"Information and Resources for Terraform AWS Integration" matching MCP servers:

MCP server for AWS SSO authentication and profile management, enabling AI assistants to check identity, switch profiles, and manage EC2/EKS resources.

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 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.

Enables natural language control of quantum photonics experiments through the qudi framework. Supports safe instrument control, measurement execution, and safety system management with built-in runlevel protection and parameter validation.

Provides a chat interface with natural language processing to deploy and manage AWS resources through an integrated Model Context Protocol (MCP) server.

This server implements the Multi-Agent Conversation Protocol for AWS SecurityHub, enabling interaction with AWS SecurityHub API through natural language commands.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

An AI-centric MCP server that enables automated Xilinx Vivado workflows, including project management, synthesis, implementation, and timing analysis. It allows AI agents to drive hardware design processes while integrating directly with the official Vivado GUI for visual context.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Enables large language models to interact with ROS robots seamlessly, allowing natural language control, real-time sensor monitoring, and autonomous task execution without modifying existing robot code.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

A server that integrates trained Random Forest models with Bee Framework, enabling ReAct interactivity for AI tools and agents.

A local MCP server that lets Hermes supervise Claude Code, delegating focused coding, research, or review tasks to the Claude Code CLI and managing worker sessions, background jobs, cancellations, and read-only reviews.

Uses Claude as an independent second-model reviewer inside Codex, performing reviews on disposable Git snapshots with structured findings.

Enables AI agents to control the Unity Editor through MCP, allowing scene building, runtime scripting, visual QA, and more.

MCP server for step-by-step mathematical reasoning and planning, enabling AI agents to execute calculations and perform GUI actions like opening PowerPoint.

Monitor failing GitHub Actions workflows, auto-fix them with OpenCode, and retry the run — driven by a workflow_run webhook, coordinated through an MCP server.

A FastMCP server providing real-time weather, news retrieval, and local note management tools for autonomous CrewAI agents. It enables context-aware multi-agent workflows with observability and high-speed inference integration.

A server implementation that enables LLMs to programmatically manage tasks in Todo.txt files using the Model Context Protocol (MCP), supporting operations like adding, completing, deleting, listing, searching, and filtering tasks.