Model Context Protocol (MCP)

The Bridge Between AI and Your Coreflux Environment

Modern AI assistants are powerful — but they don’t natively understand LoT (Language of Things) syntax, Coreflux broker configuration, or the nuances of industrial IoT architecture. Without access to the official documentation, an AI assistant will guess, often producing code that looks plausible but uses invented syntax. The Model Context Protocol (MCP) solves this. MCP is an open standard that lets AI assistants — like Claude, GitHub Copilot, or any MCP-compatible client — call external tools during a conversation. The Coreflux MCP Server exposes the entire Coreflux documentation as a set of tools your assistant can query in real time. When you ask “how do I create a time-based LoT Action?”, your assistant doesn’t guess — it looks up the answer in the official docs and responds with verified syntax and working examples. The result: you stay in your editor, describe what you want in plain English, and get accurate LoT code grounded in real documentation.

Like giving your AI assistant a library card for Coreflux. Instead of guessing about LoT syntax or broker configuration, your assistant can look it up directly in the official docs and give you an accurate, sourced answer.

When to Use This

You want your AI assistant to answer Coreflux questions accurately using official documentation
You need LoT syntax help while coding in an AI-powered editor (Cursor, VS Code with Copilot)
You want to search the documentation through natural language without leaving your workflow
You’re building with Coreflux and want your assistant to have up-to-date reference material

How It Works

MCP (Model Context Protocol) is a standard created by Anthropic that defines how AI assistants communicate with external services. It works like a plugin system: an MCP server exposes a set of tools, and an MCP client (your AI assistant) discovers and calls those tools during conversation. Here is what happens when you ask your AI assistant a Coreflux question:

Step	What Happens
1. You ask	You type a question in natural language — “Create a LoT Action that monitors temperature sensors”
2. AI recognizes the domain	Your assistant detects this is a Coreflux question and decides to consult the MCP tools
3. MCP tool call	The assistant calls the Coreflux MCP Server — searching the documentation or asking the docs assistant
4. Documentation responds	The MCP server returns relevant documentation snippets, syntax references, and code examples
5. AI synthesizes	Your assistant combines the documentation with your specific requirements to produce accurate, grounded LoT code
6. You review	You receive a response with correct syntax, proper patterns, and source references you can verify

This loop happens automatically once the MCP is connected. You don’t need to tell your assistant to “use the MCP” — it discovers the available tools and calls them whenever your question relates to Coreflux.

Component	Role
MCP Server	Coreflux’s hosted service that exposes documentation tools
MCP Client	Your AI assistant (Claude, Copilot, etc.) that calls those tools
Transport	HTTP connection between client and server

Setup

Connect your AI assistant to the Coreflux documentation MCP server. Choose Native to install from the contextual menu on any documentation page, or Manual to paste the server URL into your editor’s configuration file.

Native
Manual

The fastest way to connect is through the contextual menu at the top of every documentation page. It copies the hosted MCP server URL or installs the server directly in supported editors — no JSON editing required.

Documentation page contextual menu expanded, showing Copy page, View as Markdown, Open in ChatGPT, Open in Claude, Copy MCP Server, Copy MCP install command, Connect to Cursor, and Connect to VS Code

Menu option	What it does
Copy MCP Server	Copies the hosted MCP server URL to your clipboard
Copy MCP install command	Copies the `npx add-mcp` command to install the server
Connect to Cursor	Opens Cursor and installs the MCP server
Connect to VS Code	Opens VS Code and installs the MCP server

VS Code
Cursor
Other clients

Open the contextual menu

On any Coreflux documentation page, click Copy page (top right) to expand the menu.

Connect to VS Code

Select Connect to VS Code. VS Code opens the MCP server installation flow for Coreflux Documentation.

Install the server

On the MCP server page, click Install (current window) or Install in Workspace (project only). Confirm trust if prompted.

