What-is-an-MCP-client

Jun 2

What is an MCP client? 

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, audit-ability, and control. MCP is a synchronous, 2-way communication protocol, with 2 main components: 

  1. The MCP client – the consumer, running on the application side, that makes requests for data. 

  2. 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. 


How the MCP client interacts with the MCP server 

Here’s how the MCP client and MCP server work together over the MCP protocol: 

  1. The MCP client, typically embedded in an LLM-powered app or AI agent, creates a request for specific data or actions. 

  2. The MCP client transmits the request to the MCP server, secured by authentication and access policies. 

  3. The MCP server receives the request, handles permissions, retrieves data in real time from enterprise sources via Retrieval-Augmented Generation (RAG) or other GenAI frameworks, masks PII inflight, and packages the result. 

  4. The MCP server sends the response back to the MCP client for consumption by the GenAI app for grounded output. 

This process is designed to avoid AI hallucinations, maintain conversational AI latency, and protect against leaking sensitive information to unauthorized users or the LLM itself. 


What is the MCP client used for? 

MCP clients are used for various purposes, such as: 

1.   Enterprise data access

The MCP client can act as a unified interface to request data from internal data silos, like databases, applications, knowledge bases, 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. 

2.   Tool execution 

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 RAG, to automate process-driven actions. 

3.   LLM grounding 

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 other conversational AI apps. 

4.   Agent orchestration 

With multiple tool and data integrations, the MCP client can support LLM orchestrator agents that coordinate complex, multi-step processes – always under data governance and privacy controls. 

Top considerations in developing an MCP client 

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:    

  • SecurityImproper authentication or authorization can lead to unauthorized data access and security breaches.

  • Data privacyMCP clients might over-request data or mishandle privacy controls, leading to exposure of PII and other sensitive information to unauthorized users.  

  • PerformanceInefficient requests or poor handling of large datasets can overload MCP servers, over-consume LLM tokens, and negatively impact the client app’s performance.  

As revealed in our 2024 State of Data for GenAI report,
only 2% of businesses feel truly prepared for GenAI at scale –
data access, privacy, and security being the key impediments. 

 

K2view: Unified, secured, and governed data for MCP clients 

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 privacy and compliance, to prevent sensitive data from being accessed by unauthorized users 

  • Simplifying the connection to GenAI tools, through the MCP protocol. 



K2view ensures that your GenAI applications get only the data they need, when they need it and in real time – safely and with full context.  
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