mcp-rag-server is a Model Context Protocol (MCP) server that enables Retrieval Augmented Generation (RAG) capabilities. It empowers Large Language
If you already use MCP, the mcp mcp server is the piece that lets your assistant work with it directly. mcp-rag-server is a Model Context Protocol (MCP) server that enables Retrieval Augmented Generation (RAG) capabilities. It empowers Large Language Models (LLMs) to answer questions based on your document content by indexing and retrieving.
npx on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Configuration is passed through the environment: BASE_LLM_API, EMBEDDING_MODEL, VECTOR_STORE_PATH, CHUNK_SIZE, LLM_API_KEY. 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.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. It is maintained by kwanLeeFrmVi; 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We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
{
"mcpServers": {
"rag": {
"command": "npx",
"args": ["-y", "mcp-rag-server"],
"env": {
"BASE_LLM_API": "http://localhost:11434/v1",
"EMBEDDING_MODEL": "nomic-embed-text",
"VECTOR_STORE_PATH": "./vector_store",
"CHUNK_SIZE": "500"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| BASE_LLM_API | Configuration value read at startup. | Optional |
| EMBEDDING_MODEL | Configuration value read at startup. | Optional |
| VECTOR_STORE_PATH | Filesystem location the server is allowed to use. | Optional |
| CHUNK_SIZE | Configuration value read at startup. | Optional |
| LLM_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.