MCP(Model Context Protocol)은 생성형 AI application이 외부 데이터를 활용하는 주요한 인터페이스로 빠르게 확산되고 있습니다. 2024년 11월에 Anthropic의 오픈소스 프로젝트로 시작되었고, 현재 Cursor뿐 아니라
Most AI and media services work still happens through a UI a human drives. MCP MCP server moves it into the conversation instead. MCP(Model Context Protocol)은 생성형 AI application이 외부 데이터를 활용하는 주요한 인터페이스로 빠르게 확산되고 있습니다. 2024년 11월에 Anthropic의 오픈소스 프로젝트로 시작되었고, 현재 Cursor뿐 아니라 OpenAI에서도 지원하고 있습니다. 여기에서는 [MCP with.
The server publishes 1 tool. What each one is for:
RAG — chat.run_rag_with_knowledge_baseConfiguration is passed through the environment: FASTMCP_LOG_LEVEL. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
The server ships on PyPI as mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. MCP's toolset — RAG — is a fair guide to whether it matches your workflow. It is maintained by kyopark2014; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| RAG | chat.run_rag_with_knowledge_base |
{
"mcpServers": {
"kyopark2014": {
"command": "uvx",
"args": ["mcp"],
"env": {
"FASTMCP_LOG_LEVEL": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| FASTMCP_LOG_LEVEL | Configuration value read at startup. | Optional |
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.