An MCP server is a component of the Model Context Protocol (MCP), a standard designed to connect GenAI applications with enterprise data and AI tools.
An MCP server, or Model Context Protocol server, facilitates the standard communication between generative AI apps and the data they utilize, to simplify and accelerate the development of accurate and robust AI systems.
The need for MCP servers arises from the challenges in managing massive volumes of data scattered across various sources. Enterprises often struggle with integrating and effectively using this data, especially when it's siloed in different systems. MCP servers provide an effective solution for ensuring that LLMs get the right data at the right time, reducing the chances of AI hallucinations and other errors.
As enterprises increasingly adopt generative AI, the volume and variety of data these AI systems require can be overwhelming. Without a standard protocol, the necessity for custom integration with each new data source creates a significant scaling bottleneck.
The Model Context Protocol (MCP) offers a simple, open standard to establish secure, bi-directional communication between AI systems and the underlying data that they require. Data is made accessible via MCP servers, and AI apps (MCP clients) consume data through these MCP servers.
[Image to be designed]
MCP servers streamline this process by allowing rapid access to fresh data from source systems, ensuring real-time responses and maintaining high performance. Additionally, MCP servers place emphasis on privacy and security guardrails to prevent sensitive data from leaking into AI models. This ensures compliance with data protection regulations, safeguarding both the enterprise and its clients.
An MCP server manages the data communication between AI models and source systems. It implements conversational latency, which guarantees immediate response times critical for user interactions.
Here’s how it operates, step by step:
An MCP client (user’s application, bot, or service) sends a request to the MCP server. The request typically includes a specific query, command, or message, along with session/user identifiers or current context state.
The MCP server receives the client request, and examines the incoming context:
Who is the user and what access rights do they have based (RBAC)?
What’s the current session state?
What previous interactions or relevant data might affect this request?
If needed, the server updates or retrieves session information to ensure continuity and personalization.
The MCP server uses an LLM together with database schema definitions, data catalogs, API and data product directories, to parse the request and decide what underlying actions are needed: which backend(s) to query, how to compose the query (using text-to-SQL), what data masking is required, how to present the final answer, and what logic to apply.
The MCP server retrieves data from the backend data sources, where each data source might have its own data access method. It might fetch a user’s transactions from multiple SQL databases, documents from file storage, exchange rates from an API, or facts from a knowledge base.
Responses from backend data sources come back to the MCP server. The server merges, transforms, or anonymizes data as required, using the session context and business logic.
The MCP server constructs a structured response with all the needed results and returns it to the originating MCP client, along with any updated context.
By employing chain-of-thought reasoning and table-augmented generation (TAG), MCP servers can effectively orchestrate and manage data exchange to ground LLMs and deliver accurate and contextual responses. They facilitate conversational AI by supporting agentic AI models that require dynamic data access.
MCP servers are used across various industries, from healthcare to finance, to expose enterprise data to generative AI systems. Here are some examples:
Instead of AI apps directly accessing sensitive data, which poses security risks and requires complex, custom integrations, they connect to a single MCP server. The server handles authentication, authorization, dynamic data masking and data retrieval based on the MCP protocol, ensuring only necessary and permitted data is accessed.
Enterprises often have data fragmented across numerous, disparate systems. An MCP server can abstract this complexity by acting a semantic data layer across all underlying sources and providing a unified interface to these silos. An AI application needing information from different databases or applications can connect to a single MCP Server, which then orchestrates the data retrieval from the underlying systems. This simplifies and accelerates AI agent development and improves data accessibility for AI.
MCP Servers can act as gateways to internal and external APIs (e.g., exchange rates, stock market data, geocoding), taking care of authentication, formatting, and tokenization. AI apps can then easily incorporate external data without dealing with the intricacies of each individual API.
MCP Servers can provide access to curated data sets, enabling AI apps to operate with a richer understanding of the domain, leading to more accurate and contextual responses. For example, an MCP server in healthcare could expose medical codes, relationships between diseases and symptoms, etc.
MCP servers may expose specific AI tools or functions that the MCP client apps can leverage (e.g., an MCP server a tool to update a customer record Salesforce, initiate a HR approval workflow in Workday, or trigger an MRP in SAP.
By centralizing data access through MCP servers, AI teams can enforce data governance policies, including data masking, tokenization, audit logging, and guardrailing data access from unauthorized users.
K2view sees MCP servers as an essential component of modern GenAI infrastructure. By easily integrating with all data sources, K2view helps businesses overcome the complexities associated with augmenting LLMs with multi-source application data. With K2view GenAI Data Fusion, AI teams can easily implement a single MCP server to access secure, real-time enterprise data access, ensuring their AI apps are robust, secure, and reliable.
Interested in seeing how K2view can empower your AI apps with an MCP server for all data sources? Read more about our solution or experience it firsthand through our interactive product tour.
Discover GenAI Data Fusion, the K2view RAG tool – the only MCP server your company will ever need.