Model Context Protocol (MCP) is a structured, interoperable standard that enables AI agents to query, invoke, and respond to external APIs or services. Think of MCP as a universal translator that allows Large Language Models (LLMs) like Claude to seamlessly connect with databases, APIs, file systems, and other services through a standardized interface.

At its core, MCP solves a critical AI development problem: the fragmented integrations landscape. Before MCP, each AI application required custom connectors and bespoke integrations for every external service it needed to access. MCP standardizes this process through a client-server architecture where AI applications act as MCP clients and external services expose themselves through MCP servers.

The Technical Architecture: How MCP Works

MCP operates on a simple yet powerful client-server model:

MCP Clients are AI applications (like Claude Desktop) that need to access external resources. These clients understand the MCP protocol and can communicate with any MCP-compliant server.

MCP Servers are lightweight processes that expose specific resources or capabilities to MCP clients. Each server implements the MCP specification to provide a consistent interface, regardless of the underlying system it connects to.

The protocol is built on JSON-RPC 2.0, ensuring reliability and broad compatibility across different programming languages and platforms. Communication happens through standard transport mechanisms like stdin, HTTP, or WebSocket connections.

Why MCP?

Universal Connectivity

Instead of building N×M integrations (where N is the number of AI applications and M is the number of services), MCP creates a 1×M model. Build one MCP server for your service; any MCP-compatible AI agent can use it immediately.

Democratized AI Integration

MCP dramatically lowers the barrier to entry for AI integrations. Developers no longer need deep expertise in specific AI frameworks or proprietary APIs. The standardized protocol allows you to focus on your domain expertise rather than integration complexity.

Security and Control

MCP maintains clear boundaries between AI agents and your systems. Each MCP server defines exactly what resources it exposes and what operations are permitted, giving you granular control over AI access to sensitive systems.

Composability and Modularity

Large language models are powerful, but they’re limited by their static and often outdated training data. MCP changes the game by giving AI access to current data and enabling it to interact with external systems like proprietary databases or third-party APIs. Imagine asking your AI assistant for the weather, a live stock quote, or even to initiate a payment—MCP makes all of that possible.

MCP servers can be combined and orchestrated to create complex workflows. An AI agent might simultaneously access your CRM through one MCP server, your documentation through another, and your deployment pipeline through a third—all using the same protocol.

You don’t need to retrain your model to teach it new tricks. With MCP, you can expose a new endpoint, and your AI is immediately empowered to use it. This makes it incredibly flexible for developers who want to add or update features on the fly, cater to different user needs, or even restrict access to specific tools.

Real-World Use Cases Enabled by MCP

Tap into your Enterprise Data – Picture an AI that can tap into your internal documentation and give precise, context-aware answers to employees. Or think about a customer support bot that pulls real-time order data from your CRM system.

Tap into your API’s – Integrate with APIs you already have. My code example does something simpler and lighthearted by invoking an API. It allows the MCP Client (Claude, in our case) to pull Chuck Norris jokes from the MCP server we write instead of depending on the LLMs’ responses. The detailed Spring MCP code example is here, with every step clearly defined in the README.md.

Tap into your Enterprise workflows – Want your AI to kick off a deployment in your CI/CD pipeline or schedule a meeting on your calendar? With MCP, that’s now possible.

The Ecosystem Impact

MCP is creating a thriving ecosystem where:

• Tool builders can create once and integrate everywhere
• AI application developers can focus on user experience rather than integration complexity
• Enterprise architects can standardize AI integrations across their organization
• Open source contributors can build reusable MCP servers for everyday use cases
• Marketplace – Developers can build and make available (free or paid) many MCP servers that can integrate with core offerings from the parent companies, or just independent servers that are good at doing something others need

Integrating a Java Spring-Based MCP Server with Claude Desktop

If you are running a custom MCP server, you can easily connect it to Claude Desktop to extend Claude’s abilities. Please refer to https://github.com/thomasma/chuckjokes-mcpserver where we use Spring’s MCP framework to build and package an MCP server and then integrate it into the Claude desktop app.

Open the Claude Desktop Settings, Navigate to Settings > Developer > hit Edit Config

That should get you to the location of the claude_desktop_config.json file, which you can edit using any text file editor. On my Mac, this location is “~/Library/Application Support/Claude/claude_desktop_config.json”

Once added, restart the Claude desktop app. Detailed logs for your running server can be located at “~/Library/Logs/Claude/mcp-server-dummy-chuck-jokes-server.log”

Test the Integration by submitting a prompt with your trigger phrase. You should see the output from your MCP server embedded in Claude’s response.