Smart Connections MCP Server

Give Claude true semantic memory of your Obsidian vault. An MCP server that searches your notes by meaning — reusing the embeddings the Smart Connections Obsidian plugin already generated, and running the same embedding model locally to understand your queries. No cloud calls; your vault never leaves your machine.

What it does

• search_notes — semantic search across one or many vaults. Matches whole notes and individual sections (blocks), returns similarity-ranked results with content snippets.

• get_similar_notes — notes similar to a given note (stored embeddings).

• get_connection_graph — walk similarity links outward to map related ideas.

• get_note_content — read a note, or extract specific blocks.

• list_vaults / get_stats — what's loaded, counts, models, load errors.

Related MCP server: Semantic Search MCP

Requirements

• Node.js 20+

• An Obsidian vault with the Smart Connections plugin installed and embeddings generated (v2 tested against Smart Connections 3.x data)

• An MCP client (Claude Desktop, Claude Code, …)

Setup (Claude Desktop)

Add to claude_desktop_config.json and restart Claude Desktop:

{
  "mcpServers": {
    "smart-connections": {
      "command": "npx",
      "args": ["-y", "smart-connections-mcp"],
      "env": {
        "SMART_VAULT_PATH": "/path/to/Vault One,/path/to/Vault Two"
      }
    }
  }
}

One vault or several — separate paths with commas.

SMART_VAULT_PATHS (plural) is also accepted as an alias for SMART_VAULT_PATH and takes precedence over it if both are set.

Claude Code

claude mcp add smart-connections -e SMART_VAULT_PATH="/path/to/vault" -- npx -y smart-connections-mcp

How it works

Smart Connections stores an embedding vector for every note and block in .smart-env/. This server loads those vectors into memory and, when you search, embeds your query with the same model your vault used (downloaded once, ~25MB, runs locally via transformers.js). Results are ranked by cosine similarity. Edits you make in Obsidian are picked up automatically.

If the embedding model can't load (e.g. no network on very first run), search degrades to literal keyword matching and says so explicitly ("mode": "keyword-fallback"). When only some vaults fall back, mode stays "semantic", those rows carry "match": "keyword", and they always rank after the true semantic rows.

Migrating from v1

• get_embedding_neighbors was removed.

• search_notes is now genuinely semantic and its response includes vault, scope, block, snippet, and mode fields.

• Everything else is backward compatible; single-vault SMART_VAULT_PATH configs work unchanged.

Development

npm install
npm test              # build + CI-tier tests (no network)
npm run test:live     # + real-model tests (downloads ~25MB once)
npm run smoke -- "/path/to/vault" "your query"

MIT — see LICENSE.