Which integrations are available for this server?

Allows the programmatic generation of Tableau Prep data flows that connect to and extract data from MySQL databases as input sources. Enables the definition of Tableau Prep data flows that integrate PostgreSQL databases for automated data preparation and ingestion.

cwprep - Text-to-PrepFlow Engine

cwprep is a Python-based engine that enables Text-to-PrepFlow generation.

By reverse-engineering the .tfl JSON structure and providing a built-in MCP (Model Context Protocol) server, cwprep acts as a bridge between LLMs (like Claude, Gemini) and Tableau Prep. You can now generate, modify, and build data cleaning flows simply through natural language conversations or Python scripts, without ever 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_tfl("./my_flow.tfl", flow, display, meta)

By default, both the SDK and MCP output only the final .tfl/.tflx archive. Use save_to_folder() only when you explicitly want the exploded folder for inspection.

Features

Feature	Method	Description
Database Connection	`add_connection()`	Connect to MySQL/PostgreSQL/SQL Server
File Connection	`add_file_connection()`	Connect to Excel (.xlsx/.xls) or CSV files
SQL Input	`add_input_sql()`	Custom SQL query input
Table Input	`add_input_table()`	Direct table connection
Excel Input	`add_input_excel()`	Read from Excel worksheet
CSV Input	`add_input_csv()`	Read from CSV file
CSV Union	`add_input_csv_union()`	Merge multiple CSV files
Join	`add_join()`	left/right/inner/full joins (single or multi-column)
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
TFLX Packaging	`build(is_packaged=True)`	Generate .tflx with embedded data files
SQL Translation	`SQLTranslator`	Translate TFL flows to equivalent ANSI SQL

Examples

See the examples/ directory for complete demos:

demo_basic.py - Input, Join, Output
demo_cleaning.py - Filter, Calculate, Rename
demo_field_operations.py - Quick Calc, Change Type, Duplicate Column
demo_aggregation.py - Union, Aggregate, Pivot
demo_comprehensive.py - All features combined
prompts.md - 8 ready-to-use MCP prompt templates for AI-driven flow generation

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

TIP

Upgrading? If you previously used uvx with an older version, clear the cache to pick up the latest release:

uv cache clean cwprep

Client Configuration

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

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"]
    }
  }
}

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

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

Create .vscode/mcp.json in project root:

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

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

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

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

Edit ~/.gemini/settings.json:

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

Edit ~/.continue/config.yaml:

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

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/.tflx file from flow definition
🔧 Tool	`translate_to_sql`	Translate flow definition or .tfl file to ANSI SQL
🔧 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
📖 Resource	`cwprep://docs/best-practices`	Common pitfalls and flow design rules
💬 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: MCP server index with fallback SDK usage guide. Detailed API and syntax references are served via MCP Resources from src/cwprep/references/.

Directory Structure

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

Configuration

Create config.yaml for default settings:

# MySQL (default)
database:
  host: localhost
  port: 3306
  dbname: mydb
  type: mysql

# SQL Server (Windows Authentication)
# database:
#   host: localhost
#   type: sqlserver
#   authentication: sspi
#   schema: dbo

# PostgreSQL
# database:
#   host: localhost
#   port: 5432
#   dbname: mydb
#   type: postgres

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

Changelog

See changelog.md for version history.

License

AGPL-3.0 License

cwprep-mcp

cwprep - Text-to-PrepFlow Engine

Installation

Quick Start

Features

Examples

MCP Server

Prerequisites

Quick Start

Client Configuration

Available MCP Capabilities

AI Skill Support

Directory Structure

Configuration

Changelog

License

Resources

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