Model Context Protocol can improve AI adoption if you take the right steps

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Article

Model Context Protocol can improve AI adoption if you take the right steps

Discover how Model Context Protocol (MCP) can streamline AI integration, boost scalability, and reduce risk if you take the right steps.

Table of contents

Topics

Model Context Protocol

Published

Published on Nov 11, 2025

on Apr 17, 2025

•

6 min read time

As businesses race to make AI more adaptable and useful in real-world applications, a breakthrough known as the Model Context Protocol (MCP) could accelerate that progress. Developed by Anthropic, MCP is an open-source standard that simplifies how AI systems interact with external tools, databases, and services. It introduces a universal framework for these interactions, eliminating the need for custom integrations and making AI systems more scalable, flexible, and efficient.

MCP opens the door to new levels of automation, decision-making, and operational agility. However, adopting this technology requires thoughtful preparation to address challenges such as security risks, implementation complexity, and governance needs.

Why MCP matters

MCP standardizes how AI systems connect with external tools and data sources. Traditionally, integrating AI with third-party services has involved building custom APIs and customized configurations for each tool, which is a slow and resource-heavy process. MCP changes this by offering a universal protocol that allows AI models to interact easily with external systems through standardized “MCP servers.”

Consider an AI-powered customer support assistant retrieving client data from a company’s CRM system. With MCP, the assistant can query an MCP server connected to your CRM without requiring custom integration. This simplicity shortens development cycles and enables businesses to expand your AI capabilities without disrupting existing workflows.

MCP’s benefits go beyond convenience:

It provides real-time access to live data rather than relying on static or pre-indexed information.
It promotes interoperability between different AI systems and tools.
It reduces vendor lock-in by offering a standardized framework that works across platforms.

Those advantages translate into faster decision-making, stronger customer experiences, and smoother operations.

MCP complements RAG

MCP complements retrieval-augmented generation (RAG) to enhance the capabilities of your AI systems. RAG is a method where a language model retrieves relevant external information (often from the enterprise data represented as a vector database or a knowledge graph) and integrates it into its response generation pipeline. This approach improves accuracy, reduces hallucinations, and allows for domain-specific or real-time data to be used in AI outputs.

MCP, on the other hand, provides a standardized framework for connecting AI models to external tools and data sources, acting as a universal interface. When combined, RAG can handle the retrieval of relevant information while MCP organizes and structures that information into modular context layers. This results in seamless integration into your AI workflow.

The decoupling of retrieval logic (handled by RAG) from tool integration (managed by MCP) allows businesses to scale your AI systems more efficiently while maintaining flexibility. Together, MCP and RAG create a foundation for building intelligent, context-aware AI systems capable of delivering accurate, actionable insights across diverse applications

MCP adoption incurs security challenges

Despite its potential, MCP adoption introduces new security risks that businesses must address early. The decentralized nature of MCP servers increases the risk of exposure if safeguards are not in place.

A significant vulnerability involves authentication token theft. MCP servers often store tokens (such as OAuth tokens) that grant access to external services. If an attacker gains access to these tokens, they could enter sensitive systems or perform unauthorized actions, creating a “keys-to-the-kingdom” scenario.

To guard against these and others risks, businesses should adopt strong security measures such as encrypting authentication tokens, monitoring AI-client and MCP-server interactions continuously to spot anomalies, and requiring human approval workflows for sensitive operations.

Handling these vulnerabilities head-on strengthens both the safety and reliability of MCP deployments.

Take the right steps to adopt MCP successfully

To deploy MCP effectively, businesses need to follow a series of technical steps that bring about seamless integration, scalability, and performance. These focus on setting up the MCP architecture, optimizing interactions between AI models and external resources, and making the system robust enough for real-world applications.

Assess contextual needs

Evaluate what your AI model requires in terms of external data and actions. Identify the specific tools (e.g., APIs or databases) and resources (e.g., knowledge bases or structured datasets) the model will interact with. For example, a customer service chatbot might need access to real-time ticket statuses and the ability to create new tickets. This assessment defines the scope of your MCP implementation and helps ensure that the system is designed to fill existing context gaps in the AI’s capabilities.

Design and build MCP servers

MCP servers act as intermediaries between AI models and external tools or data sources. Businesses must design these servers to handle specific tasks, such as querying databases, interacting with APIs, or processing structured data. Developers can use lightweight frameworks and programming languages like Python to build these servers.

Each server should expose clearly defined tools (actions the AI can perform) and resources (data it can access). To support scalability and maintainability, containerize MCP servers using platforms like Docker and deploy them on cloud infrastructure or on-premises environments.

Implement MCP clients

The MCP client is a critical component that bridges the AI model with MCP servers. It handles requests from the model, routes them to appropriate servers, and injects the retrieved data back into the model’s context for decision-making. The client must support capability discovery (querying servers to understand available tools and resources) and dynamic routing of requests based on the AI’s needs. For example, if an AI assistant needs weather data, the client should know which server provides access to a weather API and route the query accordingly.

Optimize context injection

Once data is retrieved from MCP servers, it must be injected into the AI model’s context in a way that enhances its decision-making process without overwhelming its input limits. This involves designing prompts that include only relevant information from external sources while maintaining clarity for the model. For instance, if an AI assistant retrieves a customer’s order history from a database, only key details like recent purchases should be included in the prompt.

Test in simulated environments

Before deploying MCP in production, test the entire system in controlled environments that simulate real-world scenarios. This includes handling complex queries that require chaining multiple tools (e.g., retrieving weather data and combining it with traffic information for travel time estimates) and edge cases like tool failures or missing data. Use performance benchmarks such as response time and accuracy to evaluate system efficiency.

Deploy gradually

Deploying MCP gradually minimizes risks associated with large-scale rollouts. Start with a shadow mode deployment where the new MCP-enabled system runs alongside existing systems without affecting users. Compare results to identify discrepancies or issues. Once stable, move to a canary release by rolling out MCP functionality to a small subset of users (e.g., 5%) before scaling up based on feedback.

Iterate with feedback loops

Post-deployment, continuously gather feedback from users and monitor system performance to refine interactions between AI models and MCP servers. Adjust prompts, optimize resource usage, and update server configurations based on real-world usage patterns. This iterative process ensures that the system evolves to meet changing business needs while maintaining high performance.

Centific’s frontier AI data foundry platform can support your journey

Implementing MCP successfully often requires robust support systems for data management, model optimization, scalable deployment, and governance. This is where Centific’s frontier AI data foundry platform excels. Centific offers tailored services that help businesses overcome key hurdles in MCP adoption:

High-quality data curation to power reliable AI outcomes.
Supervised fine-tuning to align models with specific business needs.
Scalable deployment options across cloud or on-premises environments.
Advanced governance frameworks that protect against security vulnerabilities and help meet global compliance standards like GDPR and CCPA.

Tapping into Centific’s expertise and platform capabilities gives businesses both technical strength and strategic guidance at every stage of your MCP journey.

Learn more about Centific’s frontier AI data foundry platform.

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