The Problem Statement and Project Blueprint
Explore how to build a multimodal Image Research Assistant that bridges vision and text retrieval using MCP. Understand the architecture involving specialized servers for visual analysis and Wikipedia research, orchestrated through an MCP client for seamless information flow. This lesson prepares you to develop a modular, scalable AI assistant that automates complex image research workflows.
Our agent can now use text-based tools to interact with web APIs and private knowledge bases. Many tasks also require information contained in images and diagrams. In this module, we will extend the system to multimodal inputs by building an Image Research Assistant. This application demonstrates how MCP can coordinate multiple capabilities, such as image analysis and text retrieval, within a single workflow.
The problem statement
A researcher browsing a digital archive may encounter a photograph of an unidentified historic building. The image lacks identifying context and includes no caption or metadata identifying the building or its location. The researcher may need to answer several questions: What is this building? Where is it located? What is its historical significance?
To answer these questions, the researcher would have to embark on a disjointed, manual workflow. First, they might use a reverse-image search tool to hopefully identify the landmark. Then, armed with a name, they would switch to a new browser tab to search Wikipedia or a search engine for articles about its history. This ...