syahriikram/proj_mcp_synthetic_data
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This document provides a comprehensive overview of setting up a Model Context Protocol (MCP) server for synthetic data generation using the SDV library.
Generate Tool
Creates synthetic data from real data using SDV Synthesizer.
Evaluate Tool
Evaluates quality of synthetic data by assessing statistical similarity to real data.
Visualize Tool
Generates visualization to compare real and synthetic data for specific columns.
mcp_sdg
Creating MCP server for synthetic data generation using SDV library.
Setup
Install uv
as the main package manager
brew install uv
uv init
uv venv
source .venv/bin/activate
Packages
uv add sdv fastmcp pandas
Run inspector to test locally: npx @modelcontextprotocol/inspector uv run main.py
Install Claude Desktop
as MCP Client (or any other MCP Clients).
- Update
claude_desktop_config.json
for Claude Desktop to connect to the MCP Server (locally hosted)
MCP Tools
- Generate Tool: creates synthetic data from real data using SDV Synthesizer
- Evaluate Tool: evaluates quality of synthetic data in comparison to real data by assessing statistical similarity to determine which real data patterns are captured by the synthetic data
- Visualize Tool: generates visualization to compare real and synthetic data for specific column
Execution
Prompts to run in Claude Desktop:
- Generate synthetic data for the data present in the folder "..."
- Evaluate the synthetic data that has been generated for the actual data folder located at "..."
- Visualize amenities_fee column in the guests table, and compare the distribution of synthetic data to that of real data for this specific column
Technology Stack
Python
Core Concepts
AI
, MCP
Can you help me do the same?
Happy to work with you, contact me at syahriikram@gmail.com