mcp-vstash
MCP server integration for vstash document memory. Use when configuring Claude Desktop or other MCP-compatible AI assistants with persistent document memory, setting up vstash MCP tools for semantic search and Q&A, or integrating vstash with AI assistant workflows via Model Context Protocol.
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- 2026年4月2日 14:22
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- mcp-vstash
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- MCP server integration for vstash document memory. Use when configuring Claude Desktop or other MCP-compatible AI assistants with persistent document memory, setting up vstash MCP tools for semantic search and Q&A, or integrating vstash with AI assistant workflows via Model Context Protocol.
# vstash MCP Server
Enable AI assistants (Claude Desktop, Cursor, Copilot) to search and answer questions from your local document memory via MCP.
## Setup
### 1. Install vstash
```bash
pip install vstash
```
### 2. Add to Claude Desktop config
Edit `~/Library/Application Support/Claude/claude_desktop_config.json`:
```json
{
"mcpServers": {
"vstash": {
"command": "vstash-mcp"
}
}
}
```
> **pyenv users:** Use full path to binary:
> `"command": "/path/to/.pyenv/versions/3.x.x/bin/vstash-mcp"`
### 3. Restart Claude Desktop
The vstash tools appear in Claude's tool list.
## Available Tools
| Tool | Description |
|------|-------------|
| `vstash_add(path)` | Ingest file, directory, or URL into memory |
| `vstash_ask(query, top_k)` | Semantic search + LLM answer with sources |
| `vstash_search(query, top_k)` | Hybrid search with context expansion and relevance signal |
| `vstash_list()` | List all ingested documents |
| `vstash_stats()` | Database statistics (docs, chunks, size) |
| `vstash_forget(source)` | Remove document from memory |
| `vstash_collections()` | List all collections |
| `vstash_export(...)` | Export chunks as JSONL for training data curation |
| `vstash_job(job_id)` | Check status of background directory ingestion |
## Search Response Fields
`vstash_search` returns:
| Field | Description |
|-------|-------------|
| `chunks` | Array of results with ±1 adjacent chunks expanded |
| `relevance` | Confidence tier: `"high"`, `"medium"`, `"low"`, `"none"` |
| `hint` | Human-readable relevance explanation |
| `best_distance` | Cosine distance of best vector match (lower = more relevant) |
**Relevance tiers:**
| Distance | Tier | Meaning |
|----------|------|---------|
| ≤ 0.95 | high | Results are relevant |
| 0.95–0.98 | medium | Results may be tangential |
| > 0.98 | low | Results may not be relevant |
Works from first search — no warm-up period needed.
## API Key Configuration
MCP servers don't inherit shell environment variables. Configure inference in `~/.vstash/vstash.toml`:
```toml
[cerebras]
api_key = "your-key-here"
```
Or use fully local Ollama (no API key):
```toml
[inference]
backend = "ollama"
[ollama]
host = "http://localhost:11434"
model = "llama3.2"
```
## Troubleshooting
| Issue | Solution |
|-------|---------|
| Tools don't appear | Run `which vstash-mcp` to verify PATH |
| "No module named vstash" | MCP server uses different Python — use full path in config |
| `ask` fails but search works | Check inference backend configured in `vstash.toml` |
## Reference
See `references/mcp-reference.md` for tool-specific options and advanced configuration.
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