bridge-mcp-server

Cross-platform intelligence bridge — correlate Ignition SCADA tags with Studio 5000 PLC logic end-to-end.

What This Does

bridge-mcp-server connects ignition-mcp-server and studio5000-mcp-server to answer questions like:

• "This Ignition alarm fires when tag X goes true — here's the PLC logic that drives X"
• "Which PLC tags are mapped to Ignition but have no OPC reference?"
• "Trace this SCADA tag all the way down to the ladder rung that controls it"

It maps Ignition OPC tag paths to L5X tag names using convention-based normalization (with optional explicit mapping file override), then leverages the Studio 5000 cross-reference engine to find every line of PLC logic that references the matched tag.

Part of Project Automate by Nodeblue.

Installation

pip install bridge-mcp-server

This automatically installs both ignition-mcp-server and studio5000-mcp-server as dependencies.

Quick Start

stdio (local)

bridge-mcp-server

SSE (remote)

bridge-mcp-server --transport sse --port 8082

Configuration

kiro-cli

{
  "mcpServers": {
    "bridge": {
      "command": "bridge-mcp-server",
      "args": []
    }
  }
}

Available Tools

ping

Health check. Returns "pong".

correlate_projects(ignition_path, l5x_path, mapping_file?)

Build a full correlation map between an Ignition project and an L5X PLC project.

correlate_projects("/path/to/ignition-project", "/path/to/plc.l5x")

Returns:

{
  "matched": [
    {
      "ignitionPath": "Conveyors/Line1/Running",
      "opcItemPath": "ns=1;s=[PLC]Motor_1.Running",
      "l5xTag": "Motor_1",
      "l5xMember": "Motor_1.Running",
      "l5xDataType": "Motor_UDT",
      "l5xScope": "controller"
    }
  ],
  "ignitionOnly": [],
  "l5xOnly": [
    {"name": "EmergencyStop", "dataType": "BOOL", "scope": "controller"}
  ],
  "stats": {"matched": 3, "ignitionOnly": 0, "l5xOnly": 5}
}

trace_tag(ignition_path, l5x_path, tag_name, mapping_file?)

Deep end-to-end trace of a single tag from SCADA to PLC logic.

trace_tag("/path/to/ignition-project", "/path/to/plc.l5x", "Running")

Returns the complete signal chain: Ignition tag config → OPC item path → L5X tag details → every rung/line of PLC logic that references it.

find_unmapped_tags(ignition_path, l5x_path, mapping_file?)

Identify commissioning gaps — tags that exist on one side but not the other.

find_unmapped_tags("/path/to/ignition-project", "/path/to/plc.l5x")

OPC Path Mapping

The bridge uses convention-based mapping by default:

| Ignition OPC Item Path | L5X Tag Name | |---|---| | ns=1;s=[PLC]Motor_1.Running | Motor_1.Running | | [PLC]Motor_1.Running | Motor_1.Running | | [PLC]Program:MainProgram.StartPB | Program:MainProgram.StartPB | | Motor_1.Running | Motor_1.Running (passthrough) |

For complex setups (aliased tags, scaled values), provide a JSON mapping file:

{
  "ns=1;s=[PLC]Custom_Alias": "Motor_1.Running",
  "ns=1;s=[PLC]Scaled_Speed": "LineSpeed"
}

Pass it via mapping_file parameter on any tool.

Example Conversation

You: What PLC logic drives the Conveyors/Line1/Running tag in Ignition?

Agent calls: trace_tag("/projects/MyPlant", "/plc/MainPLC.l5x", "Running")

Agent: The Ignition tag Conveyors/Line1/Running maps to PLC tag Motor_1.Running
via OPC path ns=1;s=[SampleController]Motor_1.Running.

Motor_1 is a Motor_UDT instance. Motor_1.Running is referenced in:
- MainProgram/MainRoutine rung 1: Motor_Control AOI call
- MainProgram/MainRoutine rung 2: Fault detection branch
- MainProgram/FaultHandler line 1: IF Motor_1.Faulted THEN...

The Motor_Control AOI sets Running from MotorFeedback (rung 4).

Project Structure

src/bridge_mcp_server/
├── __init__.py       # v0.4.0
├── __main__.py       # CLI entry point (stdio/SSE)
├── server.py         # FastMCP with 4 tools (ping + 3 correlation tools)
└── correlator.py     # OPC path normalizer + correlation engine

tests/
└── test_correlator.py

Development

git clone https://github.com/nodeblue-ai/bridge-mcp-server.git
cd bridge-mcp-server
pip install -e .
pip install pytest
pytest tests/ -v

License

MIT

Built by Nodeblue — Engineering-driven technology across software, industrial automation, and applied research.