SQL Server, Oracle) only if you use the source-database analyzer.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
10 minutes
Difficulty
intermediate
Tool listing metadata
Pricing
open-source
Disclosure
editorial
Application category
DeveloperApplication
Operating system
Cross-platform
Full copyable content
{
  "awslabs.dynamodb-mcp-server": {
    "command": "uvx",
    "args": ["awslabs.dynamodb-mcp-server@latest"],
    "env": {
      "FASTMCP_LOG_LEVEL": "ERROR"
    }
  }
}

About this resource

Overview

AWS DynamoDB MCP Server is the official AWS Labs developer-experience Model Context Protocol server for Amazon DynamoDB. Rather than running table CRUD, it focuses on design: it gives Claude an expert DynamoDB data-modeling prompt and a set of tools to validate models, analyze an existing source database, convert a model into a machine-readable schema, and generate CDK resources.

It runs locally over stdio via uvx from the published awslabs.dynamodb-mcp-server Python package. The core modeling guidance needs no AWS credentials; the validation and source-analysis tools touch a local DynamoDB instance or a source database you point them at.

Features

  • Data modeling — retrieve the DynamoDB Data Modeling Expert prompt with enterprise design patterns, cost strategies, and multi-table philosophy.
  • Model validation — stand up DynamoDB Local, create tables and test data, and execute the model's access patterns, saving validation results.
  • Source DB analysis — extract schema and access patterns from MySQL, PostgreSQL, SQL Server, or Oracle to seed a DynamoDB model.
  • Schema conversion — convert a data model into a structured schema.json for code generation, validated over multiple iterations.
  • Resource generation — generate a CDK app from the data model to deploy the designed DynamoDB tables.

Use Cases

  • Design a DynamoDB data model from your application's access patterns.
  • Validate a proposed model against DynamoDB Local before committing to it.
  • Migrate from a relational database by analyzing its schema and access patterns.
  • Generate CDK infrastructure from a finalized DynamoDB data model.

Installation

Claude Code

  1. Install Python 3.10+ and uv.
  2. Add the server with the stdio configuration above (command uvx, package awslabs.dynamodb-mcp-server@latest).
  3. Verify it is connected with claude mcp list.

Claude Desktop / Cursor / Kiro / VS Code

Add the configSnippet above to your client's MCP configuration. The first run downloads the package via uvx. For model validation, ensure Docker / DynamoDB Local is available.

Source And Trust

This entry is based on the official AWS Labs awslabs/mcp repository and the published PyPI package (Apache-2.0). The server is design- and validation-focused; the modeling guidance is advisory and the validation/analysis tools touch a local DynamoDB instance or a source database you choose. Review generated models, schemas, and CDK output, and verify the configuration against the linked source before relying on it.

#aws#dynamodb#data-modeling#database#aws-labs

Source citations

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How it compares

AWS DynamoDB MCP Server side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

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Official AWS Labs developer-experience MCP server for Amazon DynamoDB that provides expert data-modeling guidance, model validation against DynamoDB Local, source-database analysis, schema conversion, and CDK generation.

