We shipped a streaming MCP server - connect Cursor, Windsurf, or Claude Desktop with one URL
Cloud World Model now has a streaming MCP server. Paste the URL into Cursor, Windsurf, or Claude Desktop and simulate AWS/GCP/Azure/OCI/DigitalOcean architectures, inject chaos, run RL training - no install, no API key required to start.
What we shipped: a hosted Streamable HTTP MCP server for cloud simulation. You paste one URL into your AI client, and it can simulate production infrastructure, inject failures, analyze costs, and run RL training episodes - all without provisioning anything real.
The zero-friction angle is the lead story. No npx install, no Docker container, no local setup. Just:
https://www.cloudworldmodel.ai/mcpDrop that into @Cursor (mcpServers config), @Windsurf , or @Claude for Desktop remote servers, and you're done. We also listed it on Smithery for one-click connection.
Two tiers:
No API key - 6 demo tools, 50 total calls, 20 sim steps. Enough to run a real simulation and see what the server does before committing to anything.
API key - all 49 tools, no caps. Architecture validation, chaos testing, multi-cloud cost comparison, RL agent training, AI-generated recommendations.
Who it's for: AI engineers who want to test cloud architecture decisions from inside their coding environment without spinning up real infrastructure. Cursor users who want to ask "will this design handle 10k RPS?" and get a real answer. RL researchers who need a fast, free simulation environment for cloud optimization policies.
We built the simulation engine first (97%+ accuracy against real AWS/GCP/Azure/OCI benchmarks). The MCP server is the interface layer that makes it accessible from any AI toolchain.
Would love to hear what you build with it - or what's missing from the current tool set.
Replies
Love the direction. How do you balance simulation speed with accuracy? At what point do you decide a slower but more realistic simulation is worth the tradeoff?
@joseph_parker3 Great question. We use a hybrid approach: a fast rule-based simulator backed by an ML layer that captures provider-specific behavior like autoscaling and latency patterns. For exploration, speed is critical. For validation, we’re happy to spend a little more time if it helps avoid an expensive architectural mistake in production.
The multi-cloud stuff is cool as well. One cool question to try asking the simulator is "When is OCI cheaper than Digital Ocean?". One might be surprised at the answer.