Bauplan MCP Server

A Model Context Protocol (MCP) server that gives AI assistants (Claude Code, Claude Desktop, Cursor) access to Bauplan lakehouse operations: querying tables, schema inspection, branch management, and running pipelines. A video walkthrough demonstrates setup and usage.

[!NOTE]

This project is released in Beta under MIT license. APIs and features may change without notice as we continue development.

[!IMPORTANT]

Server-side deployment is now available for existing Bauplan users. You no longer need to run the MCP server locally — contact your Bauplan account team for details.

Overview

This repository contains the Bauplan MCP Server — a Model Context Protocol server that gives AI assistants access to Bauplan lakehouse operations. A blog post with context and background is available here.

[!TIP]

Looking for the best local AI setup with Bauplan? Check out **BauplanLabs/bauplan-skills** — it includes agent playbooks (CLAUDE.md), skills, and everything you need to get AI coding assistants working with Bauplan via CLI and SDK, without running an MCP server.

MCP Quick Start

You can get started in one minute with your existing AI assistant: a video setup with Claude Desktop and Claude Code is also available here for reference.

You need:

• a Bauplan API key properly configured in your local config file (default profile) - the server will pick it up automatically (see below for alternative authentication methods);
• uv (or a standard pip managed virtual environment, see below);
• an AI platform able to leverage the MCP, as for example Claude Code, Cursor, Claude Desktop.

[!WARNING]

do not use an Admin Bauplan API key: while the server will refuse to write on main, it is good practice to use a non-admin key for AI-assisted development (see our roadmap below for more details on upcoming security features).

Start the server with:

uv sync
uv run python main.py --transport streamable-http

The MCP server is now available at http://localhost:8000/mcp. You can configure the server in Claude Code for example with:

claude mcp add -t http mcp-bauplan http://localhost:8000/mcp

Similar commands can be run on Claude Desktop or Cursor to enable the AI to access the server.

Et voilà! You can now start asking your AI questions about your data lakehouse (and much more!).

Advanced Configurations

Bauplan Credentials

The Beta release covers the local development use case. Authentication to your Bauplan lakehouse happens as follows:

• if you do not specify a Bauplan profile as a flag, the default one on the machine running the server will be used at every interaction with the lakehouse.
• if you specify a profile as a flag, this profile will be used instead when instantiating a Bauplan client.
• if you specify a header in your assistant - key=Bauplan, value=your_api_key (e.g. in Claude code claude mcp add -H "Bauplan: " ...) -, your_api_key will be used instead when instantiating a Bauplan client. This is convenient for quick tests, and opens up the possibility of hosting the catalog on a shared infrastructure, delegating to clients the Bauplan API key management.

Server CLI Options

The server supports the following CLI options, mostly useful for specifying alternative transport options:

Note: The --host and --port options are ignored when using stdio transport since it communicates through stdin/stdout.

Claude Desktop

To add the Bauplan MCP server to Claude Desktop, follow the guide to get to your claude_desktop_config.json file.

You can then add this configuration (modify the paths as needed):

{
  "mcpServers": {
    "mcp-bauplan": {
      "command": "/path/to/bauplan-mcp-server/.venv/bin/python3",
      "args": [
        "/path/to/bauplan-mcp-server/main.py",
        "--transport",
        "stdio"
      ],
      "workingDirectory": "/path/to/bauplan-mcp-server/"
    }
  }
}

Quit and restart Claude Desktop. Now all Bauplan tools are available to your assistant, as this video demonstrates.

MCP Inspector

Start the MCP Inspector if you wish to manually test the server (Node is required):

npx @modelcontextprotocol/inspector

Now, configure the inspector with the proper variables, e.g. for Streamable HTTP:

• Transport Type: Streamable HTTP
• URL:
• Session Token: Use the token from inspector output

Features

Tool List

Data Operations

• **list_tables**: List all tables in a branch/namespace
• **get_schema**: Get schema for all tables in a branch/namespace
• **get_table**: Get schema for a specific table (more efficient for single table)
• **run_query**: Execute SELECT queries on tables
• **run_query_to_csv**: Execute SELECT queries and save results directly to CSV file (scalar data types only)

Branch Management

• **get_branches**: List branches with optional filters
• **get_commits**: Get commit history from branches
• **create_branch**: Create new branches from references
• **has_branch**: Check if a specific branch exists
• **merge_branch**: Merge branches with custom commit messages
• **delete_branch**: Delete branches (with safety checks)

Namespace Management

• **get_namespaces**: List available namespaces in a branch
• **create_namespace**: Create new namespaces in branches
• **has_namespace**: Check if a specific namespace exists in a branch
• **delete_namespace**: Delete namespaces from branches

Tag Management

• **get_tags**: Get tags with optional filters
• **create_tag**: Create a new tag from a reference
• **has_tag**: Check if a tag exists
• **delete_tag**: Delete a tag

Table Management

• **create_table**: Create a table from S3 location using schema detection (creates ICEBERG table structure but doesn't populate data)
• **plan_table_creation**: Create a table import plan from S3 location (generates YAML schema plan with job tracking)
• **apply_table_creation_plan**: Apply a table creation plan to resolve schema conflicts (returns job_id for tracking)
• **has_table**: Check if a specific table exists in a branch/reference
• **delete_table**: Delete a table from a specific branch
• **import_data**: Import data into an existing table from S3 location (returns job_id for tracking)
• **revert_table**: Revert a table from a source reference to a target branch with optional replacement

Project Management

• **project_run**: Run a Bauplan project from a specified directory and reference with configurable parameters (dry-run, timeout, detach mode)
• **code_run**: Run a Bauplan project from code files provided as a dictionary (useful for clients that cannot submit paths), automatically creates temporary directory and validates project structure

Job Management

• **list_jobs**: List jobs in the Bauplan system with optional filtering for all users
• **get_job**: Get detailed information about a specific job by its ID
• **cancel_job**: Cancel a running job by its ID and get updated job status

User Management

• **get_user_info**: Get information about the current authenticated user (username and full name)

Instructions and Guidance

• **get_instructions**: Get detailed instructions for specific Bauplan use cases (pipeline, data, repair, wap, test, sdk)

---

License

This project is provided with no guarantees under the attached MIT License.