Generate Synthetic Data with the DataFramer MCP Server

The DataFramer MCP server lets you generate synthetic datasets directly from your AI coding assistant. The demo above shows the full workflow from uploading seed data to getting the final dataset, done autonomously by the AI agent.

Unlike a raw API integration, the MCP server also provides your AI assistant with detailed instructions for using DataFramer effectively. This means it can guide you through the entire workflow conversationally: gathering your requirements, choosing generation settings, polling for results, and reviewing output quality.

Common use cases include test data and mock datasets for development and QA, synthetic training data for LLM fine-tuning, and evaluation datasets for benchmarking AI pipelines. DataFramer also handles edge cases and rare scenarios, inputs that are underrepresented or hard to collect from real data, which makes it useful for stress-testing.

The platform works in two modes. In seeded mode, you upload a few example records and DataFramer learns their structure, variation, and statistical patterns to generate many more like them. In seedless mode, you describe what you need in plain text and it generates from scratch. Both modes support CSV, JSON, JSONL, plain text, PDF, and multi-file structures, including document collections and code repositories.

Getting Started

See the documentation for setup instructions.