What is a model context protocol (MCP)?

Model context protocol (MCP) is an open standard that enables AI models and AI-powered applications to securely connect with external tools, data sources, and systems in a consistent way. MCP creates a standardized interface between large language models (LLMs) and the business environments they operate in, making it easier for AI systems to retrieve context, execute actions, and interact with real-time data.

As businesses adopt AI tools across marketing, analytics, operations, and customer engagement, one challenge quickly emerges: AI systems are only as useful as the context they can access.

Most large language models operate in isolation by default. Without access to business systems, campaign data, customer records, or operational workflows, their outputs remain generic and disconnected from real-world execution. MCP addresses this limitation by giving AI applications a structured way to interact with external systems.

Rather than building custom integrations for every AI tool and every data source, organizations can use MCP to standardize how context is shared between systems. This reduces complexity, improves interoperability, accelerates the deployment of AI-powered workflows, and keeps analysis centralized.

For businesses operating complex martech and ad tech stacks, MCP helps connect AI systems with the platforms where critical performance data already lives.

At a high level, MCP acts as a communication layer between AI models and external tools or services.

Instead of embedding all information directly into an AI model, MCP allows the model to request information dynamically from connected systems. This information could include:

• Marketing attribution data
• Campaign performance metrics
• CRM records
• Product analytics
• BI dashboards
• Workflow automation tools
• Internal documentation
• Third-party APIs

An MCP-compatible system typically consists of three components:

The AI client

This is the application or assistant interacting with the user. It could be an AI chatbot, internal assistant, workflow automation agent, or analytics co-pilot.

The MCP server

The MCP server exposes tools, functions, and data sources in a standardized format that AI systems can access securely.

External systems and data sources

These include the business platforms and operational systems the AI needs to interact with, such as analytics platforms, ad networks, databases, CRMs, or reporting tools.

This architecture allows AI assistants to move beyond static responses and become operational tools capable of retrieving insights and triggering workflows in real time.

One of the biggest advantages of MCP is interoperability.

As organizations expand their AI ecosystems, they often encounter fragmented tooling. Different AI assistants, environments, models, and applications may require separate integrations or proprietary connectors. MCP introduces a shared protocol that reduces this fragmentation.

This creates several benefits:

• Faster AI deployment: Teams can integrate AI systems with existing infrastructure more quickly without rebuilding custom connections for every use case.
• Greater flexibility: Organizations can swap AI models or tools without redesigning their entire data integration layer.
• More scalable workflows: MCP supports composable architectures where multiple tools and systems work together through a unified framework.
• Improved governance and control: Standardized access methods make it easier to manage permissions, monitor data access, and maintain security compliance.

For enterprises increasingly investing in AI operations, interoperability is becoming essential. MCP helps future-proof AI infrastructure by reducing dependency on isolated vendor ecosystems.

As AI workflows become more integrated into marketing and analytics operations, businesses need reliable ways to connect attribution data with the tools and systems they already use. Adjust MCP allows organizations to operationalize Adjust data within their own AI environments, supporting faster analysis, automation, and decision-making.