An MCP client is the application-side consumer that requests data from an MCP server to ground LLMs, access enterprise data, trigger tools, and orchestrate AI agents securely.
An MCP client is an application (AI app, bot) that securely requests enterprise data from an MCP server to inform LLMs, access systems, and automate actions.
The Model Context Protocol (MCP) is an open, standardized way for LLMs and AI tools to interact with enterprise data while maintaining privacy, auditability, and control. It is a synchroneous, 2-way communication protocol, with two main components:
The MCP client – the consumer, running on the application side, that makes requests for data.
The MCP server – the provider, running within the enterprise, retrieving and filtering requested data (by the client) from internal systems.
The MCP client initiates requests to the MCP server, which decides which data should be returned, applies privacy rules, and then responds with only the allowed information. This approach allows GenAI apps to safely ground their responses in live enterprise data, with strong security at every step.
Here’s how the MCP client and MCP server work together over the MCP protocol:
The MCP client, typically embedded in an LLM-powered app or AI agent, creates a request for specific data or actions.
The request is transmitted securely to the MCP server, with authentication and access policies.
The MCP server receives the request, handles permissions, pulls data in real time from enterprise sources, leverages retrieval augmented generation (RAG) to search unstructured knowledge, applies data masking, and packages the result.
The response is sent back to the MCP client and consumed by the GenAI app for grounded output.
This process is designed to avoid hallucinations, keep conversational latency low, and protect against leaking sensitive information to unauthorized users or the LLM itself.
MCP clients are used for various purposes:
The MCP client can act as a unified interface to request data from internal data silos, like databases, applications, knowledgebases, or APIs. For example, it might pull live invoice data for a customer, gather past call interaction logs, or summarize the terms and conditions from a specific contract to respond to a user query.
The MCP client enables AI agents to securely trigger and control AI tools, such as updating Salesforce CRM data, triggering an HR workflow, or submitting a Zendesk support ticket. This allows for agentic AI, to automate process-driven actions.
The MCP client can query MCP servers to retrieve real-time data to ground the responses of Large Language Models (LLMs). This is key for GenAI apps, such as RAG chatbots and conversational AI.
With multiple tool and data integrations, the MCP client can support “orchestrator” LLM agents that coordinate complex, multistep processes – always under data governance and privacy controls.
There are several technical risks to consider when building an MCP client. Since an MCP client is a generative AI app that might interact with sensitive enterprise data and through an MCP server, it's crucial to address potential vulnerabilities. Here are some key risks to consider:
Security: Improper authentication or authorization can lead to unauthorized data access and security breaches.
Data privacy: MCP clients might over-request data or mishandle privacy controls, leading to exposure of PII and other sensitive information to unauthorized users.
Performance: Inefficient requests or poor handling of large datasets can overload MCP servers, over-consume LLM tokens, and negatively impact the client application’s performance.
As revealed in the K2view State of Data for GenAI survey, just 2% of businesses feel truly prepared for GenAI at scale – data access, privacy, and security being the main blockers.
Implementing the MCP protocol enables organizations to tap into their own data sources for GenAI applications, without compromising data security and privacy. But making the most of this protocol depends on MCP servers that can unify, secure, and expose multi-source enterprise data – structured and unstructured.
K2view GenAI Data Fusion solves these challenges through a single MCP server, by:
Unifying fragmented data, directly from the sources, and exposing it in conversational latency
Enforcing granular privacy and compliance to prevent sensitive data from being accessed by unauthorized users
Enabling easy connection to data-centric AI tools through the MCP protocol.