MCP server for rag knowledge. Features semantic search, knowledge graph query, index documen...
Rag MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. MCP server for rag knowledge. Features semantic search, knowledge graph query, index documen...
RAG knowledge base MCP — semantic search, document chunking, relevance ranking, hallucination reduction. MIT.
The server ships on npm as @smithery/cli, 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.
Once Rag is connected, these are the calls the assistant has available:
Well — documented APIProduction-ready — Active maintenancePro — ** $99/mo — Full MCP suite + EU AI Act trackingEnterprise — ** $499/mo — Custom dev + SLA + Dedicated supportDomain — PurposeTier — PricePlenty 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. Rag's toolset — Well, Production-ready, Pro and 3 more — is a fair guide to whether it matches your workflow. It is maintained by CSOAI-ORG; 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We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.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 |
|---|---|
| Well | documented API |
| Production-ready | Active maintenance |
| Pro | ** $99/mo — Full MCP suite + EU AI Act tracking |
| Enterprise | ** $499/mo — Custom dev + SLA + Dedicated support |
| Domain | Purpose |
| Tier | Price |
{
"mcpServers": {
"rag-knowledge": {
"command": "npx",
"args": ["-y", "@smithery/cli"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
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.