Glossary / Model Context Protocol (MCP)

Model Context Protocol (MCP)

Published: October 1, 2025

What is Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open standard that allows AI models to securely connect with external tools, applications, and data sources. It provides a structured framework for models to extend their capabilities beyond training data by retrieving additional context, executing actions, and integrating into real-world workflows.

Why MCP matters for AI

MCP solves a major limitation of large language models: their reliance only on training data and conversation input. By using MCP, organizations can give models access to live systems and trusted information, making AI more reliable, context-aware, and aligned with business needs. This means MCP plays a critical role in moving AI from experimental use cases into enterprise-grade solutions.

Key features of the Model Context Protocol

MCP is designed to be open, extensible, and secure. Its main features include:

Standardized interface for AI models – a universal method for models to communicate with external systems.
Context integration in AI workflows – retrieve and inject relevant external data to improve accuracy.
Action execution capabilities – let AI trigger approved operations such as database queries or updates.
Security and governance for AI tools – ensure safe, controlled access to systems and data.
Cross-platform compatibility – designed to work across multiple clouds, platforms, and applications.

Together, these features make MCP a powerful foundation for extending AI functionality while maintaining trust, consistency, and interoperability.

Benefits of using the Model Context Protocol

Organizations that adopt MCP can:

Extend AI functionality without retraining models.
Improve AI accuracy with real-time data grounding.
Integrate AI into business systems more flexibly.
Strengthen security with access governance and traceability.

In practice, these benefits help companies operationalize AI more effectively, ensuring it delivers real business value without introducing unnecessary risk.

Challenges of the MCP

Like any emerging standard, MCP comes with challenges that organizations should consider:

Adoption curve – As a relatively new protocol, it requires buy-in from tool providers, developers, and enterprises.
Integration complexity – Connecting diverse systems through MCP may require additional technical expertise.
Security considerations – While MCP emphasizes governance, improper implementation could expose sensitive data.
Evolving standards – MCP is still maturing, which means organizations need to adapt as best practices and guidelines develop.

These challenges don’t diminish MCP’s potential but highlight the importance of thoughtful planning, strong governance, and continuous evaluation when adopting the protocol.

Examples of MCP in action

Organizations are already experimenting with MCP to bring real-time intelligence into workflows. Some common examples include:

Customer support AI – A model uses MCP to pull customer history from a CRM before responding, ensuring accurate and personalized answers.
Identity and access management – AI agents can use MCP to check authorization policies before granting access to sensitive resources.
Financial services automation – A model retrieves up-to-date market data through MCP when generating investment insights or compliance reports.
IT operations – Virtual assistants use MCP to query monitoring systems, detect anomalies, and even trigger remediation actions.

These examples show how MCP moves AI from being a standalone tool to becoming an integrated part of enterprise systems, where context and action are critical.

How MCP relates to identity and access management

The Model Context Protocol has a natural connection to identity and access management (IAM), since IAM systems are among the most important external tools that AI models need to interact with securely. MCP provides a standardized way for AI to access identity data, enforce access policies, and trigger authorization checks without bypassing governance.

For example, when an AI agent needs to determine whether a user should be granted access to an application, MCP can be used to query an identity provider or policy engine in real time. This ensures that the AI system is not making decisions in isolation but is instead anchored to existing IAM rules and compliance frameworks.

By integrating with IAM, MCP enables:

Stronger security enforcement – AI agents can check entitlements and enforce least privilege.
Dynamic policy application – MCP makes it possible to apply continuous access evaluations in real time.
Auditability and compliance – All access decisions routed through MCP can be logged for governance.

This relationship is especially critical in enterprise environments, where aligning AI with IAM reduces risk and ensures that intelligent systems respect organizational boundaries and regulatory requirements.

At Strata, we see MCP as complementary to our App Fabric and Maverics Identity Orchestration Platform. Just as Maverics abstracts and unifies identity systems across multi-cloud environments, MCP offers a similar standardization layer for AI agents. When combined, these approaches allow enterprises to securely extend identity controls into AI-driven workflows, ensuring that agentic AI operates with the same governance, policy enforcement, and observability as traditional applications.