MCP Servers: How to prepare your data

Alexandr Wang, founder of Scale AI, notes that even the most advanced AI systems perform only as well as the quality of data they process. This makes data preparation a crucial step for the success of agentic AI. Without clean, accessible, and contextual information, even the most sophisticated AI agents deliver unreliable outcomes.

Model Context Protocol (MCP) servers address this problem by providing a secure, standardized interface that connects AI agents to trusted data models. Moreover, they offer a consistent and reliable access layer providing clean, accessible, and contextual information essential for agentic AI.

In this article, we cover the key data preparation principles for MCP servers and show how dbt can ensure data is ready for this new autonomous AI era.

Why agentic AI demands new data standards

Agentic AI is a set of intelligent systems powered by autonomous agents that perceive, reason, and reach goals with minimal human help. These agents make decisions, run workflows, and adapt in real time based on changing environments and inputs.

Unlike traditional AI, these systems reshape enterprise operations through autonomous decision-making, reasoning, and goal-driven action. That goes beyond traditional AI applications, which primarily analyze but leave actions up to users.

AI agents actively run multi-step workflows, call APIs, and dynamically adapt to evolving inputs with minimal human intervention. For example, in manufacturing, an AI agent can identify a fault, order necessary parts, and adjust production, all while handling the entire response with minimal human intervention.

This change requires redesigning data strategies beyond conventional pipelines to accommodate agentic systems.

Beyond traditional pipelines

The core distinction between agentic AI and earlier AI systems, such as rule-based bots, lies in their real-time, contextual access requirements. Unlike static models or rule-based systems, agentic AI operates in dynamic environments where it must make decisions within milliseconds.

Legacy data standards fall short because agentic AI operates independently, adapts in real time, and learns from experience. This adaptability and continuous learning require immediate data ingestion and processing, a capability that is largely absent in legacy data infrastructures.

For example, an AI agent managing fleet logistics needs instant traffic and weather data to reroute deliveries. Traditional pipelines can’t deliver such data in real time.

The absence of real-time, contextual information flow creates blind spots for agentic AI. This hinders agents’ ability to react effectively to changing conditions and make insightful decisions. To overcome this, agents require a secure and consistent access layer to trusted data models. Here, the limitations of legacy systems become particularly evident in complex operational settings.

For example, industrial environments often struggle with integrating various systems such as Supervisory Control and Data Acquisition (SCADA) platforms, each with unique data formats, access protocols, and latency characteristics.

This fragmentation creates fragile pipelines that can’t support the seamless, real-time access that agentic AI demands. Organizations without real-time and scalable data architectures risk being outpaced by their competitors.

Five essential data pillars

Agentic AI depends on five essential data pillars:

Governance

Audit trails ensure compliance and build trust.

Robust governance guarantees agents receive monitored, auditable, and secure access while meeting evolving regulatory demands. Without this framework, autonomous decisions lack accountability and risk compliance failures.

Structure

Raw data is fundamentally inadequate.

Agents require modeled, tested, and contextualized datasets transformed into analytics-ready assets. This structured foundation enables precise reasoning and prevents costly errors from unvalidated inputs.

Semantics

Ambiguity sabotages AI decision-making.

Business-critical terms, such as revenue or churn, must be precisely defined and consistently applied across all systems. Semantic unity ensures agents interpret metrics identically, eliminating conflicting outputs.

Governed access

Security and accessibility must coexist.

Strict permission controls prevent data leaks while also enabling the agents to securely retrieve contextual information via APIs.

Observability

Real-time monitoring is non-negotiable.

Teams require immediate visibility into agent actions, their reasoning, and the resulting outcomes. Automated alerts and self-healing mechanisms enable rapid intervention when anomalies arise.

Understanding MCP servers in the agentic AI ecosystem

Organizations require infrastructure that provides secure, structured, and real-time access to various systems. MCP servers are designed to meet these demands for agentic AI. They act as standardized interfaces exposing models, metadata, lineage, and system capabilities to AI agents through a unified protocol.

MCP servers bridge AI agents and enterprise data systems.

MCP servers abstract the complexity of backend integrations, providing agents with seamless access to both structured and unstructured data. This access enables consistent reasoning and real-time action across disparate systems.

MCP servers offer a framework to build and support these capabilities:

• Standardized access to models, APIs, and data sources.
• Real-time operations with low-latency run times.
• Real-time access to contextual data for dynamic, millisecond-level decision-making.
• Secure interfaces with built-in authentication and encryption.
• Interoperability across tools, services, and platforms.

Once authenticated, agents use lightweight, protocol-driven requests to:

• Initiate multi-step workflows.
• Call external APIs.
• Query logs and system telemetry.
• Retrieve semantic models and business rules.

MCP servers apply business rules to agent requests and return structured responses like JSON or XML that enable agents to decide and act in real time.

Benefits of the MCP architecture

MCP servers help agents manage tasks across systems. Their architecture offers clear benefits for real-world use:

1. Reduces development overhead: Reusable, plug-and-play interfaces remove the need for hardcoded integrations.
2. Democratizes access: Business users can use agentic systems via intuitive and natural language interfaces.
3. Supports scalability: MCP servers are containerized and load-balanced, with support for modular expansion.
4. Enhances security and auditability: Every transaction is logged and governed, aligning with enterprise compliance needs.

Preparing your data for MCP integration

For MCP servers to effectively empower agentic AI, organizations must proactively refine their data infrastructure to meet the unique demands of autonomous systems.

