Building an End-to-End Data Pipeline with Custom NER on Unstructured using MCP

Unstructured offers a user-friendly platform for managing unstructured data, crucial for organizations utilizing GenAI applications, through an open standard called the Model Context Protocol (MCP), which connects advanced data processing capabilities with LLM interfaces like Claude Desktop. MCP, developed by Anthropic, serves as a universal connector, enabling effective communication between LLMs and external applications by standardizing AI integration and offering flexibility in choosing models and vendors. This protocol follows a client-server model where LLM applications act as clients requesting information, and services like the Unstructured API serve as servers responding to these requests with functionalities categorized as resources, tools, and prompts. The integration of MCP with the Unstructured API facilitates constructing custom data pipelines, such as an end-to-end pipeline for processing documents from an Amazon S3 bucket with custom Named Entity Recognition, using natural language commands to streamline workflow management. This approach not only simplifies complex data processing tasks but also enhances security by allowing data to remain within the user's infrastructure, thus providing a powerful and flexible framework for custom data pipeline creation, while promoting a more connected AI ecosystem.