The Model Context Protocol (MCP) is a standardized framework designed to manage and share contextual information across machine learning (ML) models and systems. It was created to address the challenge of maintaining consistent, reusable context—such as user inputs, environmental data, or prior model outputs—when integrating multiple models into a single workflow. Without a shared protocol, developers often resort to custom solutions for passing context between models, leading to fragmented code, compatibility issues, and redundant data processing. MCP provides a unified structure for defining, storing, and exchanging context, ensuring that models can operate with a shared understanding of the data they process.
A key motivation for MCP was the growing complexity of ML pipelines. For example, consider a customer support chatbot that uses separate models for language translation, sentiment analysis, and response generation. Without MCP, each model might require custom code to track user intent, conversation history, or error states. This creates brittleness—if one model changes its output format, others might break. MCP solves this by standardizing how context is represented (e.g., using JSON schemas) and versioned, allowing models to access only the data they need. It also supports partial updates, so a sentiment analysis model can add its results to the context without overwriting other fields like timestamps or user metadata.
From a technical perspective, MCP is designed to be lightweight and language-agnostic. It defines APIs for reading, writing, and subscribing to context changes, which can be implemented in libraries for Python, JavaScript, or other languages. For instance, a fraud detection system could use MCP to share transaction details, risk scores, and user profiles across rules-based and ML-based components. By reducing boilerplate code for context management, MCP lets developers focus on model logic rather than integration glue. Its creation reflects a practical need in the industry: as ML systems scale, standardized protocols are essential to maintain interoperability, reduce errors, and simplify debugging in multi-model environments.