Devrag
A local RAG system that indexes your markdown documentation and exposes semantic search to Claude via MCP. Uses vector embeddings to retrieve relevant chunks instead of reading entire files, cutting token usage by roughly 40x. Provides tools for searching with directory and file pattern filters, indexing new documents, listing what's indexed, and reindexing when files change. Runs as a single binary with automatic GPU detection and works offline once models download. Useful when you're working with large doc repositories and want Claude to find relevant sections without burning context on full file reads. The CLI doubles as a standalone search tool if you need it outside of Claude sessions.
- GitHub stars
- 60
- tools
- 5
- last updated
- Dec 15, 2025
Tools
5 toolsPublic tool metadata for what this MCP can expose to an agent.
searchPerform semantic vector search with optional filtering
Perform semantic vector search with optional filtering
No parameter schema in public metadata yet.
index_markdownIndex a markdown file
Index a markdown file
No parameter schema in public metadata yet.
list_documentsList all indexed documents
List all indexed documents
No parameter schema in public metadata yet.
delete_documentRemove a document from the index
Remove a document from the index
No parameter schema in public metadata yet.
reindex_documentRe-index a document
Re-index a document
No parameter schema in public metadata yet.
Sponsors
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DevRag
Free Local RAG for Claude Code - Save Tokens & Time
DevRag is a lightweight RAG (Retrieval-Augmented Generation) system designed specifically for developers using Claude Code. Stop wasting tokens by reading entire documents - let vector search find exactly what you need.
Why DevRag?
When using Claude Code, reading documents with the Read tool consumes massive amounts of tokens:
- ❌ Wasting Context: Reading entire docs every time (3,000+ tokens per file)
- ❌ Poor Searchability: Claude doesn't know which file contains what
- ❌ Repetitive: Same documents read multiple times across sessions
With DevRag:
- ✅ 40x Less Tokens: Vector search retrieves only relevant chunks (~200 tokens)
- ✅ 15x Faster: Search in 100ms vs 30 seconds of reading
- ✅ Auto-Discovery: Claude Code finds documents without knowing file names
Features
Details
- Repository
- github.com/tomohiro-owada/devrag
- Category
- AI & LLM Tools
- Registry
- MCP registry, active
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