"Amazon S3" matching MCP servers:

Enables persistent skill management over the Model Context Protocol, allowing agents to use, write, list, and migrate skills backed by directory, Git, or S3 stores.

A Model Context Protocol (MCP) server that enables integration of Strands agents with Amazon Q and other MCP-compatible systems through a plugin architecture.

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.

Enables AI assistants to request human input by creating tasks on Amazon Mechanical Turk, integrating human-in-the-loop capabilities via the MCP standard.

Enables AI agents to autonomously provision, pay for, and manage S3-compatible storage and vector databases without human intervention.

Provides automated security incident remediation tools for AWS, enabling real-time threat response by quarantining malicious S3 objects, restricting IAM access, and updating security groups.

Enables Alexa+ to monitor budgets, check affordability, recommend Amazon deals within spending limits, and execute automated purchase plans through MCP tools.

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A Model Context Protocol server that integrates with Amazon Braket, allowing AI assistants to access, control, and interpret results from quantum computing resources.

A Model Context Protocol server that enables AI assistants to create images and videos using Amazon Nova Canvas and Nova Reel models.

A containerized MCP server deployed on Amazon EKS that provides a simple calculator tool for adding two numbers. Demonstrates how to deploy and scale MCP servers in a Kubernetes environment with proper session management and HTTP endpoints.

Provides prompt injection detection, PII/secrets redaction, and an audit trail for AI agents via MCP tools.

A self-hosted decision engine that sits between an AI agent and blockchain execution. Agents submit a proposed action, Guardian returns an explainable ALLOW / WARN / BLOCK before anything gets signed or broadcast.

An MCP server that exposes three tools for auditing, scaffolding, and triaging agent intent specs against a unified template, helping prevent intent failures in AI agents.

Enables MCP clients to create, read, message, wait for, interrupt, and settle persistent peer threads through an existing T3 Code server, keeping them visible across its clients. It supports project-scoped coordination with source attribution and retry-safe commands.

Enables AI agents to make authenticated API calls and run commands with secrets injected, while keeping credentials completely hidden from the model, with policy enforcement, grants, and audit logging.

Three practical MCP tools to investigate agents, threads, and content trust signals.

Automatically enhances developer prompts with quality requirements, codebase context, and architectural patterns, then orchestrates other MCP servers to ensure AI coding assistants produce high-quality, structured code that follows best practices and security standards.

Persistent, consensus-validated institutional memory for AI agents. Gives LLMs real memory that survives across sessions - validated through BFT consensus, not just dumped to a file.

Enables hands-free, voice-driven management of T3 Code projects, threads, agent runs, and pending actions via GPTVoice tool calls.