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.
MCP AI standardizes the communication between generative AI apps and the data they use, simplifying and accelerating 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, like MCP, the necessity for custom integration with each new data source creates a significant scaling bottleneck.
The Model Context Protocol (MCP) for AI 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.
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.