"AWS Organizations" matching MCP servers:

Lets AI chat clients query and manage multiple AWS accounts and organizations through IAM Identity Center SSO, fanning one request across accounts using rule-selected IAM roles and returning merged, redacted results.

Enables AI assistants to interact with AWS services and resources through AWS CLI commands, allowing management of AWS infrastructure with security controls.

Enables users to generate professional AWS architecture diagrams, sequence diagrams, flow charts, and class diagrams using Python code through the diagrams package. Supports customizable styling and secure diagram generation for cloud infrastructure visualization.

Enables read-only querying of AWS accounts, regions, and resources through a small fixed set of MCP tools backed by a YAML catalog of views.

A small AWS MCP for AI assistants: one server, one config entry, SSO re-auth baked in, generic CRUD over hundreds of resource types, live docs lookup, server-side scripting for batched workflows.

Enables LLMs to search through AWS Lambda Powertools documentation across multiple runtimes (Python, TypeScript, Java, .NET) using a Model Context Protocol server.

Provides AWS service recommendations based on use case descriptions and allows browsing AWS services organized by categories. Helps users discover the most suitable AWS services for their specific technical requirements.

A Model Context Protocol server that integrates with AWS CodePipeline, allowing users to manage pipelines through Windsurf and Cascade using natural language commands.

Enables interaction with multiple Azure DevOps organizations simultaneously, providing access to pipelines, builds, repositories, and pull requests across different organizations without switching contexts or restarting the server.

Exposes LocalStack-managed AWS services as AI-agent tools, enabling LLMs and AI agents to interact with local AWS infrastructure during development and testing.

Provides guidance and tools for the AWS Cloud Development Kit, including infrastructure patterns, GenAI constructs, and security compliance via CDK Nag. It streamlines development by generating Bedrock Agent schemas and providing comprehensive documentation for Lambda layers and AWS Solutions Constructs.

Provides read-only access to AWS resources including S3 buckets, EC2 instances, IAM users, and caller identity verification through the Model Context Protocol.

Enables management and provisioning of AWS resources like EC2, S3, and RDS using natural language prompts through the Model Context Protocol. It allows users to automate complex infrastructure tasks, such as setting up VPCs and security groups, via a chat interface.

A Model Context Protocol (MCP) server that enables AI assistants like Claude to interact with your AWS environment. This allows for natural language querying and management of your AWS resources during conversations. Think of better Amazon Q alternative.

A unified MCP server for AWS that enables natural language infrastructure management, cross-service resource discovery, and dependency mapping. It features 30 intelligent tools for cost optimization, incident investigation, and multi-account operations protected by a robust safety system.

MCP server that migrates Amazon Quick Suite resources (Spaces, Chat Agents, Action Connectors, and S3 Knowledge Bases) between AWS accounts, preserving permissions and linkages, with idempotency and preview dry-run support.

Enables planning, creating, and verifying Laravel + React + AWS ECS projects, adding frontend apps to generated projects, and reading project status and logs through MCP tools with AWS-safe operational guidance.

Runs on Cloudflare Workers to let MCP clients execute AWS API commands with your own credentials via SigV4-signed HTTPS requests. Supports common S3, EC2, IAM, Lambda, and STS operations plus a generic escape-hatch for any AWS API call.

Enables read-only AWS FinOps scans for hidden costs such as NAT Gateway endpoint traffic, infinite log retention, gp2/orphaned EBS volumes, idle Elastic IPs, and stale snapshots, with USD/month estimates and Terraform or CloudFormation remediation patch generation.

Enables AI assistants to execute AWS CLI commands and retrieve service documentation through the Model Context Protocol. It supports Unix pipes for output filtering and provides pre-defined prompt templates for common cloud management tasks.

A Python CLI package aaws published to GitHub with PyPI distribution via GitHub Actions. Natural language in → AWS CLI command out, with explain, confirm, and safety layers. Available as MCP server.

Enables AI agents to access private GitHub repositories, organizations, issues, pull requests, and file contents via OAuth authentication.

A minimal, production-ready MCP server running on AWS Lambda with Streamable HTTP transport, enabling deployment of custom tools behind API Gateway.

Enables AI assistants to execute AWS CLI commands and access AWS documentation, resources, and prompt templates through the Model Context Protocol with support for Unix pipes and secure Docker-based deployment.

Bridges MCP clients to AWS SigV4-gated MCP endpoints, signing requests with AWS credentials via stdio or a local HTTP listener. Enables standard MCP clients to securely access Bedrock AgentCore runtimes and IAM-authenticated endpoints without handling SigV4 themselves.

A framework to deploy MCP servers on AWS using serverless services like Lambda, API Gateway, and EventBridge, with dynamic tools, prompts, and resources managed via SSM parameters and Lambda functions.

Provides conversational access to LocalStack AWS services (S3, DynamoDB, Lambda, SQS) through natural language, enabling local cloud infrastructure management without real AWS costs.

An MCP server allowing AI assistants like Claude to execute read-only SQL queries against AWS PostgreSQL databases, enabling data retrieval while preventing database modifications.

Enables interaction with Buildkite CI/CD to list organizations, pipelines, builds, jobs, and logs, as well as retry jobs.

Provides a comprehensive suite of tools for interacting with AWS services like S3, EC2, and Lambda using natural language commands. It enables AI assistants to inspect, manage, and operate AWS resources directly through the Model Context Protocol using your local credentials.

A boilerplate TypeScript MCP server with Express.js designed for AWS AppRunner deployment. Provides sample tools, resources, and prompts with Docker containerization and GitHub Actions CI/CD workflow.

Enables running a stateless Streamable HTTP MCP server on AWS Lambda behind API Gateway with JWT bearer authentication, per-scope tool allowlists, OAuth protected-resource metadata, and a CDK construct for deployment.

Enables managing local AWS session context and ordered execution-plan state through MCP tools, including session lifecycle, profile/region settings, and explicit plan step advancement without calling AWS APIs.

An MCP server that provides comprehensive access to AWS Amplify Gen 2 Next.js documentation, enabling AI assistants to provide accurate, up-to-date guidance on building applications with Amplify.

A read-only MCP server for safe, structured investigation of AWS serverless resources, providing curated tools for tracing dependencies, permissions, and failures without exposing raw SDK access.

Provides offline access to AWS CDK API references and integration test code samples for both stable (aws-cdk-lib) and alpha (@aws-cdk) modules, enabling developers to browse CDK documentation through MCP resources.

A minimal MCP server deployed on AWS Lambda and API Gateway using AWS CDK, enabling tool execution via JSON-RPC (e.g., an add tool).

Enables interaction with AWS services (EC2, S3, Lambda, DynamoDB, etc.) through the Model Context Protocol, allowing natural language management of cloud resources.

MCP server to control a Flask application and optimize AWS costs, providing tools for app management, AWS resource checking, and cost analysis via Claude.

This MCP server enables users to interact with AWS Amplify Gen2 application data through natural language, allowing AI assistants like Claude to perform operations on Amplify data models using conversational language instead of complex code.