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Documentation Index

Fetch the complete documentation index at: https://docs.dataframer.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Getting access

  1. Register at app.dataframer.ai
  2. Request access to Dataframer at [email protected]
  3. Get your API key from the dashboard (Settings → API Keys)
  4. Set the environment variable:
export DATAFRAMER_API_KEY="your-api-key"

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Python SDK

Install the SDK: 
pip install pydataframer
Quick taste: 
from dataframer import Dataframer

client = Dataframer()  # uses DATAFRAMER_API_KEY env var

spec = client.dataframer.specs.create(
    name="Customer Support Tickets",
    generation_objectives="Generate realistic customer support tickets...",
)
# ... poll for completion, start a run, download results
See the Basic Use of Python SDK tutorial for a complete, runnable example.

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MCP

MCP lets AI assistants (Claude Code, Cursor, etc.) interact with DataFramer directly.

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Claude Code

claude mcp add --transport http \
  --header "Authorization: Bearer $DATAFRAMER_API_KEY" \
  --scope user dataframer https://df-api.dataframer.ai/mcp
Unlike the raw API, MCP also provides your AI assistant with detailed instructions on how to use DataFramer effectively — so it can guide you through the entire workflow conversationally.

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Next steps

Core Concepts

Understand how DataFramer works

Basic Use of Python SDK

Complete runnable example

API Reference

Full endpoint documentation