Model Context Protocol MCP Client-Server Pattern for AI Building an Interactive Voice Agent

MCP Explained: How to Connect Any LLM to Any Tool

Published on: December 7, 2025MCP

The Model Context Protocol (MCP) is a standardized, open protocol designed to solve the "N x M" integration problem in AI—connecting N different LLMs or AI agents with M different external tools, data sources, and services.It acts as a universal language or a standardized interface (like a USB-C port for AI) that enables an LLM to reliably discover, access, and execute external functions in real time.

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Core MCP Components

🏗️ MCP is built on a client-server architecture, typically using JSON-RPC 2.0 messages over a transport layer(likeSTDIO for local or HTTP/SSE for remote connections).
Component Role Description
MCP Host The top-level application or environment that houses the LLM and the client, like an orchestrator. It handles the user interaction. A custom AI chatbot, an AI-enhanced IDE (like Cursor), or a conversational AI platform.
MCP Client Code within the Host that manages the connection to MCP servers. It translates the LLM's requests into MCP format and server responses back to the LLM. The McpToolset class in the Java ADK.
MCP Server The external service that exposes a set of capabilities (Tools, Resources, Prompts) and connects to a real-world system (e.g., a database, an API, a file system). A server providing database_query or send_email tools.
Transport Layer The communication channel between the MCP Client and MCP Server, which can be local (STDIO) or remote (HTTP/SSE). STDIO for local connections or HTTP/SSE for remote connections.

Benefits of Using MCP

  • Interoperability: MCP provides a standardized way for different LLMs and tools to communicate, reducing the complexity of integrations.
  • Scalability: Easily add or swap out tools and services without changing the core LLM logic.
  • Flexibility: Supports various transport layers and can be adapted to different architectures and environments.
  • Real-time Functionality: Enables LLMs to access up-to-date information and perform actions in real time.
  • Extensibility: New tools, resources, and prompts can be added to MCP servers as needed, enhancing capabilities over time.

🛠️ The Tool Interaction Flow

The typical flow of interaction between an LLM and external tools via MCP is as follows:

  1. Tool Discovery: The MCP Client requests the list of available tools from the MCP Server using the mcp_getTools method.
  2. Tool Selection: The LLM selects the appropriate tool based on the user's query and the available options.
  3. Tool Invocation: The MCP Client sends a request to the MCP Server to invoke the selected tool using the mcp_invokeTool method, providing necessary parameters.
  4. Tool Execution: The MCP Server executes the requested tool and returns the result to the MCP Client.
  5. Response Handling: The MCP Client processes the response and presents the result to the user through the LLM.

Why MCP is the Right Choice for Enterprises

While concerns about context bloating and cost optimization are valid, MCP remains the preferred choice for enterprises due to its standardized approach to LLM-tool integration. MCP's architecture allows for efficient management of context by enabling selective tool invocation, which helps mitigate unnecessary data transmission. Additionally, MCP's extensibility and interoperability facilitate seamless integration with existing enterprise systems, making it easier to adopt and scale AI solutions. By leveraging MCP, enterprises can ensure that their AI applications remain flexible, maintainable, and cost-effective in the long run.

Note:

It is also important to note that for enterprise applications, one would typically not connect to massive general-purpose MCP servers, which would potentially add a large and irrelevant context. Instead, enterprise would deploy their own private MCP servers that expose only the relevant tools and data sources needed for their specific use cases. These private servers can be optimized for specific use cases and curated set of tools/capabilities ensuring that the context remains focused and relevant, thereby minimizing bloat and associated costs.

Let's see this in action with a simple example of an LLM using MCP to query a tool or send an email.