What is Model Context Protocol? 

Model Context Protocol (MCP) is a concept and emerging open standard for defining and exchanging all relevant context information of a machine learning model in a structured, interoperable way. It encapsulates metadata about model development and deployment environments – from training data references and preprocessing steps to hyperparameters, code, dependency versions, and system settings – into a unified format​ ​(kdnuggets.com). 

By providing a vendor-agnostic protocol for context, MCP ensures that models can be seamlessly integrated with external data sources and tools, and that their lineage and environment can be understood regardless of platform​ (anthropic.com). This helps eliminate ad-hoc, custom integration code for each data source by replacing fragmented connectors with one universal interface for context sharing​ (testcollab.com). The goal of a Model Context Protocols is to facilitate reproducibility, allowing any stakeholder to reconstruct the conditions under which a model was trained or is being used, and interoperability in MLOps workflows by standardizing how model metadata is communicated.

Cross-System Integration is the main use case for MCP as it integrates AI models with enterprise data systems and tools in a plug-and-play fashion. 

Rather than writing custom code for each data source (databases, file storage, APIs) to provide context to a model, an organization can adopt a Model Context Protocol so that any compliant model can connect to any compliant data source or service​ (testcollab.com). For instance, a question-answering AI assistant in a company could use MCP to interface with various internal knowledge bases (wikis, ticketing systems, databases) through a unified mechanism, requesting information and receiving it in a format it understands, without bespoke adapters for each system​.

Source; https://modelcontextprotocol.io/introduction

Key Components

• Context Metadata Schema: MCP defines a schema of metadata that collectively represents a model’s context. This typically includes the training dataset identifiers (or dataset URIs), data preprocessing steps, model configuration and hyperparameters, training code or pipeline version, library/framework versions, and hardware or runtime environment details​.
• Standardized Interface: As a protocol, MCP provides a standardized interface (often API or file format) through which context data is logged and retrieved. Instead of each tool having its own format for model metadata, MCP offers a common format so that different systems (e.g. experiment trackers, model registries, or inference servers) can exchange context information easily​. In many implementations, this takes a client-server form: for instance, an ML model or application (client) can request or send context to a context server that adheres to MCP specifications​. This standardization promotes interoperability – a model saved with its context in one framework can be understood and reused by another, as long as both support the protocol. It also reduces integration complexity by acting as a single “plug” for connecting models to data sources or services (analogous to a universal port)​
• Two-Way Context Exchange: A robust Model Context Protocol not only documents static metadata but also supports dynamic context exchange during model operation. This means an AI system can query external data sources for additional context at runtime and incorporate that into its reasoning, and conversely external systems can log updates back to the model’s context​. For example, a large language model could use MCP to fetch relevant documents from a knowledge base when answering a question, via a secure two-way connection, instead of being limited to its training data​.