MCP Server and Client

Overview of the MCP Server & Client

The Model Context Protocol (MCP) server serves as a bridge that lets AI assistants—such as Claude Desktop—extend their capabilities by invoking external services. Rather than embedding all logic directly into the assistant, developers can expose custom tools and resources through a lightweight server that speaks MCP. This approach decouples the AI’s core language model from domain‑specific functionality, enabling rapid iteration and secure integration of proprietary data or APIs.

The server’s primary role is to host one or more MCP services. Each service declares a set of tools (annotated with ), prompts, and sampling strategies. When an assistant receives a user query that matches a tool’s description, it forwards the request to the MCP server, which executes the corresponding Java method (or any supported language) and returns structured results. Because the server runs independently of the assistant, developers can update or replace functionality without redeploying the AI client. This separation also simplifies permission management: only the server needs access to sensitive data or credentials, while the assistant remains agnostic of backend details.

Key features of this MCP implementation include:

• Java‑based tooling: The example uses JDK 17, allowing developers to write tools in familiar Java code. Commands and arguments are configured via the file, mirroring Docker‑style command specifications.
• Dynamic tool registration: Tools can be added or removed by editing the server’s source, then re‑deploying. The assistant automatically discovers available tools through MCP discovery.
• Rich response handling: Tools can return plain text, structured JSON, or even trigger downstream processes. The assistant then formats the response according to its own conversational style.
• Error isolation: If a tool fails, the assistant receives an error payload without crashing, enabling graceful degradation.

Real‑world scenarios that benefit from this architecture include:

• Enterprise data access: A company can expose internal databases or analytics dashboards as MCP tools, allowing employees to ask natural language questions that the assistant resolves via secure queries.
• IoT and automation: Devices or home‑automation scripts can be wrapped as MCP services, letting users control lights, thermostats, or security systems through conversational commands.
• Custom knowledge bases: A legal firm could provide a tool that fetches precedent cases from a proprietary repository, enabling attorneys to retrieve relevant information instantly.

Integrating the MCP server into AI workflows is straightforward: after configuring with the appropriate command and arguments, launching Claude Desktop automatically registers the server. The assistant then advertises its available tools in the chat UI, and users can invoke them by phrasing requests that match the tool descriptions. Because the server handles execution, developers enjoy a clean separation of concerns and can iterate on backend logic without touching the assistant’s core code.

Overall, this MCP server implementation empowers developers to augment AI assistants with custom functionality quickly and safely. By exposing tools through a standardized protocol, it turns an otherwise static language model into a dynamic platform capable of interacting with the world’s data and services.