How MCP Bridges the Code-to-Context Gap in the Enterprise

AI can’t move the needle when stuck on the outside, forced to work with generic sandbox data, unable to tap into the actual workflows, business logic, and tools that matter most.

Real transformation starts with context—allowing agents and models to “see” the systems, data, and relationships that run the business behind the scenes. This is where most teams hit their wall. Because it’s not just about having good data, or even well-documented APIs. The gap runs deeper: old integrations, silent business rules, and ad hoc workflows all combine to shut AI out of the richest opportunities for value.

The Model Context Protocol, or MCP, is starting to close this gap in ways that are changing business and engineering priorities. Instead of yet another interface to learn, MCP acts as a common language between AI models and the tangled web of enterprise systems, unlocking a new tier of productivity—if the hard work of specification, mapping, and context-sharing is done right.

The Code-to-Context Gap

Trying to make AI work within an enterprise is like teaching a foreigner to drive on a complex, unfamiliar road network — without a map or GPS. The core challenge isn’t just plugging in AI models, but providing them with the right context in a way they understand. Without this, AI agents fumble, stuck with generic data or outdated, partial info.

Yuval Perlov, Chief Technology Officer at K2view, sums it up well: “Enterprises operate in highly customized, multi-source environments with strict governance, compliance, and security requirements. The hardest part is delivering context that reflects real-time, fine-grained data access, with dynamic policies and privacy rules in place. Without this, AI models get lost in translation and produce less useful, sometimes risky outputs.”

This means organizations can’t simply expose APIs and assume AI agents will thrive. The complex reality of shifting permissions, silent business logic, and fragmented data demands more sophisticated context management — and that’s precisely where the Model Context Protocol steps in. MCP acts like a universal language, allowing AI models to plug into a dynamic, governed layer of context that mirrors the real world, rather than an oversimplified snapshot.

For teams tackling AI enablement, this insight advises a shift from “spray and pray” integrations to deliberate context collection and governance strategies — identifying which data needs constant freshness, which user roles need limited access, and how business logic should dynamically flow into AI workflows.

Turning Legacy Systems Into AI-Ready Tools

For most teams, the promise of AI breaks down at the specification stage. It isn’t rocket science to build an MCP server; what trips everyone up is surfacing the logic and rules buried deep inside old codebases, incomplete OpenAPI specs, or workflows managed in spreadsheets and Slack threads. That’s where powerful new frameworks and a renewed focus on “code-first” integration come in.

Greg Jennings, VP of Engineering, AI at Anaconda, points out, “Connecting AI models to workflows used to require bespoke adapters, but MCP is democratizing AI application development. MCP allowed us to build ‘Conda MCP,’ which enables models like Claude to invoke Conda commands to install, update, or remove packages on the fly. MCP dramatically lowers barriers to experimentation. Developers no longer need to wait for large companies to prioritize specific use cases. Anyone with basic programming skills can create an integration that allows AI models to connect to their favorite tools or data sources.”

His experience makes one thing clear: closing the gap between what’s in a legacy application and what an AI model can use starts with robust, living specs and frameworks that adapt as the business changes. For most enterprises, the fastest route to real MCP value is working directly from the codebase outward—generating machine-readable specs automatically whenever possible, creating a process that keeps context fresh, and empowering a wider community of developers to participate.

What Closing the Gap Really Delivers

Moving from generic AI to real business value takes more than just a standards-compliant server—it’s all about workflow and user context. Teams that put MCP to work often see rapid shifts not just in how AI is plugged in, but in what’s actually possible across departments.

Karen Ng, SVP of Product and Partnerships at HubSpot, explains how their engineering teams built a remote MCP server for deep research connectors. “Our deep research connector for ChatGPT helps businesses apply advanced research capabilities to their customer data and take action on the insights. For example, customers can analyze their seasonal ticket volumes and activate our Breeze Customer Agent to handle the spikes, find their top-converting personas for email marketing, or identify high–potential deals. And because we built the connector with MCP, it requires no specialized expertise or technical configuration, and reflects the same user-level permissions as inside HubSpot’s native UI.”

