SchemaVault

MCP server for storing and retrieving database schema information for LLMs.

Features

• Auto-load Databricks Unity Catalog schemas on startup

• Vector-based semantic search with configurable embedding service

• File-based storage (no external database required)

• MCP interface via HTTP/SSE for LLM integration

• LM Studio compatible

Related MCP server: Databricks MCP Server

Quick Start

1. Copy .env.example to .env and configure:

cp .env.example .env

2. Configure your .env:

# Embedding API (default: local embedding service)
EMBEDDING_API_URL=http://localhost:8000/v1
EMBEDDING_API_KEY=your-secret-token
EMBEDDING_MODEL=nomic-embed-text

# Databricks (optional)
DATABRICKS_HOST=https://your-workspace.cloud.databricks.com
DATABRICKS_TOKEN=your-token
DATABRICKS_CATALOGS=main

3. Build and run:

docker-compose up --build

Server runs on http://localhost:8001

MCP Tools

Endpoints

• GET /mcp/sse - SSE connection for MCP

• POST /mcp/messages - MCP message handler

• GET /health - Health check

LM Studio Integration

Add to ~/.lmstudio/mcp.json:

{
  "mcpServers": {
    "schemavault": {
      "url": "http://localhost:8001/mcp/sse"
    }
  }
}

Claude Desktop Integration

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "schemavault": {
      "command": "docker",
      "args": ["exec", "-i", "schemavault-schemavault-1", "python", "-m", "src.server"]
    }
  }
}

How It Works

1. On startup, cleans existing data and reloads schemas

2. Loads all schemas from Databricks Unity Catalog (if configured)

3. Embeds schemas using configured embedding service

4. Stores embeddings in Hnswlib vector index

5. LLM queries via MCP for semantic schema search

Environment Variables

Storage

Data stored in ./data/ (refreshed on each startup):

• vectors.index - Hnswlib vector index (768 dimensions)

• schemas.json - Table metadata

Requirements

• Docker

• Embedding service (OpenAI-compatible API)

• (Optional) Databricks workspace with Unity Catalog access