About MCP RAG Server
MCP RAG Server is an archived RAG project explicitly marked deprecated, with no further features, fixes, or support. Its README points to CodeRAG as the successor; the successor’s capabilities are not features of this repository.
Key features
- 01Archived and deprecated
- 02No further features or fixes
- 03README points to CodeRAG
Use cases
- 01Check the project’s deprecated status
- 02Find the successor named in the README
MCP support
The project was an MCP server; the README does not describe the specific tools exposed by the old server.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README⚠️ DEPRECATED - Use CodeRAG Instead ⚠️
🚀 This project has evolved into something much better!
👉 github.com/SylphxAI/coderag 👈
SOTA family roadmap: docs/roadmap/sota-family-roadmap.md.
🎉 Why CodeRAG is 10x Better
We completely rewrote this project from scratch. Here's what you get:
⚡ Lightning Fast
| Feature | Old (rag-server-mcp) | New (CodeRAG) |
|---|---|---|
| Startup | 10-30s (ChromaDB + Ollama) | <1s (no external deps) |
| Indexing | Minutes (embedding API calls) | Seconds (TF-IDF + optional vectors) |
| Search | ~500ms (vector only) | <50ms (hybrid search) |
| Memory | 500MB+ (ChromaDB) | <100MB (SQLite) |
🧠 Smarter Search
| Feature | Old | New |
|---|---|---|
| TF-IDF | ❌ | ✅ StarCoder2 tokenizer |
| Vector Search | ✅ Basic | ✅ Hybrid (TF-IDF + Vector) |
| Code Understanding | ❌ Generic | ✅ Code-aware tokenization |
| Incremental Updates | ❌ Full rebuild | ✅ Smart diff detection |
🔧 Zero Dependencies
# Old way (painful)
docker-compose up -d # Start ChromaDB
ollama pull nomic-embed # Download model
# Wait 30 seconds...
# New way (instant)
npx @sylphx/coderag-mcp
# Done. That's it. 🎉
🏆 Battle-Tested Accuracy
- Smoothed IDF - No term gets ignored (even common ones like
function) - Logarithmic boosting - Stable ranking without score explosion
- StarCoder2 tokenizer - 4.7MB model trained on code
- Pre-computed magnitudes - Memory-efficient cosine similarity
🚀 Migrate in 30 Seconds
Step 1: Uninstall old
// Remove from your MCP config
{
"mcpServers": {
"rag-server": { ... } // DELETE THIS
}
}
Step 2: Install new
{
"mcpServers": {
"coderag": {
"command": "npx",
"args": ["-y", "@sylphx/coderag-mcp"]
}
}
}
Step 3: Enjoy 🎉
No Docker. No Ollama. No ChromaDB. Just works.
📊 Feature Comparison
| Feature | rag-server-mcp | CodeRAG |
|---|---|---|
| External Services | ChromaDB + Ollama | None |
| Docker Required | Yes | No |
| Startup Time | 10-30s | <1s |
| Search Algorithm | Vector only | Hybrid (TF-IDF + Vector) |
| Code Tokenization | Generic | StarCoder2 (code-aware) |
| Incremental Index | No | Yes |
| Memory Usage | 500MB+ | <100MB |
| Low Memory Mode | No | Yes (SQL-based) |
| Offline Support | No (needs Ollama) | Yes (TF-IDF) |
| Vector Search | Required | Optional |
| Actively Maintained | ❌ No | ✅ Yes |
🔗 Links
- New Repository: github.com/SylphxAI/coderag
- NPM Package: @sylphx/coderag-mcp
- Documentation: CodeRAG README
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.