Editorial
AI agents promise to revolutionize business operations by automating tasks, providing insights and interacting with customers in increasingly sophisticated ways. However, connecting these agents reliably and efficiently to real-time information and enabling them to take meaningful action remains a significant hurdle. This integration complexity often limits the scope and effectiveness of AI deployments.
To address this challenge, Anthropic created the Model Context Protocol (MCP), which some refer to as the "USB-C port for AI." The protocol focuses less on extending the core AI models and more on standardizing how AI applications connect to and utilize external tools and data sources. It provides a foundational layer for building integrated, interoperable AI solutions within the enterprise.
Anthropic has demonstrated its use by developing servers, tooling and software development kits (SDKs) that align with its core principles, demonstrating the protocol's viability. While a single, universally adopted protocol is not yet here, the underlying principles are gaining traction, supported by a growing community exploring open standards for agent interaction.
With added support from firms like OpenAI, Replit and a major open source ecosystem, the protocol is gaining early traction.
Where MCP Fits in the Enterprise
The practical implications for businesses are substantial. Model Context Protocol unlocks smarter, more contextually aware AI agents by seamlessly connecting them to your unique, real-time business data and moving beyond generic knowledge to specific operational insights.
A major selling point is the rapid integration of multiple data sources, such as customer relationship management (CRM) systems, enterprise resource planning (ERP) software, marketing analytics or support platforms, without the traditional technical friction and long development cycles.
While we’ve seen major software vendors announcing agentic capabilities, most are focusing on the safer side of automating repetitive tasks. Allowing agents to interact and operate with real-time business data presents a huge opportunity and major challenges. Adding this context in a controlled, secure way across different AI platforms makes a profound difference.
Possible use cases for MCP range from speeding up internal software development workflows by integrating tools like Slack, Jira and Figma, to powering sophisticated, data-driven customer-facing solutions. Furthermore, strategically choosing vendors who support or plan to support MCP-like standards helps future-proof your AI stack, ensuring greater flexibility and avoiding vendor lock-in down the road.
Related Article: How to Build Multi-Agent Workflows That Don't Fall Apart
The Inner Workings of Model Context Protocol
MCP provides AI applications with a "universal remote," enabling them to identify available actions (Tools) and access necessary information (Resources) on-demand, guided perhaps by pre-defined prompts or user instructions.
Instead of relying on developers to hardcode integrations at design time, the AI system can "read the instructions" for an external system at runtime. This shift decouples the AI from fixed integrations, allowing businesses to evolve their capabilities, plug in new tools or update data sources much faster, responding to change more quickly and significantly reducing development overhead. In the long term, the MCP ecosystem envisions rich, composable AI applications and sophisticated agentic behaviors enabled by potentially bidirectional communication.
Creating a protocol from scratch is difficult, so the Anthropic team was inspired by established protocols such as LSP — Language Server Protocol — used in software development for standardized editor-tool interaction. Additionally, MCP aims for simplicity and extensibility, with established formats like JSON RPC.
In its earlier days, the proponents of REST (Representational State Transfer) added a forward looking constraint called HATEOAS — Hypermedia as the Engine of Application State. It provided the vision of a fully dynamic client-server interaction via hypermedia that didn't achieve widespread adoption in the web API world. Model Context Protocol revives this powerful idea in the context of AI.
The Integration Bottleneck MCP Aims to Solve
Today, integrating AI frequently means developers must painstakingly pre-program each specific connection between the AI and an external system (like a CRM, ERP or internal database). This method is brittle — changes to the external tool often require developers to rewrite the integration. It's also slow, hindering the rapid deployment and adaptation needed in today's business environment.
MCP hopes to change this paradigm. The goal is to allow AI applications to discover and connect to new tools and data sources dynamically, in real-time, much like a person clicks through links on a website to navigate and interact.
In the earlier days after discovering large language models' capabilities and understanding their limitations in using external knowledge, many teams started adopting techniques like retrieval-augmented generation (RAG), which primarily focuses on representing content in vector space and fetching relevant snippets related to the query to inform responses.
While useful, RAG doesn't inherently solve the problem of enabling AI agents to interact with multiple live data sources or execute actions via software tools and APIs. A more robust and standardized approach is needed when enabling these dynamic capabilities, especially as part of existing software solutions.
Related Article: Is Your Data Good Enough to Power AI Agents?
What to Do Now to Stay Competitive in the MCP Era
Despite the typical challenges facing new standards, MCP is gaining significant traction due to strong enterprise demand and a growing developer community. For business leaders, this represents a crucial shift requiring strategic action: audit your AI infrastructure, launch focused pilot projects, evaluate vendor commitments to interoperability and establish internal champions to explore implementation opportunities.
Webinar
Sep
25
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Register
Webinar
Sep
25
Call Spoofing Trends Financial Institutions Can't Afford to Ignore
Don't let robocalls sabotage your customer relationships. Learn how to secure your voice channel.
Register
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
Webinar
On demand
CX Reality Check: What Your Customers Are Really Thinking
Register now and get the insights you need to not just meet rising expectations, but exceed them.
Watch Now
Webinar
On demand
Content Leaders Collective: What a Good CCMS Actually Looks Like
Discover how content leaders transform clunky systems into growth engines — and why your competitors are ahead.
Watch Now
Webinar
On demand
From Hype to High-Impact CX Strategies That Actually Scale
Turn buzzworthy AI and outsourcing trends into measurable CX wins with fresh 2025 data.
Watch Now
As Model Context Protocol evolves from emerging trend to essential infrastructure, organizations must prepare strategically — conducting small experiments now to develop competitive advantages, while positioning themselves to fully leverage these deeply integrated AI systems before competitors do. The future belongs to businesses that can harness AI agents connected to their exact data and tools when needed.
Learn how you can join our contributor community.
