Is MCP a better alternative to RAG for Observability?
RAG i.e. Retrieval Augmented Generation has been the main stay for developers trying to integrate data sources to LLMs. The purpose of such integration is to retrieve information from a real time, real world data system to augment the LLM’s knowledge. So, that the LLM generated response is accurate and contextual.
But as developers adopted RAG, issues like context window size limitations, cost of LLM calls, and unstructured patterns of data access showed up. There wasn’t a consistent, portable framework for connecting data systems and Models.
Recent addition to the Data + LLM problem space is MCP, which aims to be a standard framework to allow real time data retrieval and augmentation.
I tried out MCP with Parseable, with a simple goal - generate log data analysis dashboards on Parseable using Claude 3.5, based on simple prompts. In this post, I’ll walk you through the code and the outcomes. But before that, let’s understand MCP in a bit detail.
MCP i.e. Model Context Protocol is a framework by Anthropic which allows developers to build agentic workflows requiring real-time or private data in an LLM-agnostic manner (MCP Blog). This allows developers to augment their users’ experience by enabling LLMs to fetch data from anywhere like a PostgreSQL database or a FTP server or even an API.
