Local semantic search over your Obsidian vault - always fresh, never reindex.
io.github.KIOKO-LAB/freshvault MCP Server
This MCP server provides local semantic search over an Obsidian vault. It is described as “always fresh, never reindex,” indicating search updates without reindexing. The server focuses on local-first access for retrieving semantically relevant content from an Obsidian workspace.
Your Obsidian vault is Claude's memory. Always fresh, always local.
Every other vault-search MCP makes you re-run an index command, babysit a watch terminal, or click "Update Index" in a web UI. freshvault watches your vault from inside the MCP server: edit a note, and Claude sees it seconds later. Automatically. Forever.
freshvault demo: save a note, the watcher reindexes it automatically, semantic search finds it seconds later
🔄 Never reindex — the file watcher lives in the server process; boot catch-up absorbs offline edits
🔒 100% local — embeddings via Ollama (bge-m3), your notes never leave your machine
🌏 Multilingual by default — bge-m3 handles Korean, Japanese, and 100+ languages that English-only defaults fail on
🎯 Benchmark-driven retrieval — we built BM25 hybrid fusion, measured it on Korean paraphrase queries, watched it hurt (82.5% → 47.5% top-1), and deleted it. Pure dense, on purpose — receipts
🪶 No vector DB, no Docker, no Python — JSON metadata + a Float32 sidecar, plain Node, source you can read in one sitting
🧠 Chunking that respects sentences — YAML frontmatter stripped, splits on paragraph/sentence boundaries (CJK-aware)
That's it. The wizard detects your Obsidian vault, pulls the embedding model, builds the index, and registers with Claude Code. There is no step 2, and there is never a step 2: no index command to re-run, no watch terminal, no background service.
full note + its backlinks/outlinks from the vault link graph (path-traversal safe)
index_status
freshness report: notes/chunks, excluded count, last sync, watcher state
Scoped queries competitors gate behind settings or paywalls work per-query here:
"Search my notes tagged #project modified after June for the budget discussion"
How it works
code
Obsidian vault ──fs.watch──▶ freshvault MCP server ──search_notes──▶ Claude
(.md files) (chunks → bge-m3 embeddings (generation)
→ one JSON index, incremental)
Incremental: only changed/deleted notes are re-embedded (mtime+size diff), debounced 4s
Safety net: a 60s mtime sweep catches events the watcher misses (network drives, atomic-rename editors)
Multi-client safe: first server process becomes the writer (heartbeated lock); others are readers that hot-reload and promote themselves if the writer dies
Transactional: an embedding-server outage mid-index can never lose or corrupt notes
Scale: vectors live in a packed Float32 sidecar (fast startup, compact); brute-force cosine over thousands of chunks is milliseconds. Honest note: search is still linear — sub-100ms into tens of thousands of chunks, but this is not a vector DB replacement for huge corpora
Multiple vaults
Register one server per vault — index files are kept per-vault automatically:
bash
claude mcp add work-vault -s user -e FRESHVAULT_VAULT=/path/to/work -- npx -y freshvault serve
claude mcp add personal-vault -s user -e FRESHVAULT_VAULT=/path/to/personal -- npx -y freshvault serve
Other embedding servers (LM Studio, LiteLLM, OpenAI-compatible)
Anything speaking /v1/embeddings works; FRESHVAULT_EMBED_KEY for authenticated endpoints (never written to the config file).
Configuration
Everything works with zero config after setup. Override when needed:
Flag
Env
Default
--vault
FRESHVAULT_VAULT
from setup
--model
FRESHVAULT_MODEL
bge-m3
--ollama-url
FRESHVAULT_OLLAMA_URL
http://localhost:11434
--data
FRESHVAULT_DATA
platform data dir
—
FRESHVAULT_EMBED_API
ollama (or openai)
—
FRESHVAULT_EMBED_URL
http://localhost:1234 (openai mode)
—
FRESHVAULT_EMBED_KEY
none (openai mode, optional)
—
FRESHVAULT_IGNORE
none — e.g. Templates/,Daily/** (or ignore: [] in config)
Commands: setup · serve (default) · index (manual escape hatch) · status
Benchmark
A Korean retrieval micro-benchmark ships in-repo (node scripts/bench.mjs) — 30 Korean notes, 40 paraphrase queries, comparing embedding models on top-1/MRR. Results and the bge-m3-ko (85.0% top-1, 634MB) import guide in docs/ko-bench.md.