What is an MCP? Unlocking the Power of Connected AI

Most if not all sellers are familiar with this scenario: you’re on a call with a prospect, and to answer a single question, you have to open three different tabs.  

Revenue teams today are adopting more AI tools than ever before. But ironically, this abundance of technology can create more manual work. Tools operate in silos, forcing sellers to toggle between systems, copy-paste data, and mentally bridge the gap between disconnected sources of truth.  

This is where the Model Context Protocol (MCP) comes in. MCP is a new shared standard that allows AI agents across different platforms to exchange context and take action. Think of it as a universal language that helps your revenue stack speak coherently. For sales leaders and RevOps professionals, MCP increases seller productivity by eliminating the “toggle tax” and enabling more coordinated, AI-driven orchestration across the entire sales motion. 

In this guide, we’ll go over how connecting your AI tools in a seamless workflow can be a game changer for your revenue organization. 

What is the Model Context Protocol (MCP)? 

To understand what an MCP is, it helps to look at a familiar piece of hardware: the USB port. Before USB became the standard, connecting a printer, a mouse, or a camera to a computer required different, specialized ports and cables. It was messy and complicated. USB changed that by creating a universal standard. If a device has a USB plug, it works with your computer.  

MCP is essentially the "USB port" for AI applications. It provides a universal open standard that allows AI systems to connect and share information without the need for bespoke, brittle integrations. 

Key Terms to Know 

To understand AI agents interoperability, you need to know two main concepts: 

How Does MCP Work? 

The core problem MCP solves is fragmentation. Without a standard protocol, every connection between two apps is a point-to-point construction project. You build a bridge from App A to App B. Then you build another from App A to App C. It is expensive to maintain and slow to scale. 

With the Model Context Protocol, the architecture changes. MCP-ready tools can connect instantly because they are speaking the same language. Instead of building a bridge, you are simply plugging into the network. 

When an AI agent (the Client) needs information, it requests it via the protocol. The providing tool (the Server) delivers that context securely and in real-time. This allows intelligence to move dynamically between systems. The data doesn't just sit locked inside a dashboard; it flows to where the seller is working.  

For example, a seller drafting an email in their inbox (Client) can instantly access technical documentation from a separate knowledge base (Server) without leaving the email window. The context moves to the user, rather than the user moving to the context. 

MCP vs Traditional Integrations: What’s the Difference? 

It is important to clarify that MCP complements, rather than replaces, your existing data infrastructure. 

• Traditional Integrations (like Smart Data Enrichment, Snowflake Enrichment, or standard CRM syncs) are designed for persistent, governed data movement. They are excellent for reporting, bulk data transfer, and maintaining a historical record. You still need these to run your quarterly business reviews and maintain your system of record. 
• MCP is designed for real-time, agent-to-agent context sharing. It is about "now" functionality. It allows an AI agent to ask a question and get an immediate answer to complete a task. 

Traditional integrations handle the heavy lifting of bulk data transfers, while MCP enables real-time, agent-to-agent context sharing and actions across platforms. Together, they form a modern, flexible revenue architecture. 

5 Key Benefits of Adopting MCP 

For revenue organizations, moving toward AI agents interoperability offers distinct advantages over legacy, siloed workflows. Here are 5 key benefits: 

1. Faster workflows: Agents share context instantly. Sellers don't have to manually search for information; it is retrieved and presented the moment it is needed. 
2. Smarter decisions: AI models are only as good as the context they have. By pulling data from multiple systems (CRM, email, support tickets, usage data), your AI gains a complete view of the customer, leading to higher-quality recommendations. 
3. No more toggle tax: We reduce the cognitive load on sellers by minimizing tab switching. Copy-paste workflows become a thing of the past. 
4. Future-proof architecture: Because MCP is an open standard, you are not locked into a single vendor's "walled garden." You can swap tools in and out of your stack, and as long as they are MCP-compatible, they will connect. 
5. Built for trust and governance: MCP operates within enterprise controls. It ensures visibility into what data is being accessed and by whom, maintaining accountability and responsible AI usage. 

MCP and Outreach AI: A Unified Revenue Platform 

At Outreach, we view AI integration through MCP as a critical step in evolving our platform from a tool sellers use to a teammate that works alongside them. Outreach is at the center of this new ecosystem, capable of acting as both a provider and a consumer of intelligence. 

Outreach as an MCP Server 

When Outreach is acting an MCP Server, the platform creates value for other applications in your stack. Outreach AI surfaces deep sales insights, deal health signals, and recommended actions inside other tools you might use, such as Salesforce Agentforce, Microsoft Copilot, or general AI assistants.

Outreach as an MCP Client 

On the other hand, when Outreach is acting as an MCP Client, capabilities are enhanced by pulling in intelligence from other systems like Glean, SharePoint, or Snowflake. 

Real-World Use Cases for Revenue Teams 

But how does connecting AI tools via MCP look in practice? Here are three scenarios where this technology impacts your daily operations. 

Reps: The Productivity Boost 

When a rep finishes a client meeting, instead of manually updating Salesforce, checking a product wiki for answers to a technical question, and then writing a follow-up email, MCP streamlines the flow. An AI agent can auto-summarize the meeting, surface the relevant competitor battlecard from a different tool, and update the deal record—all in one motion. 

Managers: Proactive Risk Management 

Sales managers often struggle to identify at-risk deals until it is too late. With MCP, a manager's AI assistant can correlate CRM data with engagement signals from disparate systems. It might flag a deal where email sentiment is positive (from Outreach) but technical usage is dropping (from a product dashboard), giving the manager a holistic view they wouldn't catch by looking at one tool alone. 

RevOps: Automated Orchestration 

For RevOps, the dream is automation without constant maintenance. MCP allows you to trigger sequences and insights using data from Snowflake or other systems without manual imports. You can set up workflows where an anomaly in usage data automatically triggers a retention sequence in Outreach, with no CSV uploads required. 

Final Thoughts 

Software silos need to be left behind in 2025. MCP is the foundation for a more connected, intelligent future where AI agents collaborate across the revenue stack. 

By adopting tools that support MCP server vs client architecture, you’re building a tech stack that works as a unified team. And Outreach is committed to orchestrating these AI-driven revenue workflows, ensuring your data is not just available, but actionable, exactly when your team needs it. To find out more about the game changing features Outreach has updated this quarter, visit our product press release.