Storm MCP(Model Context Protocol) 서버는 LLM 애플리케이션과 RAG 데이터 소스 및 도구들 간의 원활한 통합을 가능하게 하는 개방형 프로토콜입니다. Anthropic의 [Model Context
Serverless Rag MCP server exists for a simple reason — assistants are far more useful when they can act on Serverless Rag directly instead of describing what you should do. Storm MCP(Model Context Protocol) 서버는 LLM 애플리케이션과 RAG 데이터 소스 및 도구들 간의 원활한 통합을 가능하게 하는 개방형 프로토콜입니다. Anthropic의 Model Context Protocol을 구현하여 Claude Desktop에서 Storm Platform을 바로 이용합니다.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Once Serverless Rag is connected, these are the calls the assistant has available:
main.py — MCP 서버를 초기화하고 이벤트 핸들러를 설정합니다You will need one environment variable: STORM_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Serverless Rag's toolset — main.py — is a fair guide to whether it matches your workflow. It is maintained by sionic-ai; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| main.py | MCP 서버를 초기화하고 이벤트 핸들러를 설정합니다. |
2) JSON 내부에 MCP 서버 설정 추가:
```json
{
"mcpServers": {
"storm": {
"command": "sh",
"args": [
"/Users/sigridjineth/Desktop/work/storm-mcp-server/scripts/run.sh"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| STORM_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.