Enable AI-powered industrial automation through Model Context Protocol integration.

Overview

The AI MCP for Runtime service bridges FrameworX solutions with AI language models, enabling intelligent automation assistance while maintaining industrial-grade safety. This connector exposes your solution's live data and functionality as structured tools that AI models can invoke.

Note: This connector is for querying live data from running solutions (connects to TServer.exe). For AI-assisted solution configuration, see AI MCP for Designer Connector.

Key Capabilities

• Real-time Data Access — Query live tag values, historian data, and alarm states through AI
• Unified Namespace Browsing — Explore your tag structure and discover available data
• Historical Analysis — Query historian for trend analysis and past behavior investigation
• Alarm Monitoring — Check active alarms and query alarm history
• Custom Methods — Extend with your own solution-specific AI tools
• Platform Agnostic — Works with Claude, GitHub Copilot, and other MCP-compatible AI models

When to Use MCP for Runtime

Prerequisites

• FrameworX 10.1 or later
• .NET 8.0 runtime
• Claude Desktop or compatible MCP client
• Running FrameworX solution (TServer.exe)
• Network connectivity

Configuration

The MCP Server starts automatically when your solution runs. You need to configure your AI client to connect to it.

Connection Methods

stdio Configuration

Use stdio when the AI client runs on the same machine as your solution.

Configure Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "YourSolution": {
      "command": "C:\\FrameworX\\fx-10\\net8.0\\TMCPServerStdio\\TMCPServerStdio.exe",
      "args": ["/host:127.0.0.1", "/port:3101"],
      "transport": "stdio"
    }
  }
}

Note: For Windows-only solutions, use the path <ProductPath>\fx-10\TMCPServerStdio\TMCPServerStdio.exe (without net8.0).

HTTP Configuration

Use HTTP when the AI client runs on a different machine than your solution.

MCP Server Settings (TMCPServerHttp.json in C:\Users\Public\Documents\FrameworX\MachineSettings):

{
  "appSettings": {
    "X_API_KEY": "",
    "CertFileName": "",
    "CertPass": "",
    "CertHash": "",
    "ListenPort": "3000",
    "Runtime": {
      "Host": "localhost",
      "Port": "3101",
      "Username": "guest",
      "Password": ""
    }
  }
}

AI Client Settings (GitHub Copilot .mcp.json):

{
  "servers": {
    "FrameworX Runtime": {
      "type": "http",
      "url": "http://localhost:3000",
      "headers": {
        "X-API-KEY": ""
      }
    }
  }
}

HTTP Settings Reference:

Multiple Connections: To connect to multiple runtimes, use /instance:<number> argument and matching numbered settings (e.g., X_API_KEY2, ListenPort2, Runtime2).

Verifying Connection

1. Click the LLM connector icon — you should see your solution name
2. Go to Settings → Developer and verify the connection shows "running"
3. Ask the AI a simple question like "What is the server uptime?"

Available Tools

AI MCP for Runtime provides these built-in tools:

Data Access

Historical Data

Alarms

Example Queries

Reading Values:

• "What is the current value of Tag.Temperature1?"
• "Get the value of Tag.Plant1/Line2/Pressure"
• "What is the Server.SystemMonitor.CPUUsage?"
• "How many active alarms are there?" (uses Alarm.TotalCount)

Browsing Data:

• "What tags are available under Tag.Plant1?"
• "Show me the members of Tag.Pump1" (for UserType tags)
• "What alarm groups exist?"
• "Find all tags containing 'Temperature'"

Historical Analysis:

• "Show me the temperature history for today"
• "Get the historian data for Tag.Pressure from 8am to noon"
• "What was the highest temperature value today and when did it occur?"

Alarm Investigation:

• "Are there any active alarms?"
• "Show me alarms from the last 24 hours"
• "How many alarms were acknowledged today?"
• "Query the alarm history for Tag.Temperature1"

Custom Methods

You can extend the MCP server with your own solution-specific tools by creating custom methods in Scripts.

