The video demonstrates how to quickly set up an MCP (Model Context Protocol) server using OpenAI’s file store to manage and retrieve conversation data in a vector file store. The presenter walks through the process of organizing conversation files, building the server with Google’s Gemini 2.5 Pro, and showcases the ease of storing and summarizing conversations, highlighting the user-friendly nature of the setup.
In the video, the presenter demonstrates how to set up an easy memory database using OpenAI’s file store to create an MCP (Model Context Protocol) server. The goal is to leverage this setup for storing and retrieving conversations in a vector file store, which can be utilized by any client that supports MCP. The presenter outlines the process of gathering initial conversation data, which can be uploaded to the file store, and emphasizes the simplicity of managing this database with OpenAI’s tools.
The setup begins with the presenter copying conversations from various sources, such as ChatGPT and Gemini, and organizing them into text files labeled with dates. This initial step is crucial for creating a structured database. The presenter then navigates to the OpenAI dashboard to create a vector store, where the uploaded conversation files will be stored. The presenter sets parameters for chunk size and overlap, ensuring that the data is appropriately formatted for future retrieval.
Next, the video transitions to building the MCP server using Google’s Gemini 2.5 Pro. The presenter highlights the importance of consulting the documentation for both the MCP server and the file store to gather the necessary context for development. The presenter prepares a prompt to instruct Gemini to create the MCP server, specifying that it should return the top five results based on user queries. This step involves initializing dependencies and creating the necessary directory structure for the server.
Once the server is built, the presenter connects it to cloud code, passing the OpenAI API key and vector store ID as arguments. The successful connection is confirmed by listing the MCP servers, which shows that the memory store is now operational. The presenter tests the functionality by uploading a new conversation file to the vector store and retrieving information about a previous heart rate recovery test, demonstrating the server’s ability to search and return relevant data.
Finally, the presenter showcases the ease of storing and summarizing conversations using the MCP server. They illustrate how to create a summary of a lengthy conversation and save it as a new file in the vector store. The video concludes with the presenter expressing satisfaction with the MCP setup, highlighting its user-friendly nature for managing memory storage and retrieval. They encourage viewers to explore this method for their own applications, emphasizing the flexibility and efficiency of using an MCP server with OpenAI’s file store.