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What is an MCP Server? Understanding the Concept, Benefits, and Framework for AI Workflows

Discover the role of MCP Servers in optimizing AI workflows. Learn how these servers enable seamless communication between LLMs and agent-based systems, enhancing data management, scalability, and model performance for data engineers and AI experts.

By

Jatin

Updated on

August 25, 2025

mcp-server-concept

As someone who’s deep into the world of data engineering and AI, I’ve seen firsthand how the landscape has evolved. The complexity of workflows, the scale at which models operate, and the constant drive to optimize performance have made me truly appreciate the need for more structured communication between AI systems. That’s where the MCP Server comes into play.

Let me walk you through what MCP Servers are, why they matter, and how they’ve started to shape the future of AI models and agent workflows, especially in platforms like Decube.

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The Beginning: Why MCP Servers Are a Game Changer

When we started developing Decube, our goal was simple: to make data and AI work together in a way that didn’t feel like a struggle. We needed to build something that could seamlessly integrate multiple AI models, streamline data communication, and power agent-based workflows with minimal hassle.

Enter the Model Communication Protocol (MCP) Server. You see, an MCP Server is more than just a piece of technology—it's a bridge. It connects various systems, ensures smooth communication, and acts as a translator, ensuring all the models and agents in a workflow are speaking the same language.

So, what exactly does the MCP Server do? Let me break it down:

  • Interfacing with multiple LLMs: AI is evolving rapidly, and many systems today require multiple models working in tandem. But without a proper communication framework, these models would struggle to collaborate effectively. This is where the MCP Server steps in, helping models talk to each other and pass information seamlessly.
  • Orchestrating Agent-Based Workflows: More and more companies are looking to automate workflows using intelligent agents. The challenge is, how do you keep these agents aligned and on track? The MCP Server manages this by ensuring each agent has the right inputs, is triggered at the right time, and can operate autonomously while still integrating with other models in the system.
  • Routing and Transforming Data: In our early days, we faced challenges with ensuring data flows smoothly between systems. In complex AI setups, data can often become misaligned or outdated if not managed properly. The MCP Server takes care of transforming and routing data so that it’s always in the right place at the right time.

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The Real Power of MCP Servers in Action

At Decube, we’re all about simplifying complexity, and MCP Servers play a huge role in this. Let me share some real-world scenarios where MCP Servers truly shine:

  1. Bringing Models Together
    Imagine you’re working on a data analytics system that uses large language models (LLMs) for understanding text data. But these models need to talk to a decision-making engine to take action based on the insights they generate. Without a system to ensure that data flows properly between these models, you could end up with incomplete or delayed actions. The MCP Server is like a traffic controller, making sure that the right data is passed along to the right model at the right time.
  2. Creating Autonomous Agent Workflows
    At Decube, we wanted to automate as much of the data governance process as possible. This is where agent-based workflows come in. These intelligent agents handle everything from data categorization to quality checks without constant human oversight. But for this automation to work, each agent needs to stay in sync with the others. The MCP Server makes this happen by managing the flow of instructions and outputs between agents, ensuring a smooth process.
  3. Transforming and Routing Data
    As AI systems scale, the volume and variety of data increase. That means there’s a huge need for robust data management. Without a reliable framework in place, the risk of errors goes up. The MCP Server ensures that data is formatted, cleaned, and passed to the right agents or models. This keeps the workflow running smoothly and prevents miscommunications between different parts of the system.

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What Makes MCP Servers So Valuable for Data Engineers and AI Experts?

As I reflect on our journey at Decube, the benefits of the MCP Server become even clearer, especially for those of us in the data engineering and AI space. Here are a few reasons why I believe these servers are indispensable:

1. Streamlining Complex Integrations

One of the hardest things about working with multiple systems is ensuring they can all communicate effectively. For data engineers, this means spending hours configuring and debugging integrations. But with MCP Servers, much of that complexity is taken care of. They allow us to connect various models, agents, and data systems in a way that feels almost effortless. For us at Decube, this has meant more time focusing on what matters—building solutions for our clients and improving the overall AI architecture.

2. Building Scalable AI Systems

Scalability is something every data engineer thinks about. The systems we design today need to be able to grow with the company and adapt to new technology. The MCP Server is built to scale with ease. Whether you’re adding new models or extending your agent network, the MCP Server ensures that everything can expand without the headache of redesigning the entire system. This flexibility is something we’ve seen firsthand as Decube’s platform grows and diversifies.

3. Cutting Down on Latency

Speed is everything when it comes to real-time decision-making. Whether you’re in financial services, healthcare, or customer support, low-latency communication between models and agents is non-negotiable. By managing and optimizing data flows, MCP Servers cut down on delays, ensuring that decisions can be made in real-time without unnecessary bottlenecks.

