Lakexpress Mcp

LakeXpress MCP Server

[PyPI] [License: MIT] [MCP Registry]

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 configurationLakeXpress 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 syncLakeXpress sync --sync_id <sync_id>
# 4. Check statusLakeXpress status -a auth.json --log_db_auth_id export_db --sync_id <sync_id>

Environment Variables

Development

# Install dev dependenciespip install -e ".[dev]"
# Run testspython -m pytest tests/ -v
# Run with coveragepython -m pytest tests/ -v --cov=src --cov-report=term-missing

License

MIT