7 Things to Know About MCP (Model Context Protocol) in 2025

Written by

AdSkate

Published on

October 7, 2025

AI/Machine Learning

MCP

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Table of contents:

Listen to the podcast version here:

1. Introduction: Why Everyone’s Talking About MCP in 2025

If you follow the frontier of AI infrastructure, you’ve probably started hearing about MCP, the Model Context Protocol. In 2025, MCP is emerging as one of the most talked-about developments in how AI systems connect, collaborate, and scale. But what is it exactly, and why is the industry paying attention?

The Integration Bottleneck in Modern AI

Over the past few years, one frustration has become clear: no matter how smart a large language model (LLM) is, its usefulness is limited without contextual awareness.

To perform effectively, models need to reach beyond their training data and query live systems, analytics dashboards, CRMs, and APIs.

Until now, that required custom engineering, unique connectors, and one-off logic per system, all of which made scaling AI integration a slow, brittle, and costly process.

MCP aims to fix that by standardizing how models and systems share context, not just data.

Enter MCP: The “USB-C Port for AI”

Anthropic introduced the Model Context Protocol (MCP) as an open standard that provides a uniform, model-agnostic interface for connecting AI models to external tools and data sources.

Think of it as a “universal adapter for AI systems”, similar to how USB-C standardized connectivity across devices.

In practice, MCP allows developers to build one reusable connector that works across models and ecosystems, eliminating the redundancy that has long slowed AI progress.

(OpenAI Developer Docs – Model Context Protocol)

Why It Matters in 2025

Cross-model support: MCP is model-agnostic and now supported by OpenAI, Anthropic, and Hugging Face.
Scalability: Developers can build once and reuse across systems.
Transparency: The protocol’s structure aligns with AI governance standards emerging globally.

(VentureBeat – The Interoperability Breakthrough)

Diagram depicting unified AI experience with central hub, conversational AI, and tool access integrated through Model Context Protocol (MCP) for seamless contextual AI workflows.

2. What Exactly Is the Model Context Protocol (MCP)?

The Model Context Protocol is an open standard defining how models, tools, and systems communicate and share context. It allows different AI systems to exchange structured information in real time, not only raw data but also metadata that gives that data meaning.

Where APIs let applications share information, MCP lets AI systems share understanding.

The Core Idea: Context as a Shared Language

Imagine a marketing analytics model analyzing campaign performance.

The context includes performance history, audience segments, and creative variations.

Through MCP, the model can access all that, dynamically, without hard-coded integrations.

This design makes AI systems more adaptive and situationally intelligent, they can reason based on current realities, not static datasets.

(Anthropic – MCP Specification)

How MCP Works

MCP defines a handshake between:

Clients (initiators, like AI agents or apps)
Servers (context or tool providers)
Protocol Layer (a neutral interface defining how context is shared and updated)

This setup makes AI integration interoperable, secure, and traceable.

(Anthropic - What is MCP?)

3. How MCP Enables the Next Generation of AI Agents

The boom in AI agents, autonomous systems that plan and act, has outpaced their ability to communicate. Each agent often operates in isolation, with its own APIs and data sources. MCP fixes this by introducing a shared protocol for context exchange.

The Problem: Agents Without Shared Context

AI agents today face:

Brittle one-off integrations
Limited visibility into broader workflows
Fragmented data silos

How MCP Changes the Landscape

With MCP, agents can:

Discover tools automatically via MCP servers
Exchange state information dynamically
Collaborate across environments through shared context

This creates ecosystems of agents, where creative, analytics, and campaign agents can communicate seamlessly.

Venn diagram showing synergy between AI agents and marketing campaigns for collaborative optimization, illustrating how Model Context Protocol (MCP) enables connected creative intelligence in 2025.

4. Why MCP Is Critical for AI Transparency and Control

As AI becomes embedded in business operations, transparency has become essential. MCP is one of the first frameworks to bake governance and auditability directly into how AI systems exchange information.

Every MCP transaction, every tool invocation, context exchange, or model interaction, can be logged, permissioned, and audited.

MCP’s Governance Architecture

Explicit Context Declarations: Models can only access predefined contexts.
Permission Controls: Access requires authentication, preventing overreach.
Traceability: Logs track all context interactions.
Sandboxed Environments: Models operate within defined data boundaries.

(Deepset.ai – Understanding MCP)

This approach satisfies the governance expectations of laws like the EU AI Act, giving enterprises both capability and compliance.

5. The Companies and Tools Adopting MCP in 2025

In 2025, MCP adoption is accelerating across the AI ecosystem:

Anthropic introduced the protocol.
OpenAI adopted it across the Agents SDK.
Hugging Face, LangChain, and Deepset integrated it into their developer frameworks.
AdSkate operationalized it in marketing analytics.

(Anthropic GitHub)

AdSkate: Bringing MCP to Creative Analytics

AdSkate has integrated audience analysis and creative testing directly into the MCP framework.

Advertisers can now test creatives against 1,000+ synthetic audiences directly from their conversational AI tools.

This enables:

Seamless testing within AI assistants
Performance feedback from simulated audiences
Context-driven campaign optimization

(The Importance of MCP in Advertising)

6. Real-World Applications of MCP

MCP’s flexibility has led to real-world adoption across industries.

Creative Analytics & Marketing

Marketing teams are using MCP to connect creative testing, analytics, and automation.

AI systems can now exchange real-time context to optimize creative direction dynamically.

AdSkate’s Synthetic Audiences

AdSkate’s integration allows advertisers to test campaign creatives against 1,000+ synthetic audiences using conversational AI assistants.

This transforms creative iteration into a contextual feedback loop, reducing guesswork and accelerating testing cycles.

Data Analytics & CX

MCP allows analytics and CX tools to feed models live data safely, driving predictive intelligence without compromising security.

7. What’s Next: The Future of MCP and Contextual AI

If 2025 is the year of adoption, 2026 will be the year of expansion.

MCP is evolving into the standard infrastructure for contextual AI.

Future trends include:

Agent Webs: Distributed AI agents sharing knowledge in real time
Decentralized Context Networks: Open registries of MCP-compatible tools
Continuous Learning Ecosystems: Models adapting autonomously using shared context

(VentureBeat – Interoperability Breakthrough)

(The Drum – AI, privacy, and the federal crossroads)

For AdSkate, MCP represents the next phase of creative intelligence, where testing, optimization, and analytics operate in one contextual layer.

Illustration of AI collaboration showing how AI agents, contextual data, and advanced technologies combine to drive enhanced decision-making using Model Context Protocol (MCP) for interoperability.

Conclusion: The Start of the Context-Aware AI Era

The Model Context Protocol is no longer an experiment. It’s the foundation of contextual AI, powering interoperability, accountability, and collaboration across industries.

With AdSkate, OpenAI, and Anthropic pioneering its real-world use, MCP is proving how open standards can reshape creativity, analytics, and intelligence itself.

The future of AI belongs not to those with the most data, but to those with the richest context, and MCP is how we’ll get there.

(Hugging Face – MCP Course)