DataDesigner — MCP Server by NVIDIA-NeMo

by NVIDIA-NeMo · MCP Server · ★ 1.8k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

About DataDesigner

🎨 NeMo Data Designer Generate high-quality synthetic datasets from scratch or using your own seed data. Welcome! Data Designer helps you create synthetic datasets that go beyond simple LLM prompting. Whether you need diverse statistical distributions, meaningful correlations between fields, or validated high-quality outputs, Data Designer provides a flexible framework for building production-grade synthetic data. What can you do with Data Designer? Generate diverse data using statistical samplers, LLMs, or existing seed datasets Control relationships between fields with dependency-aware generation Validate quality with built-in Python, SQL, and custom local and remote validato

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Quick Facts

Stars1,838
Forks166
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score49.032/100
Open Issues46
Last Updated2026-05-13
Created2025-10-16
Platformsmcp, python
Est. Tokens~40043k

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Frequently Asked Questions

What is DataDesigner?

DataDesigner is 🎨 NeMo Data Designer: Generate high-quality synthetic data from scratch or from seed data.. It is categorized as a MCP Server with 1.8k GitHub stars.

What programming language is DataDesigner written in?

DataDesigner is primarily written in Python. It covers topics such as agentic-ai, data-augmentation, data-generation.

How do I install or use DataDesigner?

You can find installation instructions and usage details in the DataDesigner GitHub repository at github.com/NVIDIA-NeMo/DataDesigner. The project has 1.8k stars and 166 forks, indicating an active community.

What license does DataDesigner use?

DataDesigner is released under the Apache-2.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to DataDesigner?

The top alternatives to DataDesigner on Agent Skills Hub include MemOS, git-mcp, cursor-talk-to-figma-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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