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OPC UA Modeler

OPC UA companion spec types, namespace dependencies, and engineering units for AI agents

Install OPC UA Modeler

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "opc-ua-modeler": {
      "command": "npx",
      "args": [
        "-y",
        "node-opcua-modeler-mcp-server"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

The install command started, but didn't respond the way we expected when we tried to talk to it.

npx -y node-opcua-modeler-mcp-server

initialize succeeded but no response to tools/list.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 6d ago.

Overview

Tools7

What OPC UA Modeler lets your agent do. Expand a tool for its parameters.

Self-reported~190 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

8/8 checks healthy over the last 45d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 18h ago via OSV.dev · node-opcua-modeler-mcp-server (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars2Stargazers on the source repository.
npm downloads346Package downloads in the last 30 days.
Last commit5d agoMost recent push to the default branch.
Install checkInconclusiveStarted but did not complete the MCP handshake in our test.
Tools exposed7Callable tools this server registers over MCP.
Directory activity4 viewsConfig copies, upvotes, and views on AllMCPs.

From the project README

node-opcua-modeler-mcp-server

npm version npm downloads/month npm downloads total License: Apache-2.0 MCP

An MCP server that gives AI agents access to the OPC UA companion specification type system — 817 types across 33 industrial namespaces, plus 1,533 engineering units — and lets agents validate, generate, reverse-engineer, and create OPC UA information models.

Built on node-opcua, the most widely used OPC UA stack for Node.js.

Why?

When an AI agent needs to build an OPC UA information model, it must know:

  • What companion spec types exist (DI, Machinery, Robotics, Machine Tools…)
  • What components, properties, and methods each type has
  • What namespace dependencies are required
  • What engineering unit symbols are valid (UNECE Rec. 20)

This MCP server answers all of those questions — offline, for free, in milliseconds.

Quick Start

With Claude Desktop

Add to your claude_desktop_config.json:

config.json
{
  "mcpServers": {
    "opcua-modeler": {
      "command": "npx",
      "args": ["-y", "node-opcua-modeler-mcp-server"],
      "env": {
        "OPCUA_MODELER_API_KEY": "stfv_your_api_key_here"
      }
    }
  }
}

Note: The API key is optional for discovery tools (offline) and opcua_model_validate (50 anonymous calls/day). It is required for opcua_model_generate, opcua_model_reverse, and opcua_model_create. Register at opcua-modeler.sterfive.io and create a key under Settings → API — the free tier gives 25 calls/day for 90 days; see pricing for paid plans.

With any MCP client

Terminal
npx node-opcua-modeler-mcp-server

The server communicates over stdio using the Model Context Protocol.

Using a local backend instead of the hosted API

If you run the OPC UA Modeler CLI on the same machine, the model tools can be served from it instead of the hosted API — your YAML never leaves the host.

Start the server (requires a licence that includes the serve entitlement):

bash
opcua-modeler serve

Then set one environment variable in your MCP client config:

config.json
{
  "mcpServers": {
    "opcua-modeler": {
      "command": "npx",
      "args": ["-y", "node-opcua-modeler-mcp-server"],
      "env": {
        "OPCUA_MODELER_BACKEND": "local"
      }
    }
  }
}

No API key is needed in this mode — the client discovers the local endpoint and its credentials automatically.

VariableValuesPurpose
OPCUA_MODELER_BACKENDcloud (default), localWhich backend serves the model tools
OPCUA_MODELER_API_KEYstfv_…API key, cloud backend only
OPCUA_MODELER_API_URLURLOverride the hosted API base URL

Notes

  • The two backends are never mixed, and there is no fallback between them. If local is selected and no server is running, the call fails with instructions rather than silently sending your model to the hosted API.
  • The seven discovery tools are local to this package and work offline on either setting.
  • opcua_model_create (AI generation) is available on the cloud backend only.

Tools

list_namespaces

List all 33 OPC UA companion spec namespaces with aliases, URIs, and dependencies.

Code
→ list_namespaces()
← [
    { "alias": "di", "name": "OPC UA for Devices", "uri": "http://opcfoundation.org/UA/DI/", "dependencies": [] },
    { "alias": "robotics", "name": "OPC UA for Robotics", "uri": "http://opcfoundation.org/UA/Robotics/", "dependencies": ["di", "ia"] },
    ...
  ]

resolve_dependencies

Resolve the full dependency chain for companion spec aliases. Returns a topologically sorted list for the YAML namespaces: block.

Code
→ resolve_dependencies({ aliases: ["machineTool"] })
← ["di", "ia", "machinery", "isa95JobControl", "machineryJobs", "machineTool"]

list_types

List all ObjectTypes and VariableTypes defined in a companion spec namespace.

Code
→ list_types({ alias: "robotics" })
← [
    { "browseName": "MotionDeviceType", "kind": "ObjectType", "subtypeOf": "di:ComponentType", ... },
    { "browseName": "AxisType", "kind": "ObjectType", "subtypeOf": "di:ComponentType", ... },
    ...
  ]   // 25 types

get_type_details

Get the full structure of a type — components, properties, methods, interfaces, including inherited members.

