cwprep 0.2.2

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
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.

Installation

pip install cwprep[mcp]

Usage

# Local (stdio)
cwprep-mcp

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

MCP Server Configuration

Example config for Claude Desktop:

Add to your Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "cwprep": {
      "command": "python",
      "args": ["-m", "cwprep.mcp_server"]
    }
  }
}

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