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

In This Page

How It Works — The architecture connecting your AI to Coreflux
Setup — Installation instructions for Cursor, Claude Desktop, Claude.ai, and VS Code
Available Tools — Reference for the tools provided by the Coreflux MCP Server
Verify Your Connection — Test that everything works
Best Practices — Tips for getting the most out of the integration
Next Steps — Where to go from here

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

The Coreflux MCP Server is available as a hosted service. To connect your AI assistant, add the server URL to your client’s MCP configuration. Coreflux MCP Server URL:

https://mcp-beta.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 configuring manually.

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://mcp-beta.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://mcp-beta.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://mcp-beta.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://mcp-beta.coreflux.org/mcp"
    }
  }
}

Or in your global settings.json:

{
  "mcp": {
    "servers": {
      "coreflux": {
        "type": "sse",
        "url": "https://mcp-beta.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.

Available Tools

Once connected, the Coreflux MCP Server provides two documentation tools. Your AI assistant discovers these tools automatically and uses them when your questions relate to Coreflux.

Search Documentation

Tool name: consult_documentation Performs semantic and keyword search across the full Coreflux documentation. Returns ranked results with titles, URLs, and content snippets from actual documentation pages.

Parameter	Type	Required	Description
`query`	string	Yes	The search query. Be specific for better results
`page_size`	integer	No	Number of results to return (1–20). Default: 10
`response_format`	string	No	Output format: `markdown` or `json`. Default: `markdown`

Best for:

Looking up specific syntax (e.g., “CALL PYTHON syntax”, “ON TOPIC trigger”)
Finding code examples for a particular feature
Browsing documentation pages on a topic
Quick reference lookups

Example prompts:

“Search the Coreflux docs for Modbus TCP route configuration”
“Find examples of LoT actions that use Python integration”
“Look up the syntax for data storage routes”

Ask Documentation Assistant

Tool name: consult_internal_documentation An AI assistant trained on Coreflux documentation that provides synthesized, natural language answers. It understands context, compares approaches, and includes source references from the documentation.

Parameter	Type	Required	Description
`query`	string	Yes	Your question about Coreflux, LoT, or related topics
`conversation_history`	string	No	Previous conversation in JSON format for multi-turn context

Best for:

Learning new concepts with explanations (e.g., “What is the difference between Actions and Models?”)
Step-by-step guidance (e.g., “How do I create a time-based action?”)
Comparing approaches and understanding best practices
Getting architecture advice (e.g., “How should I structure a system to monitor 100 sensors?”)

Example prompts:

“Ask the Coreflux docs: what are the best practices for error handling in LoT actions?”
“How do I set up a PostgreSQL route to store sensor data?”
“What’s the difference between LoT Rules and LoT Actions?”

Using Both Tools Together

The two tools complement each other. A good workflow is:

Start with the assistant (consult_internal_documentation) to understand concepts and get guided explanations
Follow up with search (consult_documentation) to find additional code examples or dive deeper into specific reference pages

Your AI assistant may use both tools automatically in a single response when it needs both conceptual understanding and specific code references to answer your question fully.

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 Coreflux tools (consult_documentation or consult_internal_documentation) 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 the documentation source links. The consult_internal_documentation tool includes source references in its responses, letting you navigate directly to the relevant docs page.

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 and available tools 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

MQTT Broker

AI

LoT Language

LoT Notebooks

Actions

Routes

Models

Rules

The Bridge Between AI and Your Coreflux Environment

When to Use This

In This Page

How It Works

Setup

Available Tools

Search Documentation

Ask Documentation Assistant

Using Both Tools Together

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

MQTT Broker

AI

LoT Language

LoT Notebooks

Actions

Routes

Models

Rules

​The Bridge Between AI and Your Coreflux Environment

​When to Use This

​In This Page

​How It Works

​Setup

​Available Tools

​Search Documentation

​Ask Documentation Assistant

​Using Both Tools Together

​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

In This Page

How It Works

Setup

Available Tools

Search Documentation

Ask Documentation Assistant

Using Both Tools Together

Verify Your Connection

Quick Test

What a Connected Response Looks Like

Best Practices

Next Steps