Creating Custom Methods

1. Navigate to Scripts → Classes
2. Create a new class with type MCP Tool
3. Define methods using MCP decorators:

[McpServerTool, Description("Get current tank level for a specific tank")]
public string get_tank_level(
    [Description("Tank identifier (1-4)")] string tank_id = "1")
{
    return @Tag[$"Tank{tank_id}_Level"].ToString();
}

Method Structure:

Custom Method Examples

Simple Value Retrieval:

[McpServerTool, Description("Get production count for today")]
public string get_production_count()
{
    return @Tag.Production.TodayCount.ToString();
}

With Error Handling:

[McpServerTool, Description("Get data for a specific tag with validation")]
public string get_tag_data(
    [Description("Full tag path")] string tag_name)
{
    try
    {
        if (!@Tag.Exists(tag_name))
            return $"Error: Tag '{tag_name}' not found";
        
        return @Tag[tag_name].ToString();
    }
    catch (Exception ex)
    {
        @Info.Trace($"MCP Error: {ex.Message}");
        return "Error: Unable to retrieve data";
    }
}

Aggregated Data:

[McpServerTool, Description("Get summary of all tank levels")]
public string get_all_tank_levels()
{
    var result = new StringBuilder();
    for (int i = 1; i <= 4; i++)
    {
        var level = @Tag[$"Tank{i}_Level"].Value;
        result.AppendLine($"Tank {i}: {level}%");
    }
    return result.ToString();
}

Important: After creating or modifying custom methods, fully restart the AI client (close from Task Manager, not just the window).

Best Practices

For AI Queries

• Be specific about tag paths when asking for values
• Use "active alarms" for current state, "alarm history" for past events
• Specify time ranges clearly for historical queries
• Ask the AI to browse the namespace if you're unsure of tag names

For Custom Methods

• Use descriptive names and descriptions — the AI relies on these
• Include parameter validation and error handling
• Return clear error messages that help the AI understand what went wrong
• Follow snake_case naming convention for consistency
• Keep methods focused on single tasks

Security Considerations

• Use X_API_KEY authentication for HTTP connections
• Configure appropriate runtime user permissions
• Be cautious with write operations — consider read-only access for AI
• Review custom methods for security implications

Troubleshooting

MCP Server not starting

• Verify .NET 8.0 runtime is installed
• Check firewall settings for the configured port
• Confirm TMCPServerStdio.exe path is correct in AI client config

AI cannot access methods

• Ensure solution is running (TServer.exe active)
• Verify AI client configuration matches server settings
• Restart AI client completely (close via Task Manager)
• Check that MCP decorators are properly applied to custom methods

Data not updating

• Confirm tags are properly configured in the solution
• Verify real-time database connectivity
• Check custom method error handling for exceptions

Custom methods not appearing

• Verify class type is set to "MCP Tool"
• Check for syntax errors in method code
• Restart AI client completely after changes

HTTP connection failing

• Verify ListenPort is not blocked by firewall
• Check X_API_KEY matches between server and client
• For HTTPS, verify certificate is properly configured
• Test with http://localhost:3000 first before remote access

Related Documentation

Quick Start Tutorial

Build your first MCP connection step by step:

• AI MCP for Runtime Tutorial — Creating and deploying MCP Tools

Example Implementation

Explore a complete working example:

• SolarPanels MCP Demo — Full solution demonstrating MCP Tools with solar panel monitoring

Technology Information

• AI-Ready by Design — Platform architecture for AI integration

Reference Information

• Scripts Module Reference — Complete scripting documentation

AI MCP for Designer

For AI-assisted solution configuration (creating tags, displays, alarms):

• AI MCP for Designer Connector — Enable AI to configure solutions
• AI MCP for Designer Tutorial — Step-by-step guide for AI-assisted configuration

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