4. Helping Models Perform at Their Best

For AI models to produce accurate results, they need accurate, timely data. But often, models underperform simply because they’re not receiving the right input at the right time. MCP Servers ensure that the data is properly formatted, transformed, and delivered, helping our models perform at their best. For Decube, this means the insights and actions generated by our AI models are more reliable, which translates to better outcomes for our clients.

5. Built-In Fault Tolerance

When we’re talking about AI systems that need to operate 24/7, fault tolerance is a must. The last thing you want is for your system to go down in the middle of an important process. MCP Servers are designed with this in mind, providing redundancy and quick recovery in case something goes wrong. This makes them an excellent choice for mission-critical applications, ensuring that the system keeps running even if there’s an issue somewhere along the chain.

MCP Server Framework

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The Anatomy of an MCP Server: Decoding the Framework

At Decube, the MCP Server is built around key components that allow it to work seamlessly with AI workflows:

  • Model Connectors: Think of these as the entry points for LLMs and other models into the MCP Server. They allow models to interact with each other in a way that’s smooth and scalable.
  • Communication Protocols: The protocols ensure that data is passed between models and agents without errors, using both synchronous and asynchronous methods depending on the specific needs.
  • Workflow Orchestration Engine: This is the brain behind the scenes, managing when and how each task is carried out in the workflow. It ensures that models and agents are working in sync and that tasks are executed efficiently.
  • Data Management Layer: This is the backbone for all the data that flows through the system. It ensures that data is cleaned, transformed, and routed properly to ensure smooth operations.
  • Monitoring and Analytics: With this component, you can track everything happening in your MCP Server in real-time. From monitoring task completion to identifying bottlenecks, it ensures everything is running as it should.

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Why MCP Servers Are Crucial for the Future of AI

As AI systems continue to evolve, the need for efficient, scalable, and reliable communication systems will only grow. The MCP Server is essential for ensuring that multiple models and agents can work together in a cohesive, intelligent system. Whether it’s powering data governance, driving business decisions, or automating workflows, MCP Servers are the unsung heroes of modern AI architecture.

At Decube, we’ve seen the impact that an MCP Server can have on AI-driven workflows, helping us connect the dots between models, agents, and data sources to create a unified, efficient system. The future of AI is bright, and MCP Servers are at the heart of it all, ensuring that communication between complex systems remains fluid, fast, and future-ready.

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Frequently Asked Questions (FAQ's)

