What Is Model Context Protocol (MCP)? A Quick Start Guide.

You might not consider scalability a critical limitation of enterprise AI—but development velocity is undoubtedly at the top of executive agendas this year.

Nearly every SaaS tool today comes with some sort of AI feature to expedite something or other—and that’s mostly a good thing. AI is unlocking new capabilities for teams of all sizes, and the ability to turn cycles is unlike anything I’ve seen before. One data leader we spoke with in the pharma space is even targeting an 80% adoption rate for AI-coding tools across all data + AI teams by year’s end.

Unfortunately, all that AI enablement is also bringing with it a whirlwind of scalability issues. At the heart of the problem? A proliferation of new data sources and features, each in need of specialized workflows and comprehensive reliability coverage. 

Enter the Model Context Protocol (MCP). MCP is an open standard that connects AI systems to data sources via a universal protocol that can reduce repetition and elevate AI tooling. 

In this article, we’ll unpack what MCP is, why it’s becoming the standard for AI implementations, and how MCP and observability will work together to deliver reliable data + AI applications.

Let’s dive in. 

What is Model Context Protocol (MCP)—and why should you care?

Think of Model Context Protocol like a USB-C port for AI.

We all use a menagerie of technology in our daily lives—smartphones, laptops, watches…you get the idea. And every one of those tools requires a connection of some kind to be effective.

Now imagine for a second that each one of those items required its own specialized connection. One needs a USB-A, one is using an HDMI, and a few are using those crazy pronged cables that you have to screw in.

How would you manage that? How would you pack?!

In the same way we use a menagerie of technology to power our daily lives, AI applications rely on a similar menagerie of tools and sources to solve for a given use case. For the last several years, teams have relied on specialized connections to support each AI feature individually—and while this workflow was technically functional, it wasn’t practically feasible.

That’s where Model Context Protocol comes in. Instead of using a proprietary cable to support each different connection, MCP provides a standardized connector that works with everything at once—replacing a tangled mess of proprietary cables with a single extensible solution. 

Released by Anthropic in November 2024, MCP has basically taken over as the solution to AI fragmentation. The protocol is maintained as an open-source project on GitHub, with hundreds of servers covering everything from database connections to productivity tools, including for enterprise systems like Slack, Git, Postgres, and Puppeteer. 

And it’s not Anthropic-specific either: its widespread popularity has already prompted rivals like OpenAI and Google DeepMind to commit to adding support for MCP to their models and SDKs. 

How does Model Context Protocol (MCP) work? The technical overview:

At its core, Model Context Protocol (MCP) follows a client-server architecture:

• MCP clients: Components integrated within AI applications (like Claude or other LLMs) that facilitate interactions with external data sources
• MCP servers: Lightweight programs that expose specific capabilities from data sources and services
• MCP hosts: Applications like Claude Desktop, IDEs, or AI tools that want to access data through MCP
• Data sources: Various backends including local storage, databases, or remote services

To get a little deeper in the weeds, MCP builds on JSON-RPC and supports multiple transport methods, including stdio and HTTP via SSE, making it flexible for both local and remote integrations. 

What sets MCP apart from previous AI integration approaches is its focus on standardization and interoperability. Unlike proprietary connectors or custom integrations, MCP creates an open ecosystem where any AI system can connect to any data source using the same protocol. This transforms the dreaded “M×N problem” (connecting M different AI models to N different data sources) into a more manageable “M+N” scenario. Instead of building custom integrations for every combination, you build once to the standard.

MCP integrates three primary mechanisms for AI-data interaction:

1. Tools (model-controlled): Function-like capabilities that LLMs can call to perform specific actions
2. Resources (application-controlled): Data sources LLMs can access without performing computation or causing side effects
3. Prompts (user-controlled): Pre-defined templates for using tools or resources in the most optimal way

For example, a bank’s customer chatbot (an MCP host) connects to their transaction database, CRM, and knowledge base through Model Context Protocol. When a customer asks why their card was declined, the chatbot simultaneously queries all three systems through standardized connections. It provides a complete answer incorporating the transaction details, account status, and relevant policies — all without requiring custom integrations for each data source, which would each require their own development and maintenance overhead.

MCP delivers four core capabilities that manage how AI models interact with external content: 

1. Context processing framework: A structured approach for how AI models ingest, organize, and utilize external information, ensuring standardized handling across various data sources.
2. Information prioritization mechanisms: Components that determine which information is most relevant to a particular query or task, helping manage large volumes of context efficiently.
3. Contextual awareness maintenance: Systems for maintaining relevant context throughout interactions and across multiple queries, preserving important information.
4. Context window management: Techniques for efficiently utilizing limited token capacity, optimizing how context is loaded into and maintained within AI models.

