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node-opcua-modeler-mcp-server

node-opcua-modeler-mcp-server

Official
by node-opcua
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

opcua_model_create

Generate an OPC UA YAML model from a natural language description using AI. Automatically detects companion specs and validates the model.

Instructions

Generate an OPC UA YAML model from a natural language description using AI. Requires an API key (set OPCUA_MODELER_API_KEY env var). The AI will auto-detect relevant companion specs, generate a validated model with documentation, and auto-correct validation errors.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "opcua_model_create"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYesNatural language description of the OPC UA model to generate
forceSpecsNoCompanion spec aliases to force (e.g. ["di", "ia"])

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.0

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses the AI-driven process, auto-detection of companion specs, validation, and auto-correction of errors. It does not describe output location or side effects, but covers key behavioral traits beyond basic function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the main action, then prerequisites, then process details. Every sentence adds value with no redundancy or unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 params, no output schema, no annotations), the description covers core functionality, prerequisite, and behavior. It lacks explicit mention of output format or next steps but is sufficient for an experienced user.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The description reiterates the purpose of 'prompt' (natural language) and implies context for 'forceSpecs' (companion specs), but does not add significant meaning beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates an OPC UA YAML model from natural language, using AI. It distinguishes from siblings like opcua_model_reverse (reverse engineering) and opcua_model_generate (possibly from other inputs) by specifying 'from a natural language description'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions a prerequisite (API key) but does not explicitly state when to use this tool vs alternatives like opcua_model_reverse or opcua_model_generate. The usage context is implied by the tool's name and sibling names, but no direct guidance is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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