From this, it’s clear that success isn’t measured by how many APIs are MCP-enabled, but by how much friction is removed for business users—empowering marketers, analysts, and operations staff to solve their own problems without running through RevOps or IT bottlenecks.

Sean Kruzel, Lead AI Engineer at Infactory, describes how bringing data integration closer to users unlocks productivity: “By adding an MCP server to our data integrations, we enable analysts to query their data directly from ChatGPT, Claude, and others. Developers can add Infactory’s MCP servers to Cursor and Windsurf to quickly build front-end applications which access their published slices of their data and the data they subscribe to.” This shift means data can flow quickly to frontline users, increasing agility and delivering faster results to clients and decision-makers.

No matter the use case, the common theme is that the code-to-context gap shrinks when ordinary users and developers can work with governed, meaningful data and actions directly inside their favorite AI tools. The recommendation for teams is simple: deliver context not for its own sake, but for the real-world moments when users need it most—whether it’s troubleshooting a customer spike, spinning up a campaign, or surfacing insights from operational data in seconds.

From Workflow Improvement to New Business Models

Michael Pytel, Lead Technologist at VASS U.S. & Canada, sees MCP as far more than a tool for boosting developer productivity. “The Model Context Protocol is a massive accelerator for developers, allowing them to build sophisticated AI solutions faster by turning any SAP API into a discoverable 'tool' for an AI agent. This dynamic capability doesn't just shorten build cycles. It opens up a vibrant marketplace for new, monetizable AI services that can be chained together on demand. However, as we empower agents to use these tools on behalf of people, our focus on security must intensify, ensuring that every action is authenticated against the user's true identity and permissions to maintain trust and control. This is super important to every organization running enterprise systems (like SAP, Salesforce, ServiceNow, Microsoft Dynamics, etc.).”

For enterprise leaders, the lesson is clear: when MCP closes the gap, AI stops being a pure cost center or a background accelerator and becomes a revenue-enabling platform. Suddenly, specialized services that were once custom, monolithic, or glued together with fragile scripts can be packaged, discovered, and monetized—if robust controls are put in place. The move to an agentic, MCP-powered market isn’t just efficiency for efficiency’s sake; it’s about unlocking fresh growth, building new commercial angles, and maintaining governance as your product surface area expands.

Scaling Without Breaking Structure

Facundo Giuliani, Solutions Engineering Team Manager at Storyblok, puts the spotlight on the unique challenges of content-driven enterprises. “One of the key challenges we’re solving with MCP is bridging the gap between structured content and generative AI outputs. In a headless CMS, content is highly modular and context-dependent. MCP helps us preserve that structure and context when using AI for content generation or enrichment.”

Getting generative AI to work with modular, structured content—rather than flattening everything into simple text—means not just maintaining coherence, but protecting brand integrity and compliance. Giuliani adds, “Integrating Storyblok into other composable solutions enhances its power and provides users with all the features they require. With the rapid advent of AI tech, we quickly realized that MCP provides the perfect avenue to maintain and enhance Storyblok’s flexibility to a new generation of solutions.”

The real lesson for content, marketing, and product leaders: before adding new AI-powered workflows, ensure any MCP implementation is designed to respect and enrich your existing data models, not circumvent them. The best results come when AI works within the structure your organization already trusts—amplifying strengths, not creating hidden liabilities.

Governance, Safety, and Building for Trust

As MCP-powered integrations proliferate, enterprises are quickly learning that the value of connected, context-rich AI brings a new frontier of risk. Security, permissioning, and compliance become top-of-mind—not afterthoughts. The most forward-thinking organizations are baking controls into every layer of their MCP stack.