Code
→ get_type_details({ alias: "robotics", browseName: "MotionDeviceType" })
← {
    "browseName": "MotionDeviceType",
    "kind": "ObjectType",
    "subtypeOf": "di:ComponentType",
    "interfaces": ["di:IVendorNameplateType", "di:ITagNameplateType"],
    "components": [
      { "browseName": "robotics:Axes", "nodeClass": "Object", "typeDefinition": "FolderType", "modellingRule": "Mandatory" },
      { "browseName": "robotics:PowerTrains", "nodeClass": "Object", "typeDefinition": "FolderType", "modellingRule": "Mandatory" },
      ...
    ],
    "properties": [
      { "browseName": "di:Manufacturer", "dataType": "LocalizedText", "modellingRule": "Mandatory" },
      { "browseName": "robotics:MotionDeviceCategory", "dataType": "MotionDeviceCategoryEnumeration", "modellingRule": "Mandatory" },
      ...
    ]
  }

search_types

Search for types across all companion specs by keyword.

Code
→ search_types({ query: "temperature" })
← [
    { "alias": "glass", "browseName": "MotorTemperatureTooHighEventType", ... },
    { "alias": "padim", "browseName": "TemperatureMeasurementVariableType", ... },
    { "alias": "amb", "browseName": "OverTemperatureConditionClassType", ... }
  ]

find_reusable_block

Find reusable Interfaces and AddIns by capability — pass a member name or keyword and get the standard blocks that already expose it. Prefer composing an existing block over redefining its members by hand.

Code
→ find_reusable_block({ query: "SerialNumber" })
← [
    {
      "browseName": "di:IVendorNameplateType",
      "alias": "di",
      "kind": "interface",
      "matchedMembers": ["di:SerialNumber"],
      "members": ["di:Manufacturer", "di:Model", "di:SerialNumber", ...]
    },
    {
      "browseName": "machinery:MachineIdentificationType",
      "alias": "machinery",
      "kind": "addin",
      "matchedMembers": ["di:SerialNumber"],
      "members": ["machinery:Location", "di:SerialNumber", ...]
    }
  ]

kind tells you how to apply it: interface goes in interfaces:, addin in addIns:.

find_engineering_unit

Find the official UNECE Rec. 20 engineering unit symbol. Supports fuzzy matching and natural language aliases.

Code
→ find_engineering_unit({ query: "celsius" })
← { "symbol": "°C", "matchType": "alias", "confidence": 1 }

→ find_engineering_unit({ query: "revolutions per minute" })
← { "symbol": "r/min", "matchType": "alias", "confidence": 1 }

→ find_engineering_unit({ query: "bar" })
← { "symbol": "bar", "matchType": "exact", "confidence": 1 }

get_dsl_reference ☁️

Fetch the YAML DSL grammar reference — the file header, top-level sections, type and instance syntax, and a list of common mistakes. No API key required.

Served from the API rather than bundled into this package, so the reference an agent reads always matches the validator that will judge its output.

Code
→ get_dsl_reference()
← { "version": "1", "reference": "# OPC UA Modeler YAML DSL ..." }

opcua_model_validate ☁️

Validate an OPC UA YAML model for correctness. Returns diagnostics with severity, codes, messages, and line numbers. Works without an API key (limited to 50 calls/day per IP).

Code
→ opcua_model_validate({ yaml: "namespaces:\n  di:\n..." })
← {
    "valid": true,
    "diagnostics": [
      { "severity": "warning", "code": "W001", "message": "...", "line": 42 }
    ]
  }

By path (local stdio server only). Pass model_path instead of yaml. With OPCUA_MODELER_BACKEND=local, the model's imports: then resolve from its own folder (beside it, then deps/), as in VS Code: an import that names only a uri: needs this, because a model sent as text resolves against the folder opcua-modeler serve was started in. The hosted connector never takes paths.

Code
→ opcua_model_validate({ model_path: "C:/models/cip/cip-example.model.yaml" })

opcua_model_generate ☁️

Generate OPC UA NodeSet2.xml and the symbols CSV from a validated YAML model. Requires an API key.

Keep NodeIds stable. The symbols CSV records the NodeId given to every node. A regeneration that does not pass it back allocates new ids, which breaks every server and client bound to the old ones. So each call must say what it is:

CallMeaning
symbols_csv: "<previous CSV>"Regeneration: existing nodes keep their ids. Accepts the CSV text, or the base64 symbols_csv a previous call returned.
mode: "new"First generation: there are no symbols yet.
mode: "renumber"Allocate new ids on purpose.
a self-contained .opcua.yaml streamIt carries its own symbol table, which is replayed.

A call with none of these is refused before reaching the backend with SYMBOLS_DECISION_REQUIRED, and the message says how to retry.

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Frequently Asked Questions about OPC UA Modeler

OPC UA Modeler is an MCP server that gives AI agents access to the OPC UA companion specification type system, including 764 types across 30 industrial namespaces and 1,533 engineering units. It supports validating, generating, reverse-engineering, and creating OPC UA information models through MCP tools built on node-opcua.

It helps AI agents work with OPC UA information models by discovering companion specification types, components, properties, methods, namespace dependencies, and valid UNECE Rec. 20 engineering unit symbols. It also provides tools to validate, generate, reverse-engineer, and create models.

The discovery tools work offline, and opcua_model_validate allows up to 50 anonymous calls per day. The seven discovery tools are local to the package and work with either the cloud or local backend; generation, reverse-engineering, and creation use a backend.

Add an MCP server entry that runs npx with node-opcua-modeler-mcp-server. The cloud backend uses the optional OPCUA_MODELER_API_KEY for model operations, while discovery tools and limited anonymous validation do not require a key.

You can use a local backend by running opcua-modeler serve and setting OPCUA_MODELER_BACKEND to local. This requires a CLI licence with the serve entitlement and keeps your YAML on the host; the local and cloud backends do not mix or fall back to each other.

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