"Terraform - Infrastructure as Code Tool or Related Search" matching MCP connectors:
Matching Connector Tools:
Integration Infrastructure Agents Actually Control
AI infrastructure design agent. Describe your app in plain English; Riley designs, prices, and deploys AWS or GCP infrastructure with generated Terraform.
MCP server aggregating developer infrastructure deals, free tiers, and startup programs
Deploy and host AI-built websites on EU infrastructure, straight from your AI agent.
Live status for 172 cloud and SaaS vendors from their official feeds. Is it you, or is it them?
Agent-native web hosting — deploy sites, manage DNS, register domains, scale infrastructure
Designs, prices, and deploys AWS/GCP cloud infrastructure from plain-English requirements.
AI compute infrastructure intelligence: facilities, supply chains, sovereign AI, export controls.
Deploy a GitHub repo to a live HTTPS URL from your AI tool; read logs, set variables, resize apps.
LLM and GPU rental prices: model price lookup, GPU listings, cheapest-GPU search, price history
Provision, SSH into, run commands on, and manage Linux VPSes from an AI agent. Pay USDC over x402 (Base) or by card over HTTP 402, a running box in under 60s. No signup, no API key to buy. This remote endpoint offers free browse/discovery, quotes, and server status.
MCP commerce surface for compute credits, API keys, GPU instances, and cloud storage.
TitanStore provides AI agents with programmatic access to compute credits, API keys, cloud storage, and GPU capacity. Search products, manage cart, and complete purchases in a single agentic workflow. No authentication required.
Provides capabilities that let LLM agents perform a range of infrastructure management tasks.
The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
AI first app deployment, unlike lovable or figma make, webslop.ai lets you or your ai of choice setup node.js apps or static sites in seconds. Designed be be the perfect place for you to deploy websites and apps super fast to the rest of the world and has a generous free tier.
The Telnyx MCP server is an official implementation of the Model Context Protocol that enables AI clients (like Claude Desktop, Cursor, and OpenAI Agents) to interact with Telnyx's telephony, messaging, and AI assistant APIs. It provides comprehensive capabilities including making and managing phone calls, sending SMS/MMS messages, purchasing and configuring phone numbers, creating AI assistants with custom instructions, managing cloud storage buckets, scraping and embedding website content, and handling integration secrets. The server exists as both a local implementation and a remotely hosted version, allowing developers to integrate real-world communication infrastructure directly into AI applications.
The Google Compute Engine MCP server is a fully-managed Model Context Protocol server that provides tools to manage Google Compute Engine resources through AI agents. It enables capabilities including instance management (creating, starting, stopping, resetting, listing), disk management, handling instance templates and group managers, viewing machine and accelerator types, managing images, and accessing reservation and commitment information. The server operates as a zero-deployment, enterprise-grade endpoint at https://compute.googleapis.com/mcp with built-in IAM-based security.
CloudOracle - 14-tool multi-cloud compliance MCP: AWS, Azure, GCP posture, IAM, configs.
The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costs—all without needing to parse text output or use complex kubectl commands.