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

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

Tool	Description
add_schema	Store a table schema
query_model	Semantic search for table info
list_models	List all stored tables

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

Variable	Default	Description
EMBEDDING_API_URL	http://localhost:8000/v1	Embedding service URL
EMBEDDING_API_KEY	your-secret-token	Embedding API key
EMBEDDING_MODEL	nomic-embed-text	Embedding model name
DATABRICKS_HOST	-	Databricks workspace URL
DATABRICKS_TOKEN	-	Databricks PAT
DATABRICKS_CATALOGS	main	Catalogs to load (main, a,b, or *)
DATABRICKS_SCHEMAS	(all)	Schemas to load (optional: schema1,schema2 or *)

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