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How to Configure MCP Servers in Claude Code

gordan

Mastering the configuration of MCP (Modular Control Platform) servers in Claude Code can seem intimidating at first, but with the right guidance, it becomes a powerful and efficient process. Whether you’re automating industrial systems, managing digital twins, or integrating edge devices, setting up your MCP server with Claude’s scripting environment enables higher scalability, precision, and control.

TLDR: Configuring MCP servers in Claude Code involves setting up the server environment, crafting modular control logic, and managing deployment workflows. It’s essential to understand key components like control modules, service interfaces, and deployment parameters. Having a clear architecture before starting greatly simplifies future updates and optimizations. This guide walks you through the must-know steps for seamless MCP integration using Claude Code.

1. What Is an MCP Server?

An MCP server (Modular Control Platform server) enables modular, distributed, and easily upgradable control logic execution across various devices or nodes. It’s a flexible architecture ideal for industry 4.0 applications, automation, and cloud-backed IoT systems. Claude Code, Anthropic’s structured programming language, makes it easier to create, test, and push control logic to these servers in a predictable and safe manner.

Claude Code supports configuration templates, version control, and type-safe operations—all essential when scaling across distributed environments.

2. Prerequisites

Before configuring your first MCP Server in Claude Code, make sure you have the following:

3. Defining the Modular Architecture

Before diving into code, map out your control architecture. MCP emphasizes modularity—think of each module like a microservice for control logic.

Ask yourself:

This planning shapes how you construct modules and allocate responsibilities across your system.

4. Creating Control Modules in Claude Code

In Claude, MCP modules are defined using the module keyword and encapsulate logical units of control. A sample control module might look like this:

module ConveyorControl {
  input speed: Float
  input direction: String
  output motorStatus: String

  function operate(): String {
    if speed > 0 {
      output.motorStatus = "Running " + direction
    } else {
      output.motorStatus = "Stopped"
    }
    return output.motorStatus
  }
}

This module is self-contained, easy to test, and can be reused or updated independently. Repeat the pattern for each area of your application (e.g., sensors, analytics, UI feedback).

5. Configuring MCP Server Settings

Once your modules are written and tested locally, you’ll need to configure the MCP server for deployment. The configuration involves:

Here’s an example of a simple configuration manifest in Claude-compatible YAML format:

mcp_server:
  name: LineOneServer
  modules:
    - ConveyorControl
    - SensorFetch
    - EmergencyStop
  io_mappings:
    - sensor1: SensorFetch.sensorValue
    - emergency_button: EmergencyStop.trigger
  security:
    encryption: AES256
    role_based_access: true

This manifest keeps the deployment consistent across updates and enables replication to other environments.

6. Setting Up Deployment Environment

You can simulate the execution environment locally or deploy directly to production-grade servers. Claude Code supports containerization (e.g., Docker) and cloud deployment integrations (such as AWS IoT Core or Edge). Set environment variables for seamless deployment using Claude CLI:

$ claude deploy --server-config server_manifest.yaml --modules ./modules --env production

This command validates the server setup, compiles the modules, and pushes them live. Automatically, the MCP server binds modules to IO sources and begins execution.

7. Testing and Monitoring the MCP Server

One strength of the Claude ecosystem is how well it supports testing. You can log internal changes, emulate events, or remotely monitor sensor values via the Claude Dashboard. Use real-time charts or mocked inputs for validation under test conditions:

Example code snippet to log changes:

on state_change in ConveyorControl.motorStatus {
  logger.info("Motor status changed to: " + ConveyorControl.motorStatus)
}

8. Updating Control Logic on Live Systems

Sometimes you need to push updates while systems are live. Claude facilitates hot-swapping of modules (with zero downtime) using the version control model. Here’s how:

  1. Modify your module code
  2. Tag the version (e.g., v2.1)
  3. Use CLI to push update:
$ claude module update ConveyorControl --version v2.1

MCP automatically handles dependency resolution and input/output remapping if they remain consistent between versions. Make sure to read real-time logs to confirm the system adapted without errors.

9. Best Practices

To ensure a robust and sustainable MCP server deployment, consider the following best practices:

10. Troubleshooting Common Issues

Here are a few common issues you may encounter when configuring MCP servers in Claude Code:

If errors persist, Claude CLI’s verbose debug mode and the Claude Dashboard trace logs can provide deeper insights.

Conclusion

Configuring MCP servers in Claude Code enables scalable, secure, and modular control systems. By following a clean architecture plan, writing reliable modules, and using well-structured configuration files, you can deploy responsive and adaptable solutions efficiently. Whether you’re building for IoT, industrial automation, or cloud-edge integrations, Claude provides just the right mix of structure and flexibility.

So start small, debug smart, and deploy confidently—your MCP-controlled infrastructure will take care of the rest!

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