Here's how to build a solid foundation:

Step 1: Map data dependencies

Agents must understand how changes in one system affect others. Begin by documenting the complete lineage of your data, from its origin in source systems through transformations to the final business reports.

For example, if a supplier changes their ID in your procurement system, agents must recognize how this impacts production forecasts and compliance reports. Without this visibility, agents can’t anticipate ripple effects; however, with full lineage mapping, they transform from simple task runners to proactive problem solvers.

Step 2: Embed quality checks

Autonomous systems increase the risk of bad data, so teams must build data quality validation checks directly into their pipelines.

• Set freshness alerts to flag delayed data streams before they impact real-time pricing agents.
• Implement uniqueness checks to prevent duplicate customer records during automated onboarding.
• Create null constraints to block incomplete transactions in payment processing.
• Enforce referential integrity to ensure warehouse inventory counts match shipping manifests.

These validations act as safety nets, catching errors before agents act on flawed information.

Step 3: Standardize metadata

Agents can’t work together if the data means different things in different places. Establish clear, consistent language across your data ecosystem:

• Clearly define business terms. For example, specify an “active customer” as a user who has logged in within the past 30 days.
• Tag sensitive information, such as patient health records, with clear ownership labels.
• Make data lineage accessible through protocol-driven requests, enabling agents to verify context before making decisions.
• Store policies directly in metadata, allowing agents to automatically comply with regional regulations.

This eliminates ambiguity, ensuring that a sales agent in Europe and a logistics agent in Asia interpret "order fulfillment rate" identically.

Step 4: Design secure interfaces

Agent systems create new vulnerabilities if not properly contained. Implement strict access protocols:

• Restrict agents to the minimal necessary permissions, like allowing customer service agents to access only the support ticket history.
• Require authentication through single sign-on for all data requests.
• Mask sensitive details in responses, like partially redacting credit card numbers.
• Log every agent action to maintain audit trails and meet compliance standards.

These controls let agents operate freely while keeping sensitive data protected.

Step 5: Optimize for low-latency

Delayed data breaks real-time agents, as even a few seconds of stock update lag can cost millions. In fast-moving environments, latency isn’t a metric; it’s a liability.

Design your systems for speed:

• Use buffering techniques to temporarily hold incoming sensor data and prevent overload during sudden surges.
• Build failover systems to instantly switch to backup servers during cloud outages.
• Implement priority routing so that critical operations, such as emergency shutdowns, bypass queues.
• Continuously monitor performance at peak traffic moments.

If your pipelines can't deliver data within 500 milliseconds, agentic workflows will fail when they're needed most.

How dbt prepares your data for MCP servers

dbt transforms data infrastructure into an agent-ready foundation by embedding governance, testing, and semantic consistency directly into transformation workflows. In addition, dbt supports its own MCP server to serve data to any MCP-ready LLM for powering real-world, operational scenarios.

Governance by design

With dbt, organizations can build trust in their data by automating governance. Every model change is version-controlled and documented, with sensitive fields protected using column-level security for traceability and compliance.

When regulations change, teams can update definitions centrally, allowing those updates to automatically flow across all dependent models. This ensures agents only access authorized and up-to-date data.

Transformation engine

dbt turns raw and fragmented data into tested and documented analytics-ready models. Built-in testing features like uniqueness checks, not-null constraints, and freshness alerts catch errors early in the pipeline.

These automated safeguards make sure agents interact with accurate and current data. dbt also scales with modern cloud platforms like Snowflake and BigQuery, so performance holds steady even during peak activity.

Semantic clarity

The dbt Semantic Layer enforces a consistent definition of business metrics across the organization. Centralized models ensure that terms like "churn" or "revenue" have the same meaning in every tool. These definitions are versioned, documented, and available to downstream systems.

MCP-ready metadata

dbt exposes metadata such as model documentation, column-level security, and data lineage to enable seamless integration with MCP servers. Agents query this metadata via standardized interfaces to understand data sources, transformation logic, and freshness timestamps. dbt inherits access policies from enterprise identity providers like Okta or Azure AD, reducing redundant permission configuration for MCP interactions.

Observability and feedback loops

dbt enables continuous data quality monitoring by running automated tests on every change to catch issues before agents access the data. Logging and alerts provide insights into freshness metrics, test outcomes, and pattern deviations. This visibility helps teams refine data quality and ensures agents operate with trustworthy inputs.

Enterprise impact

dbt addresses key operational needs for MCP servers:

• Unified access to governed data through documented models
• Consistent metric definitions across all systems
• Built-in compliance via audit trails and access controls
• Scalability with cloud platform automation

These capabilities ensure autonomous systems receive structured and reliable data without compromising on speed or governance.

Conclusion

MCP servers create a secure, standardized way for agents to access data. However, agents are only as effective as their upstream data.

dbt turns raw sources into tested, governed models by catching errors early through built-in checks like freshness, uniqueness, and null constraints. Data is modeled once and reused everywhere, so agents see consistent definitions and context no matter where they run.

Through dbt Cloud's MCP Server, teams can securely expose:

• Model-level lineage for dependency mapping
• Semantic Layer metrics for unified business logic
• Critical metadata for contextual decision-making

With dbt, you can both prepare high-quality data for MCP and serve it directly to LLMs, all from a single platform. To get started, sign up for a free dbt account today.