tfl-generator

name	tfl-generator
description	Generate Tableau Prep flows (.tfl/.tflx) using cwprep MCP Server or Python SDK.

TFL Generator

MCP Server (Recommended)

When the cwprep MCP Server is connected, use MCP Tools directly:

Tool	Purpose
generate_tfl	Generate .tfl/.tflx file from a flow definition
validate_flow_definition	Validate definition before generating
list_supported_operations	List all supported node types and parameters

Before generating, read MCP Resources for context:

Resource	Content
cwprep://docs/api-reference	SDK API reference (database + file connections, all transforms)
cwprep://docs/calculation-syntax	Tableau Prep formula syntax (differs from SQL!)
cwprep://docs/best-practices	Common pitfalls and flow design rules

Supported Connection Types

• Database: MySQL, PostgreSQL, SQL Server (SSPI + username/password)
• File: Excel (.xlsx/.xls), CSV — auto-detected from extension

Output Formats

• .tfl — Standard flow (references external data)
• .tflx — Packaged flow (embeds data files inside)

Fallback: Python SDK

If MCP is unavailable, use the SDK directly:

from cwprep import TFLBuilder, TFLPackager

builder = TFLBuilder(flow_name="My Flow")

# Database connection
conn_id = builder.add_connection(host="localhost", username="root", dbname="mydb")
orders = builder.add_input_table("orders", "orders", conn_id)

# File connection (Excel/CSV)
file_conn = builder.add_file_connection("returns.xlsx")
returns = builder.add_input_excel("Returns", "Sheet1", file_conn)

# Multi-column join
joined = builder.add_join("Join", orders, returns,
    ["product_id", "order_id"], ["product_id", "order_id"])
builder.add_output_server("Output", joined, "My_Datasource")

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

See the project README for full SDK documentation.