Lakexpress MCP

LakeXpress MCP Server

A Model Context Protocol (MCP) server for LakeXpress — a database to Parquet export tool with sync management and data lake publishing.

Features

• 14 subcommands supported: logdb management, config management, sync execution, status, and cleanup
• 5 source databases: SQL Server, PostgreSQL, Oracle, MySQL, MariaDB
• 6 log databases: SQL Server, PostgreSQL, MySQL, MariaDB, SQLite, DuckDB
• 6 storage backends: Local, S3, S3-compatible, GCS, Azure ADLS Gen2, OneLake
• 7 publish targets: Snowflake, Databricks, Fabric, BigQuery, MotherDuck, Glue, DuckLake
• Command preview before execution with safety confirmation
• Auth file validation
• Workflow suggestions based on use case

Installation

pip install -e ".[dev]"

Claude Code Configuration

Add to your Claude Code MCP settings:

{

"mcpServers": { "lakexpress": { "command": "python", "args": ["-m", "src.server"], "cwd": "/path/to/lakexpress-mcp", "env": { "LAKEXPRESS_PATH": "/path/to/LakeXpress", "LAKEXPRESS_TIMEOUT": "3600", "LAKEXPRESS_LOG_DIR": "./logs", "FASTBCP_DIR_PATH": "/path/to/FastBCP/" } } } }

Or using the installed entry point:

{

"mcpServers": { "lakexpress": { "command": "lakexpress-mcp", "env": { "LAKEXPRESS_PATH": "/path/to/LakeXpress", "FASTBCP_DIR_PATH": "/path/to/FastBCP/" } } } }

Tools

preview_command

Build and preview any LakeXpress CLI command without executing it. Supports all 14 subcommands with full parameter validation.

execute_command

Execute a previously previewed command. Requires confirmation: true as a safety mechanism.

validate_auth_file

Validate that an authentication file exists, is valid JSON, and optionally check for specific auth_id entries.

list_capabilities

List all supported source databases, log databases, storage backends, publishing targets, compression types, and available commands.

suggest_workflow

Given a use case (source DB type, storage destination, optional publish target), suggest the full sequence of LakeXpress commands with example parameters.

get_version

Report the detected LakeXpress binary version and capabilities.

Workflow Example

# 1. Initialize the log database (first-time setup)

LakeXpress logdb init -a auth.json --log_db_auth_id export_db

2. Create a sync configuration

LakeXpress config create -a auth.json --log_db_auth_id export_db \ --source_db_auth_id prod_db --source_schema_name sales \ --output_dir ./exports --compression_type Zstd

3. Execute the sync

LakeXpress sync --sync_id

4. Check status

LakeXpress status -a auth.json --log_db_auth_id export_db --sync_id

Environment Variables

Development

# Install dev dependencies

pip install -e ".[dev]"

Run tests

python -m pytest tests/ -v

Run with coverage

python -m pytest tests/ -v --cov=src --cov-report=term-missing

License

MIT