MCP server for Obsidian Smart Connections. Semantic search using your vault's embeddings.
MCP server for Obsidian Smart Connections. Semantic search using your vault's embeddings. Exposed over MCP by the smart mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
A security-first MCP server for Smart Connections. Read-only. Path-validated. Auditable.
The server ships on npm as @modelcontextprotocol/inspector, 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.
Everything the assistant can do here goes through one of these:
search_by_text — Search using freeform text (computes embedding locally)search_similar — Find notes semantically similar to a given notesearch_by_embedding — Search using a raw embedding vectorget_note — Get content of a specific note (path validated)get_model_info — Get embedding model configurationlist_indexed — List all indexed notesPrerequisites — The Prerequisites tool exposed by this serverSetup — The Setup tool exposed by this serverYou will need one environment variable: VAULT_PATH. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Smart's toolset — search_by_text, search_similar, search_by_embedding and 5 more — is a fair guide to whether it matches your workflow. It is maintained by gogogadgetbytes; 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We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.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 |
|---|---|
| search_by_text | Search using freeform text (computes embedding locally) |
| search_similar | Find notes semantically similar to a given note |
| search_by_embedding | Search using a raw embedding vector |
| get_note | Get content of a specific note (path validated) |
| get_model_info | Get embedding model configuration |
| list_indexed | List all indexed notes |
| Prerequisites | The Prerequisites tool exposed by this server. |
| Setup | The Setup tool exposed by this server. |
Or manually add to `~/.claude.json`:
```json
{
"mcpServers": {
"smart-connections": {
"command": "node",
"args": ["/path/to/smart-connections-mcp/dist/index.js"],
"env": {
"VAULT_PATH": "/path/to/your/obsidian/vault"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| VAULT_PATH | Filesystem location the server is allowed to use. | Optional |
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.