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Trust
Install riskReview firstReview firstReview firstReview first
Notes Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
BrandAWS Labs logoAWS LabsAWS Labs logoAWS LabsAWS Labs logoAWS LabsAWS Labs logoAWS Labs
Categorymcpmcpmcpmcp
Sourcesource-backedsource-backedsource-backedsource-backed
AuthorAWS LabsAWS LabsAWS LabsAWS Labs
Added2026-06-212026-06-212026-06-212026-06-21
Platforms
Claude CodeCodexCursorClaude Desktop
Claude CodeClaude Desktop
Claude CodeClaude Desktop
Claude CodeClaude Desktop
Source repo————
Safety notes✓The core tools provide data-modeling guidance and are advisory; review all generated models, schemas, and CDK output before deploying anything to AWS. Model validation sets up a local DynamoDB instance and creates test tables; the source-database analyzer connects to a database you point it at (RDS Data API or a direct connection). Generated CDK apps and `dynamodb_data_model.json` files are written to your workspace; inspect them before running or committing.✓The server is read-only by default. Adding the `--allow-write` flag (with the matching IAM permissions) enables create and append operations on S3 Tables; there is no delete or general update. Enable write only deliberately. AWS advises that you are responsible for your agents: if you enable write, back up your data first and validate LLM-generated instructions before execution, since misconfigured permissions can cause data loss. This server acts on real S3 Tables data with your AWS credentials; scope the profile least-privilege and run it only on a trusted host.✓The configuration above is read-only. Setting `ALLOW_WRITE=true` lets the server create and modify infrastructure (ECR repos, CloudFormation stacks, ECS services) and `ALLOW_SENSITIVE_DATA=true` exposes logs; enable these only deliberately. AWS documents this server as primarily for development, testing, and non-critical environments; keep write/sensitive-data disabled for production accounts and prefer non-production targets while evaluating it. This server acts on real infrastructure with your AWS credentials; scope the profile to the intended account, region, and resources, and run it only on a trusted host.✓The configuration above is read-only. Adding the `--allow-write` flag lets the server create, update, patch, and delete EKS/Kubernetes resources (including creating clusters via CloudFormation) and `--allow-sensitive-data-access` exposes logs and events; enable these only deliberately. This server acts on real infrastructure with your AWS credentials; scope the profile to the intended account, region, and clusters, and prefer non-production targets while evaluating it. Run it only on a trusted host, and review any generated manifests or CloudFormation actions before applying them.
Privacy notes✓Access patterns, entity definitions, and any source-database schema you share are processed to produce the model and validation artifacts. The source-database analyzer can read schema structure and access-pattern data from the database you connect; keep database credentials out of public prompts, issues, and screenshots.✓Table schemas, metadata, query results, and bucket/namespace identifiers can be returned through tool calls and exposed to the model. Keep account identifiers, credentials, and any sensitive table data out of public prompts, issues, and screenshots.✓Cluster, service, task, task-definition, and ECR metadata plus account/region identifiers can be returned through tool calls and exposed to the model. With sensitive-data access enabled, logs and deployment details may be returned; keep account identifiers, credentials, and log contents out of public prompts, issues, and screenshots.✓Cluster state, resource manifests, ARNs, and account/region metadata can be returned through tool calls and exposed to the model. With sensitive-data access enabled, pod logs and Kubernetes events may be returned; keep account identifiers, credentials, and log contents out of public prompts, issues, and screenshots.
Prerequisites
  • Python 3.10 or newer and `uv` / `uvx` installed (Astral) to run the package.
  • An MCP client that supports stdio servers (Claude Code, Claude Desktop, Cursor, Kiro, or VS Code).
  • Docker or a local DynamoDB Local setup if you want to use the model-validation tool, which creates tables and runs your access patterns.
  • Connection details/credentials for any source database (MySQL, PostgreSQL, SQL Server, Oracle) only if you use the source-database analyzer.
  • An AWS account with S3 Tables and permissions for the table buckets you intend to read (and, if enabled, write).
  • Python 3.10 or newer and `uv` / `uvx` installed (Astral) to run the package.
  • AWS credentials configured locally (for example via `aws configure` or `AWS_PROFILE`) scoped least-privilege to the intended S3 Tables resources.
  • An MCP client that supports stdio servers; the server runs locally on the same host as the client.
  • An AWS account with Amazon ECS/ECR and permissions to view (and, if enabled, deploy) the target resources.
  • Docker or Finch for containerization and local image builds.
  • Python 3.10 or newer and `uv` / `uvx` installed (Astral) to run the package.
  • AWS credentials configured locally (for example via `aws configure` or `AWS_PROFILE`) scoped to the intended account, region, and resources.
  • An AWS account with Amazon EKS and permissions to view (and, if enabled, manage) the target clusters.
  • Python 3.10 or newer and `uv` / `uvx` installed (Astral) to run the package.
  • AWS credentials configured locally (for example via `aws configure` or `AWS_PROFILE`) scoped to the intended account, region, and clusters.
  • An MCP client that supports stdio servers; the server runs locally on the same host as the client.
Install
uvx awslabs.dynamodb-mcp-server@latest
uvx awslabs.s3-tables-mcp-server@latest
uvx --from awslabs-ecs-mcp-server ecs-mcp-server
uvx awslabs.eks-mcp-server@latest
Config
{
  "mcpServers": {
    "awslabs.dynamodb-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.dynamodb-mcp-server@latest"],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "type": "stdio"
    }
  }
}
{
  "mcpServers": {
    "awslabs.s3-tables-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.s3-tables-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "${AWS_PROFILE}",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "type": "stdio"
    }
  }
}
{
  "mcpServers": {
    "awslabs.ecs-mcp-server": {
      "command": "uvx",
      "args": ["--from", "awslabs-ecs-mcp-server", "ecs-mcp-server"],
      "env": {
        "AWS_PROFILE": "${AWS_PROFILE}",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR",
        "ALLOW_WRITE": "false",
        "ALLOW_SENSITIVE_DATA": "false"
      },
      "type": "stdio"
    }
  }
}
{
  "mcpServers": {
    "awslabs.eks-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.eks-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "${AWS_PROFILE}",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "type": "stdio"
    }
  }
}
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