How Model Context Protocol (MCP) improves AI performance

Model Context Protocol (MCP) doesn’t just improve development velocity though—it also improves performance for AI models. MCP adopters can expect:

• Greater contextual understanding across tools. By providing AI models with access to relevant data sources in real-time, MCP allows them to develop a deeper understanding of the context surrounding queries. Instead of relying solely on their training data, models can draw on current, organization-specific information to provide more accurate responses.
• Less redundant information processing. With MCP, AI systems don’t need to reprocess information they’ve already encountered. The protocol manages context efficiently by maintaining state between interactions, storing relevant information in a structured way, and exposing standardized endpoints for data retrieval. 
• More relevant responses. Access to up-to-date information means responses are not just technically accurate, but also relevant to the current state of your business. 
• Addressing the “context switching tax”. One of the most significant advantages of MCP is how it addresses the “context switching tax” — the degradation in AI performance that occurs when models need to switch between different information sources or tasks. Providing a standardized interface for all context reduces this friction, leading to more coherent, consistent responses.

This should all translate to business value in the form of improved performance and reduced costs.

How MCP builds business trust

Even though every organization is racing to adopt AI, trust often lags behind. When implemented correctly, MCP serves to address AI reliability concerns by providing: 

• Consistency in AI responses. When all your SaaS tools interface with MCP, AI responses become more consistent across your tech stack. Users receive similar quality experiences whether interacting with AI in your CRM, analytics platform, or development environment.
• Accountability through standardized context tracking. MCP provides a structured way to track what information was available to the AI when it made a recommendation or took an action. This creates clearer accountability and makes it easier to understand why the AI responded as it did.
• More transparent AI decision trails. With MCP, the path from information to AI response becomes more transparent. Teams can trace exactly what context was provided to the model, improving explainability and helping build trust with stakeholders.
• Responsible AI implementation. By providing controls over what information AI systems can access and how they use it, MCP supports more responsible AI implementation across your organization. By standardizing how AI accesses enterprise data, MCP reduces the risk of inconsistent outputs or inappropriate data usage across different systems.

All of this adds up to some pretty significant governance benefits. Traditionally, each AI connection would have its own authentication, authorization, and logging mechanisms. MCP abstracts away all that complexity by providing a consistent layer where governance policies can be enforced consistently across connections. 

This means centralized visibility into what data each AI system can access, consistent audit trails of how AI systems use enterprise data, and uniform security controls across all AI implementations. 

The advantages of MCP—why early adoption pays off

The industry is rapidly converging on MCP as the standard for AI-data connectivity. Forward-thinking organizations are already implementing MCP, and their experiences highlight several key advantages:

• Meaningful differentiation. The future of enterprise AI isn’t the next big model release — it’s how effectively you integrate AI with your first-party data to deliver business value. This is where MCP supports competitive advantage. Early adopters are using MCP to build secure, standardized connections to proprietary data, enabling context-aware AI that leverages a company’s most valuable asset to deliver real business value. 
• Operational efficiency. A data + AI team’s productivity will always be determined by their most repetitive workflows. By eliminating the time-consuming specialization, teams can spend less time managing connections and more time creating value. 
• Future-proofing that’s future-proof. MCP isn’t a static standard – it’s a living protocol that continues to evolve while maintaining compatibility with existing implementations. Organizations implementing MCP today are building a foundation that can adapt to future AI advancements. 

The same problem with leveraging point solutions for things like data quality monitoring is present in the connectors dilemma—and in the same way choosing a best-in-class partner offers reliability protection for future data + AI extensions, MCP provides bankable extensibility for scaling future AI use-cases.

Calculating the ROI of Model Context Protocol (MCP)

Looking to calculate the tangible value MCP can deliver? Expect improvements in the following areas: 

• Development efficiency: Reduction in hours spent creating custom integrations between AI models and data sources.
• Maintenance overhead: Decreased engineering time dedicated to maintaining AI-data connections, shifting from M×N to M+N integration points.
• Incident reduction: Fewer failures and outages caused by integration problems between AI systems and data sources.
• Deployment acceleration: Faster time-to-value for new AI capabilities through standardized connections.
• Enhanced productivity: Improved AI performance leading to better business outcomes and user experiences.

With these concrete ROI drivers in mind, the next question becomes: how do you actually implement MCP across your organization?

Implementation roadmap: getting started with Model Context Protocol (MCP)

Transforming your AI infrastructure doesn’t require a complete overhaul. You can adopt MCP incrementally, starting with high-value use cases and expanding over time. Here’s a pragmatic approach that won’t require boiling the ocean:

1. Audit your current tools for MCP compatibility

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Start by cataloging your existing AI-enabled tools and assessing their MCP readiness. Look for:

• AI assistants like Claude Desktop that already support MCP
• Development environments such as Cursor, Zed, and Replit that have added MCP integration
• Data platforms and observability tools beginning to implement MCP connections

There are hundreds of MCP servers available on GitHub, from enterprise integrations to specialized connectors. The GitHub Model Context Protocol organization is the best starting point to explore both official implementations and community contributions.

