Provides GenAI agents with a secure mechanism to inspect dataset samples before performing analysis.
Sponsored
This project demonstrates a critical component for secure GenAI workflows within enterprises: a Model Context Protocol (MCP) server designed to expose structured, restricted data access. It features a `dataset_preview` tool that allows AI agents to safely inspect the initial lines of financial sales datasets, simulating real-world scenarios where LLMs interact with data through controlled interfaces rather than direct filesystem access. This setup ensures auditable data flow and maintains a clear separation between the agent's reasoning layer and the data access layer, enhancing security and governance for sensitive business data analysis.
Key Features
011 GitHub stars
02Simulates enterprise-grade tool-based data access for LLMs
03Securely provides initial data samples (first 8 lines) from CSV files
04Supports the Model Context Protocol (MCP) standard
05Exposes a `dataset_preview` tool for GenAI agents
06Built with Node.js for a lightweight and minimal server implementation
Use Cases
01Demonstrating secure, tool-based data access patterns for large language models (LLMs)
02Enabling GenAI agents to safely inspect new datasets before commencing analysis
03Developing and testing Model Context Protocol (MCP) integrations for agent-based workflows