VS Code MCP Server tab for Coreflux Documentation showing Install and Install in Workspace buttons and configuration with Type http and the MCP server URL

Enable tools in Copilot

Reload the window if needed, then open Copilot Chat in Agent mode and enable the Coreflux tools in the tool picker.

For Claude Desktop, Claude.ai, or other MCP clients that are not in the menu:

Copy the server URL

Open the contextual menu and choose Copy MCP Server. Paste the URL into your client’s MCP configuration.

Or use the install command

Choose Copy MCP install command and run the copied npx add-mcp command in your terminal, then follow the prompts for your editor.

Finish setup in your client

Restart the client and confirm the Coreflux Documentation server is connected. See the Manual tab for editor-specific configuration examples.

Add the Coreflux MCP server URL to your client’s configuration file. Use this method when you need full control over the config or when the contextual menu is unavailable.Coreflux MCP Server URL:

https://docs.coreflux.org/mcp

The Coreflux AI Starter repository has pre-built MCP config files for Cursor, VS Code, and Claude Desktop — copy the file for your editor instead of writing JSON by hand.

Cursor
Claude Desktop
Claude.ai
VS Code

Cursor supports MCP servers natively. Add the Coreflux MCP to your global configuration or to a specific project.

Open MCP Configuration

Navigate to your Cursor MCP configuration file. You can use the global configuration at ~/.cursor/mcp.json, or create a project-level .cursor/mcp.json in your workspace root.

Add the Coreflux MCP Server

Add the following entry to the mcpServers object:

{
  "mcpServers": {
    "coreflux": {
      "url": "https://docs.coreflux.org/mcp"
    }
  }
}

Restart Cursor

Restart Cursor or reload the window for the MCP server to be detected. You should see the Coreflux tools become available in your AI assistant’s tool list.

Verify the Connection

Open Cursor Settings and navigate to the MCP section. You should see coreflux listed as a connected server with its tools available.

If you already have other MCP servers configured, simply add the "coreflux" entry alongside them inside the existing mcpServers object.

Claude Desktop supports MCP servers through its configuration file.

Locate the Configuration File

Open the Claude Desktop configuration file at:

Windows
macOS
Linux

%APPDATA%\Claude\claude_desktop_config.json

~/Library/Application Support/Claude/claude_desktop_config.json

~/.config/Claude/claude_desktop_config.json

If the file doesn’t exist, create it.

Add the Coreflux MCP Server

Add or merge the following configuration:

{
  "mcpServers": {
    "coreflux": {
      "url": "https://docs.coreflux.org/mcp"
    }
  }
}

Restart Claude Desktop

Fully quit and reopen Claude Desktop. The Coreflux tools will appear in Claude’s tool picker (the hammer icon) when starting a new conversation.

Claude.ai (the web interface) supports remote MCP server connections through its Integrations settings.

Open Claude.ai Settings

Go to claude.ai, click your profile icon, and navigate to Settings.

Navigate to Integrations

Select the Integrations section in the settings panel.

Add the Coreflux MCP Server

Click Add Integration (or Add MCP Server), and enter the Coreflux MCP Server URL:

https://docs.coreflux.org/mcp

Give it a name like Coreflux and confirm.

Start a Conversation

Open a new conversation. The Coreflux tools will be available for Claude to use when relevant to your prompts.

Remote MCP server support on Claude.ai may require a Pro or Team plan. Check your plan’s feature availability.

VS Code supports MCP servers through GitHub Copilot’s agent mode. Ensure you have the GitHub Copilot extension installed and updated.

Open MCP Configuration

Create or open the .vscode/mcp.json file in your workspace root. Alternatively, you can configure MCP servers globally in your VS Code settings.json.

Add the Coreflux MCP Server

In .vscode/mcp.json, add:

{
  "servers": {
    "coreflux": {
      "type": "sse",
      "url": "https://docs.coreflux.org/mcp"
    }
  }
}

Or in your global settings.json:

{
  "mcp": {
    "servers": {
      "coreflux": {
        "type": "sse",
        "url": "https://docs.coreflux.org/mcp"
      }
    }
  }
}

Reload VS Code

Reload the window (Ctrl+Shift+P → Developer: Reload Window). The Coreflux MCP tools will become available in Copilot’s agent mode.