2. Engage vendors on their MCP roadmap

For tools without current MCP support, ask vendors direct questions:

• “What is your timeline for implementing MCP integration?”
• “Which MCP primitives will you support?”
• “Will you provide pre-built servers or implementation guidance?”

Consider adding MCP support as a requirement in your RFPs and contract renewals. This signals to vendors that the capability is becoming a competitive necessity, and ensures your stack remains cohesive as you adopt the standard.

Any vendor who is planning for the future is building this feature. Look for these partners. 

3. Implement a phased approach

Phase 1: Targeted pilot (1-2 months)

• Choose one high-value use case where context makes an immediate difference
• Select tools with existing MCP support to minimize friction
• Establish baseline metrics to quantify the impact

Phase 2: Core systems (3-6 months)

• Expand to critical data sources and operational tools
• Develop internal implementation patterns and security models
• Begin seeing network effects as AI systems access more context

Phase 3: Enterprise standard (6+ months)

• Formalize MCP as your architectural standard
• Include MCP compatibility in all technology evaluations
• Create a governance framework for ongoing implementations

The key to success is starting small, measuring results, and expanding methodically. As you implement MCP across more systems, the benefits of standardization compound – each new integration makes your AI ecosystem more valuable.

Why Model Context Protocol (MCP) should be integrated with data + AI observability

While Model Context Protocol delivers broad benefits across your AI stack, nowhere is the need for standardization and extensibility more visible than in data + AI observability solutions. 

Having a universal, open standard for connecting AI systems with data sources leads to scalable monitoring and troubleshooting for reliable AI applications. As the category creator and leader, Monte Carlo has invested heavily in the production of thoughtfully automated and radically scalable data + AI monitoring, management, and troubleshooting solutions.

Today, we’re building on that enterprise framework with the introduction of MCP observability and integrations (general availability coming soon).

However, while MCP can certainly extend the reach and accessibility of data + AI observability, every MCP connection needs the scalable reliability that data + AI observability provides in order for those AI applications to be trusted—and ultimately adopted—by the broader enterprise.

Model Context Protocol (MCP) implemented in a silo won’t expedite AI success—it will only increase the velocity of bad responses. 

No pipeline can be effective—MCP or otherwise—without end-to-end observability to continuously validate the voracity of each data + AI system.

Let’s take a look at how these two strategies work together in a bit more detail. 

AI-assisted incident response transformation

Tracing incidents across AI systems is inherently challenging. When a model produces an incorrect response, was it the data source, the retrieval mechanism, or the model itself that failed? 

Model Context Protocol can enable AI incident responders by giving them standardized access to context across all components. Instead of treating symptoms like hallucinations in isolation, MCP-enabled observability tools can help teams trace these issues back to their root causes, whether in data quality, system performance, or prompt engineering. This can dramatically reduce mean time to resolution (MTTR) while empowering teams to solve problems at their source, rather than just managing symptoms.

Solutions like Monte Carlo’s troubleshooting agent—an AI feature designed to accelerate incident resolution across a preponderance of data + AI applications—is a great example of how MCP and data + AI observability can work in concert to improve data + AI team efficiency and performance.

Unified visibility experience

Breaking down silos between data and AI monitoring remains one of the biggest obstacles to reliable AI implementation. Without end-to-end visibility across both AI outputs and the pipelines feeding them, there’s simply no way to measure reliability — let alone manage it.

Data + AI observability can leverage insights like the standardized context chain created by Model Context Protocol to provide contextually-aware correlations between errant responses, root-causes, and impact—and surfaces all that consolidated insight into a single-pane of glass for data + AI leaders, governance teams, and business users to review and action. 

Future-proof observability architecture

Building for tomorrow’s AI capabilities requires an observability architecture that can adapt as models, tools, and techniques evolve. MCP combined with data + AI observability provides a framework for extensibility, preparing reliability workflows for increasingly autonomous systems that will need massively automated (self-healing) resolution capabilities to scale. 

MCP & The Scalability Imperative

The AI revolution promised to transform how we work—but disconnected implementations has created technical debt, fragmented experiences, and rapidly scaling reliability issues across data + AI systems.

Data + AI leaders are acutely aware of the financial and reputational impact of an unreliable agent in product. And scaling AI access without fundamentally scaling visibility or incident management is a deprecation sentence for your next AI initiative. 

That’s why Model Context Protocol and data + AI observability go hand-in-hand. A data + AI observability solution that interfaces with MCP is your golden ticket to AI adoption.

There’s no downside for being early to the party—but there’s plenty for being late. Prioritizing Model Context Protocol as a foundation of your AI architecture, and seeking out solutions that can not only integrate with but optimize the advantages of MCP is the key to a scalable and reliable AI future.

And that’s exactly the future that Monte Carlo is committed to supporting.

Watch this space.

Our promise: we will show you the product.

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