cwprep 0.3.0

Tableau Prep Flow SDK - Programmatically generate .tfl files

cwprep - Tableau Prep Flow SDK

A Python SDK for programmatically generating Tableau Prep data flow (.tfl) files. Built through reverse-engineering the TFL JSON structure, enabling flow creation and modification via code without opening the GUI.

Installation

pip install cwprep

Quick Start

from cwprep import TFLBuilder, TFLPackager

# Create builder
builder = TFLBuilder(flow_name="My Flow")

# Add database connection
conn_id = builder.add_connection(
    host="localhost",
    username="root",
    dbname="mydb"
)

# Add input tables
orders = builder.add_input_table("orders", "orders", conn_id)
customers = builder.add_input_table("customers", "customers", conn_id)

# Join tables
joined = builder.add_join(
    name="Orders + Customers",
    left_id=orders,
    right_id=customers,
    left_col="customer_id",
    right_col="customer_id",
    join_type="left"
)

# Add output
builder.add_output_server("Output", joined, "My_Datasource")

# Build and save
flow, display, meta = builder.build()
TFLPackager.save_to_folder("./output", flow, display, meta)
TFLPackager.pack_zip("./output", "./my_flow.tfl")

Features

Feature	Method	Description
Database Connection	add_connection()	Connect to MySQL/PostgreSQL/Oracle
SQL Input	add_input_sql()	Custom SQL query input
Table Input	add_input_table()	Direct table connection
Join	add_join()	left/right/inner/full joins
Union	add_union()	Merge multiple tables
Filter	add_filter()	Expression-based filter
Value Filter	add_value_filter()	Keep/exclude by values
Keep Only	add_keep_only()	Select columns
Remove Columns	add_remove_columns()	Drop columns
Rename	add_rename()	Rename columns
Calculation	add_calculation()	Tableau formula fields
Quick Calc	add_quick_calc()	Quick clean (lowercase/uppercase/trim/remove)
Change Type	add_change_type()	Change column data types
Duplicate Column	add_duplicate_column()	Duplicate (copy) a column
Aggregate	add_aggregate()	GROUP BY with SUM/AVG/COUNT
Pivot	add_pivot()	Rows to columns
Unpivot	add_unpivot()	Columns to rows
Output	add_output_server()	Publish to Tableau Server

Examples

See the examples/ directory for complete demos:

• demo_basic.py - Input, Join, Output
• demo_cleaning.py - Filter, Calculate, Rename
• demo_aggregation.py - Union, Aggregate, Pivot
• demo_comprehensive.py - All features combined

MCP Server

cwprep includes a built-in Model Context Protocol server, enabling AI clients (Claude Desktop, Cursor, Gemini CLI, etc.) to generate TFL files directly.

Prerequisites

Method	Requirement
uvx (recommended)	Install uv — it auto-downloads cwprep[mcp] in an isolated env
pip install	Python ≥ 3.8 + pip install cwprep[mcp]

Quick Start

# Local (stdio)
cwprep-mcp

# Remote (Streamable HTTP)
cwprep-mcp --transport streamable-http --port 8000

Client Configuration

All clients below use the uvx method (recommended). Replace uvx with cwprep-mcp if you prefer a local pip install.

Claude Desktop

Edit config file:

• Windows: %APPDATA%\Claude\claude_desktop_config.json
• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "cwprep": {
      "command": "uvx",
      "args": ["--from", "cwprep[mcp]", "cwprep-mcp"]
    }
  }
}

Cursor

Settings → MCP → Add new MCP server, or edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "cwprep": {
      "command": "uvx",
      "args": ["--from", "cwprep[mcp]", "cwprep-mcp"]
    }
  }
}

VS Code (Copilot)

Create .vscode/mcp.json in project root:

{
  "servers": {
    "cwprep": {
      "command": "uvx",
      "args": ["--from", "cwprep[mcp]", "cwprep-mcp"]
    }
  }
}

Windsurf (Codeium)

Edit ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "cwprep": {
      "command": "uvx",
      "args": ["--from", "cwprep[mcp]", "cwprep-mcp"]
    }
  }
}

Claude Code (CLI)

claude mcp add cwprep -- uvx --from "cwprep[mcp]" cwprep-mcp

Gemini CLI

Edit ~/.gemini/settings.json:

{
  "mcpServers": {
    "cwprep": {
      "command": "uvx",
      "args": ["--from", "cwprep[mcp]", "cwprep-mcp"]
    }
  }
}

Continue (VS Code / JetBrains)

Edit ~/.continue/config.yaml:

mcpServers:
  - name: cwprep
    command: uvx
    args:
      - --from
      - cwprep[mcp]
      - cwprep-mcp

Remote HTTP Mode (any client)

Start the server:

cwprep-mcp --transport streamable-http --port 8000

Then configure your client with the endpoint: http://your-server-ip:8000/mcp

Available MCP Capabilities

Type	Name	Description
🔧 Tool	generate_tfl	Generate .tfl file from flow definition
🔧 Tool	list_supported_operations	List all supported node types
🔧 Tool	validate_flow_definition	Validate flow definition before generating
📖 Resource	cwprep://docs/api-reference	SDK API reference
📖 Resource	cwprep://docs/calculation-syntax	Tableau Prep calculation syntax
💬 Prompt	design_data_flow	Interactive flow design assistant
💬 Prompt	explain_tfl_structure	TFL file structure explanation

AI Skill Support

This project includes a specialized AI Skill for assistants like Claude or Gemini to help you build flows.

• Location: .agents/skills/tfl-generator/
• Features: Procedural guidance for flow construction, API reference, and Tableau Prep calculation syntax rules.

Directory Structure

cwprep/
├── .agents/skills/      # AI Agent skills and technical references
├── src/cwprep/          # SDK source code
│   ├── builder.py       # TFLBuilder class
│   ├── packager.py      # TFLPackager class
│   ├── config.py        # Configuration utilities
│   └── mcp_server.py    # MCP Server (Tools, Resources, Prompts)
├── examples/            # Demo scripts
├── docs/                # Documentation
└── tests/               # Unit tests

Configuration

Create config.yaml for default settings:

database:
  host: localhost
  username: root
  dbname: mydb
  port: "3306"
  db_class: mysql

tableau_server:
  url: http://your-server
  project_name: Default

Changelog

See changelog.md for version history.

License

MIT License