MCP Server: What is the Model Context Protocol?

What is MCP (Model Context Protocol)?

The Model Context Protocol (MCP) is an open protocol introduced by Anthropic in November 2024 that standardizes how artificial intelligence models interact with the outside world. Before MCP, each integration between an LLM and an external tool (database, API, file system) required custom development. MCP proposes a universal protocol, comparable to what USB was for computer peripherals: a single interface to connect any tool to any AI model.

MCP architecture relies on a client-server model. An MCP server is a lightweight program that exposes specific capabilities: tools (functions the AI can call), resources (data the AI can read), and prompts (predefined instruction templates). An MCP client (integrated into a host like Claude Desktop, Cursor, or Claude Code) automatically discovers the capabilities exposed by connected servers and makes them available to the AI model.

Although very recent, MCP has experienced rapid adoption in the AI ecosystem. MCP servers already exist for major databases (PostgreSQL, MongoDB), cloud platforms (AWS, GCP), development tools (GitHub, GitLab), file systems, and dozens of third-party APIs. For Belgian and European businesses, MCP represents an opportunity to connect their internal systems to AI assistants in a standardized and secure manner.

Why MCP Matters

MCP solves a fundamental problem in the AI ecosystem: model isolation. Its strategic importance is considerable.

• Universal interoperability: an MCP server written once works with any compatible client (Claude, Cursor, Claude Code, and soon others). Conversely, an MCP client accesses any compatible server without specific development.
• End of N×M integrations: without MCP, connecting N models to M tools requires N×M integrations. With MCP, each tool and each model only needs a single protocol implementation.
• Security by design: MCP integrates access control mechanisms. The user can approve or deny each tool call, and servers precisely define the permissions granted.
• Open standard: unlike proprietary solutions (such as OpenAI's GPT Actions), MCP is an open standard that anyone can implement, avoiding vendor lock-in.
• AI agent accelerator: MCP is the missing link for autonomous AI agents. By providing them structured access to tools and data, it enables them to accomplish complex tasks in the real world.

How It Works

MCP architecture has three main components. The host is the user application integrating the AI model — for example, Claude Desktop or Cursor. The MCP client, integrated into the host, manages connections with MCP servers. MCP servers are lightweight processes that expose capabilities via the standardized protocol.

Communication uses JSON-RPC 2.0 over two transport types: stdio (standard input/output) for local servers, or SSE (Server-Sent Events) over HTTP for remote servers. When a host starts, it launches configured MCP servers and the client performs a handshake to discover available capabilities: the list of tools (with their parameter schemas), accessible resources, and predefined prompts.

When the AI model decides to use a tool (via function calling), the client forwards the request to the relevant MCP server, which executes the action and returns the result. The model then integrates this result into its reasoning. A typical MCP server is a Python or TypeScript script of a few hundred lines that encapsulates access to a specific service.

Concrete Example

At KERN-IT, KERNLAB develops and deploys MCP servers to connect AI assistants to clients' information systems. A recent use case: for an industrial client, the team created an MCP server that exposes access to their ERP (order reading, production statuses, stock levels) and their technical document base. The A.M.A AI assistant, connected via MCP, can thus answer team questions by directly querying business systems, without data leaving the client's infrastructure.

Internally, KERN-IT also uses MCP to enrich its own development tools. MCP servers connected to Cursor and Claude Code allow developers to directly query development databases, access production logs, and interact with internal APIs, all from their code editor via the AI model. This seamless integration eliminates context switching and accelerates problem diagnosis and resolution.

Implementation

1. Identify needs: list the tools, databases, and APIs that your teams or AI assistants need to access. Prioritize high-impact integrations.
2. Choose existing servers: check the MCP registry (mcp.so, GitHub) for pre-built servers covering common tools (PostgreSQL, GitHub, Slack, Google Drive, etc.).
3. Develop custom servers: for internal systems, develop custom MCP servers in Python (with the mcp SDK) or TypeScript (with @modelcontextprotocol/sdk).
4. Configure security: define granular permissions for each exposed tool. Limit read/write access following the principle of least privilege.
5. Deploy and connect: configure MCP servers in clients (Claude Desktop, Cursor) via JSON configuration files. Test each tool in isolation before full integration.
6. Monitor and iterate: track MCP tool usage, errors, and latencies. Refine tool descriptions to improve the AI model's function calling accuracy.

Associated Technologies and Tools

• Official SDKs: Python SDK (mcp), TypeScript SDK (@modelcontextprotocol/sdk) for server and client development
• Compatible clients: Claude Desktop, Cursor, Claude Code, Continue.dev, Zed — the ecosystem is expanding rapidly
• Popular servers: MCP servers for PostgreSQL, GitHub, Slack, Filesystem, Brave Search, Google Drive, AWS
• Related technologies: function calling (the underlying LLM mechanism), JSON-RPC 2.0 (the communication protocol), SSE (the remote transport)
• Registry: mcp.so and the GitHub repository modelcontextprotocol/servers as reference for available servers

Conclusion

The Model Context Protocol represents a fundamental evolution in the AI ecosystem, moving from isolated models to agents connected to the real world. Although very recent, MCP is already adopted by major AI development tools and its server ecosystem is growing rapidly. KERN-IT, through KERNLAB, is at the forefront of this technology by developing custom MCP servers that connect AI assistants to the information systems of Belgian and European businesses, with particular attention to security and data sovereignty.