Does MCP Kill Vector Search?

The Model Context Protocol (MCP) introduces a novel approach for AI systems to interact with data sources by providing a standardized interface that allows agents to query real-time data directly from specialized servers, such as Salesforce or Jira, without relying on traditional vector indexing and retrieval pipelines. While MCP offers advantages in accessing structured data and executing actions through direct APIs, it faces challenges in delivering comprehensive results across multiple sources due to the lack of unified ranking and potential latency issues. Despite these limitations, MCP shows promise in enhancing real-time data manipulation and could eventually improve authentication integration. However, unstructured data still requires preprocessing and intelligent indexing to be effectively utilized by AI agents, suggesting a hybrid approach that combines MCP with traditional indexing for optimal results. Platforms like LlamaCloud aim to bridge this gap by providing advanced document parsing and indexing capabilities, thus enabling AI systems to harness the full potential of both structured and unstructured data sources.