"Two agents working simultaneously" matching MCP connectors:
Matching Connector Tools:
Simulate, test, and analyze cloud architectures without deploying real infrastructure. Cloud World Model enables AI agents to model cloud environments, evaluate architecture behavior and costs, run failure and chaos simulations, and explore infrastructure scenarios across cloud providers.
Deploy static sites from AI agents: deploy_site publishes files and returns a live URL in seconds.
Live GPU compute & inference token price indices for AI agents — 591 reference indices across H100/A100/B200/B300 spot+on-demand, Claude/GPT/Llama token pricing, and more. Every value is methodology-versioned and citable via the /v1/verify handshake.
Deploy static websites from AI agents. Free at mcp.shipstatic.com — no install, no signup.
Queryable Cloud FinOps knowledge for AI agents: AWS/Azure/GCP cost optimisation, waste playbooks.
Hosted MCP for creating, checking, deploying, and hosting static sites for AI agents.
Hosting for AI agents: publish a live website in one tool call, ephemeral or forever.
x402 pay-per-call infra for agents: $0.03 image generation, Postgres, auth, storage, functions.
Provision and manage a VPS for AI agents over MCP: register, order, get root, control the server.
Discover DigitalPublic plans, trust, ROI, status and autonomous Sandbox enrollment for AI agents.
A second opinion for AI agents: one prompt across several live Gonka models + roles, one call.
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.
EU-native PaaS for AI agents — deploy web apps with one sentence, managed Postgres, GDPR by default.
AWS cloud security scanners for AI agents — S3, IAM, EC2, EKS, RDS, CloudTrail, CloudWatch Logs
Debug, build, and manage Power Automate cloud flows with AI agents
Provides capabilities that let LLM agents perform a range of infrastructure management tasks.
The BigQuery remote MCP server is a fully managed service that uses the Model Context Protocol to connect AI applications and LLMs to BigQuery data sources. It provides secure, standardized tools for AI agents to list datasets and tables, retrieve schemas, generate and execute SQL queries through natural language, and analyze data—enabling direct access to enterprise analytics data without requiring manual SQL coding.
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.
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.