Siri Varma Vegiraju, Security Tech Lead at Microsoft Azure Security, illustrates the point: “The primary value [of MCP] lies in enabling controlled, context-aware AI capabilities. MCP allows us to combine LLMs with domain-specific tools, resulting in smarter, task-focused agents. This has significantly improved our threat modeling workflow and reduced manual effort in vulnerability analysis… By pairing our domain-specific models with MCP-exposed tools, we’ve seen a 40% improvement in vulnerability detection accuracy compared to legacy systems. Its flexibility has allowed us to rapidly iterate and expand functionality.”

Vegiraju’s team designs every MCP-exposed tool with strict identity validation, user-level permissions, and scenario-driven controls. Context isn’t just pushed to AI agents—it's filtered, reviewed, and augmented for the right level of access and auditability, with every action traceable back to real identities and roles.

Pytel of VASS U.S. & Canada echoes these concerns, noting, “As we empower agents to use these tools on behalf of people, our focus on security must intensify, ensuring that every action is authenticated against the user's true identity and permissions to maintain trust and control.”

The bottom line: AI agents using MCP should act only as smart extensions of real users—never stepping beyond established business rules, roles, or compliance boundaries. Enterprise implementers are advised to embed the same granular permissioning, auditability, and context scoping they use for humans, directly into each MCP endpoint and workflow.

Democratizing Data and Accelerating Innovation

The promise of MCP extends beyond mere technical interoperability—it fundamentally democratizes access to data and AI capabilities throughout organizations. Jennings of Anaconda, emphasizes how MCP is transforming who can build AI-powered integrations.

“Connecting AI models to workflows used to require bespoke adapters, but MCP is democratizing AI application development. MCP allows models like Claude to invoke commands on systems like Conda—install, update, or remove packages on the fly—lowering barriers and enabling developers across skill levels to contribute to AI integrations,” Jennings explains.

Similarly, Ng of HubSpot, highlights the impact on end users: “We’re democratizing access to deep research capabilities so marketers, sales teams, and customer service reps can perform complex analyses previously requiring specialized skills. Internally, thousands of teams can make their services AI-ready simply by exposing them as MCP tools, significantly accelerating AI delivery.”

This democratization leads to a virtuous cycle of innovation: as more teams build MCP-powered connectors and services, use cases multiply, and AI-powered workflows proliferate across departments. The enabling technology might be complex under the hood, but MCP brings a plug-and-play simplicity to developers and end users alike, unlocking creativity and productivity.

Measuring Impact & Quantifying the Value of MCP

For enterprises planning to leverage MCP, early and ongoing investment in measurement frameworks is essential. Tracking diverse but relevant KPIs not only builds the business case but guides iterative improvement—ensuring MCP deployments deliver on the promise of accelerating AI-driven innovation while managing risks.

The breadth of industry adoption—from CRM and marketing technology, to cybersecurity and enterprise resource planning—demonstrates its broad applicability and transformative potential. 

• Accelerated Development: MCP massively expedites developer workflows by simplifying the exposure of internal APIs and services to AI models, enabling rapid prototyping and deployment of AI-driven capabilities.
• Governance and Security: Enterprises recognize that deploying AI responsibly requires embedding stringent access controls, identity validation, and contextual safeguards directly within MCP deployments—ensuring that AI agents act only within permitted boundaries.
• Democratization of AI and Data: MCP empowers non-technical business users and diverse teams across the organization to access, query, and act on data through AI agents, fostering broader adoption and innovation.
• Emerging Ecosystems and Marketplaces: By turning APIs into discoverable ‘tools,’ MCP opens new revenue models and service opportunities, enabling dynamic chaining and composability of AI functionalities across vendors and partners.
• Measurable Impact: Organizations actively measure efficiency gains, accuracy improvements, and adoption metrics to ensure MCP investments deliver tangible business outcomes.

Enterprises exploring AI should view MCP not simply as a technical protocol but as a strategic enabler for the next generation of AI-powered workflows, services, and governance frameworks.