Confirm trust and enable tools

After reloading, confirm trust if prompted and open the “Configure Tools” button in the Chat input to verify the tools are enabled in the tool picker before proceeding.

Use in Copilot Chat

Open the Copilot Chat panel and switch to Agent mode. The Coreflux tools will be available for Copilot to call when relevant.

Verify Your Connection

After setup, confirm the MCP is working by sending a test prompt to your AI assistant. The specific prompt doesn’t matter — what matters is that the assistant calls the Coreflux MCP tools rather than answering from memory.

Quick Test

Send this prompt to your AI assistant:

Using the Coreflux documentation, show me the correct syntax for 
a LoT Action that triggers every 30 seconds and publishes a 
heartbeat message.

It’s working if your assistant calls one of the MCP tools during its response. In Cursor, you’ll see the tool calls in the assistant’s output. In Claude Desktop, look for the hammer icon indicating tool usage.

It’s not working if the assistant answers from general knowledge without calling any Coreflux tools. Double-check your MCP configuration and restart your client.

What a Connected Response Looks Like

When the MCP is active, your assistant’s response will:

Reference specific LoT syntax from the documentation (not invented patterns)
Include working code examples that match the official docs
Cite source pages you can open to verify the information
Use correct terminology — LoT, Actions, Models, Rules, Routes — exactly as defined in the documentation

Without the MCP, the assistant might still produce LoT-like code, but it will be based on general training data rather than the official Coreflux documentation. Always verify the connection before starting a development session.

Best Practices

Be Specific with Your Questions

The more specific your question, the better the results. Instead of asking “tell me about routes,” try “how do I configure a PostgreSQL data storage route with authentication.” Specific queries help the tools return more relevant documentation.

Ask for Source References

When you need to verify information, ask your assistant to include documentation source links. The Coreflux MCP tools can return references to official pages so you can open and verify the answer yourself.

Use for Code Review

Before deploying LoT code, ask your assistant to validate the syntax against the documentation. For example: “Check the Coreflux docs — is this the correct syntax for a Modbus TCP route?” This ensures your code follows the latest documented patterns.

Leverage Conversation Context

The documentation assistant supports multi-turn conversations. Ask follow-up questions to drill deeper into a topic without repeating context — the assistant remembers what you discussed previously in the same conversation.

The Coreflux MCP Server is currently in beta. The server URL may change as the service evolves. Check this page for the latest configuration instructions.

Next Steps

Developing LoT Using AI

A guide on utilizing AI agents to write, debug, and deploy LoT scripts directly through the MCP interface.

AI-Assisted Development Best Practices

Strategic advice on prompt engineering, security guardrails, and maintaining consistency when using AI to manage industrial assets.

Quick Start

Developing and Managing Coreflux

Coreflux MQTT Broker

LoT Language

Product Updates

The Bridge Between AI and Your Coreflux Environment

When to Use This

How It Works

Setup

Verify Your Connection

Quick Test

What a Connected Response Looks Like

Best Practices

Next Steps

Developing LoT Using AI

AI-Assisted Development Best Practices

Quick Start

Developing and Managing Coreflux

Coreflux MQTT Broker

LoT Language

Product Updates

Documentation Index

​The Bridge Between AI and Your Coreflux Environment

​When to Use This

​How It Works

​Setup

​Verify Your Connection

​Quick Test

​What a Connected Response Looks Like

​Best Practices

​Next Steps

Developing LoT Using AI

AI-Assisted Development Best Practices

The Bridge Between AI and Your Coreflux Environment

When to Use This

How It Works

Setup

Verify Your Connection

Quick Test

What a Connected Response Looks Like

Best Practices

Next Steps