What is a Data Trust Platform in financial services?
A Data Trust Platform is a unified framework that combines data observability, governance, lineage, and cataloging to ensure financial institutions have accurate, secure, and compliant data. In banking, it enables faster regulatory reporting, safer AI adoption, and new revenue opportunities from data products and APIs.
Why do AI initiatives fail in Latin American banks and fintechs?
Most AI initiatives in LATAM fail due to poor data quality, fragmented architectures, and lack of governance. When AI models are fed stale or incomplete data, predictions become inaccurate and untrustworthy. Establishing a Data Trust Strategy ensures models receive fresh, auditable, and high-quality data, significantly reducing failure rates.
What are the biggest data challenges for financial institutions in LATAM?
Key challenges include: Data silos and fragmentation across legacy and cloud systems. Stale and inconsistent data, leading to poor decision-making. Complex compliance requirements from regulators like CNBV, BCB, and SFC. Security and privacy risks in rapidly digitizing markets. AI adoption bottlenecks due to ungoverned data pipelines.
How can banks and fintechs monetize trusted data?
Once data is governed and AI-ready, institutions can: Reduce OPEX with predictive intelligence. Offer hyper-personalized products like ESG loans or SME financing. Launch data-as-a-product (DaaP) initiatives with anonymized, compliant data. Build API-driven ecosystems with partners and B2B customers.
What is data dictionary example?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is an MCP Server?
An MCP Server stands for Model Context Protocol Server—a lightweight service that securely exposes tools, data, or functionality to AI systems (MCP clients) via a standardized protocol. It enables LLMs and agents to access external resources (like files, tools, or APIs) without custom integration for each one. Think of it as the “USB-C port for AI integrations.”
How does MCP architecture work?
The MCP architecture operates under a client-server model: MCP Host: The AI application (e.g., Claude Desktop or VS Code). MCP Client: Connects the host to the MCP Server. MCP Server: Exposes context or tools (e.g., file browsing, database access). These components communicate over JSON‑RPC (via stdio or HTTP), facilitating discovery, execution, and contextual handoffs.
Why does the MCP Server matter in AI workflows?
MCP simplifies access to data and tools, enabling modular, interoperable, and scalable AI systems. It eliminates repetitive, brittle integrations and accelerates tool interoperability.
How is MCP different from Retrieval-Augmented Generation (RAG)?
Unlike RAG—which retrieves documents for LLM consumption—MCP enables live, interactive tool execution and context exchange between agents and external systems. It’s more dynamic, bidirectional, and context-aware.
What is a data dictionary?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is the purpose of a data dictionary?
The primary purpose of a data dictionary is to help data teams understand and use data assets effectively. It provides a centralized repository of information about the data, including its meaning, origins, usage, and format, which helps in planning, controlling, and evaluating the collection, storage, and use of data.
What are some best practices for data dictionary management?
Best practices for data dictionary management include assigning ownership of the document, involving key stakeholders in defining and documenting terms and definitions, encouraging collaboration and communication among team members, and regularly reviewing and updating the data dictionary to reflect any changes in data elements or relationships.
How does a business glossary differ from a data dictionary?
A business glossary covers business terminology and concepts for an entire organization, ensuring consistency in business terms and definitions. It is a prerequisite for data governance and should be established before building a data dictionary. While a data dictionary focuses on technical metadata and data objects, a business glossary provides a common vocabulary for discussing data.
What is the difference between a data catalog and a data dictionary?
While a data catalog focuses on indexing, inventorying, and classifying data assets across multiple sources, a data dictionary provides specific details about data elements within those assets. Data catalogs often integrate data dictionaries to provide rich context and offer features like data lineage, data observability, and collaboration.
What challenges do organizations face in implementing data governance?
Common challenges include resistance from business teams, lack of clear ownership, siloed systems, and tool fragmentation. Many organizations also struggle to balance strict governance with data democratization. The right approach involves embedding governance into workflows and using platforms that unify governance, observability, and catalog capabilities.
How does data governance impact AI and machine learning projects?
AI and ML rely on high-quality, unbiased, and compliant data. Poorly governed data leads to unreliable predictions and regulatory risks. A governance framework ensures that data feeding AI models is trustworthy, well-documented, and traceable. This increases confidence in AI outputs and makes enterprises audit-ready when regulations apply.
What is data governance and why is it important?
Data governance is the framework of policies, ownership, and controls that ensure data is accurate, secure, and compliant. It assigns accountability to data owners, enforces standards, and ensures consistency across the organization. Strong governance not only reduces compliance risks but also builds trust in data for AI and analytics initiatives.
What is the difference between a data catalog and metadata management?
A data catalog is a user-facing tool that provides a searchable inventory of data assets, enriched with business context such as ownership, lineage, and quality. It’s designed to help users easily discover, understand, and trust data across the organization. Metadata management, on the other hand, is the broader discipline of collecting, storing, and maintaining metadata (technical, business, and operational). It involves defining standards, policies, and processes for metadata to ensure consistency and governance. In short, metadata management is the foundation—it structures and governs metadata—while a data catalog is the application layer that makes this metadata accessible and actionable for business and technical users.
What features should you look for in a modern data catalog?
A strong catalog includes metadata harvesting, search and discovery, lineage visualization, business glossary integration, access controls, and collaboration features like data ratings or comments. More advanced catalogs integrate with observability platforms, enabling teams to not only find data but also understand its quality and reliability.
Why do businesses need a data catalog?
Without a catalog, employees often struggle to find the right datasets or waste time duplicating efforts. A data catalog solves this by centralizing metadata, providing business context, and improving collaboration. It enhances productivity, accelerates analytics projects, reduces compliance risks, and enables data democratization across teams.
What is a data catalog and how does it work?
A data catalog is a centralized inventory that organizes metadata about data assets, making them searchable and easy to understand. It typically extracts metadata automatically from various sources like databases, warehouses, and BI tools. Users can then discover datasets, understand their lineage, and see how they’re used across the organization.
What are the key features of a data observability platform?
Modern platforms include anomaly detection, schema and freshness monitoring, end-to-end lineage visualization, and alerting systems. Some also integrate with business glossaries, support SLA monitoring, and automate root cause analysis. Together, these features provide a holistic view of both technical data pipelines and business data quality.
How is data observability different from data monitoring?
Monitoring typically tracks system metrics (like CPU usage or uptime), whereas observability provides deep visibility into how data behaves across systems. Observability answers not only “is something wrong?” but also “why did it go wrong?” and “how does it impact downstream consumers?” This makes it a foundational practice for building AI-ready, trustworthy data systems.
What are the key pillars of Data Observability?
The five common pillars include: Freshness, Volume, Schema, Lineage, and Quality. Together, they provide a 360° view of how data flows and where issues might occur.
What is Data Observability and why is it important?
Data observability is the practice of continuously monitoring, tracking, and understanding the health of your data systems. It goes beyond simple monitoring by giving visibility into data freshness, schema changes, anomalies, and lineage. This helps organizations quickly detect and resolve issues before they impact analytics or AI models. For enterprises, data observability builds trust in data pipelines, ensuring decisions are made with reliable and accurate information.
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