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OPC UA Modeler

by io.github.node-opcua

Create a model from a description

opcua_model_create
Read-only

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.

Input Schema

TableJSON 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. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the annotations, the description adds meaningful behavior: AI auto-detects companion specs, produces documentation, validates, and auto-corrects errors. It also surfaces the API-key requirement. It does not contradict readOnlyHint or openWorldHint.

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 and about 40 words, with the main purpose up front, then the prerequisite, then behavior. Every sentence adds information; there is no filler.

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 simple two-parameter schema, read-only annotation, and no output schema, the description provides enough context for selection and intent. It does not specify the exact return shape or failure modes, but the output type (YAML model) is clear.

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?

The input schema already covers both parameters with descriptions, so the 100% schema coverage sets the baseline. The description adds no parameter-specific meaning beyond reinforcing that prompt is the natural-language input.

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 states a specific action (generate), a precise output (OPC UA YAML model), an input source (natural language description), and a method (using AI). This distinguishes it from sibling operations like validate and reverse, even though opcua_model_generate is a close sibling.

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

Usage Guidelines4/5

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

It clearly implies when to use it: when an OPC UA model should be produced from an NL description, and it gives a hard prerequisite (OPCUA_MODELER_API_KEY). It does not explicitly name alternatives or exclusion cases, which keeps it below 5.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource or operation: unit lookup, reusable-block lookup, DSL reference, type details, namespaces, type listing/search, and the four model operations are all clearly separated. Even the two search-like tools (find_reusable_block and search_types) have different intents and return different kinds of results, so an agent should not misselect.

Naming Consistency3/5

Most utility tools follow a verb_first_snake_case pattern (find_*, get_*, list_*, search_*, resolve_*), but the four workflow tools use an opcua_model_* prefix (create, generate, reverse, validate). This is a blend of two systematic conventions rather than chaotic naming, but it is not fully uniform.

Tool Count5/5

Twelve tools is well within the ideal range for this domain, and each tool covers a distinct aspect of the modeling workflow from discovery and dependency resolution to validation and artifact generation. No tool feels redundant, and the count is appropriate for the stated purpose.

Completeness5/5

The tool surface covers the full lifecycle: discovering namespaces/types/units/reusable blocks, resolving dependencies, authoring YAML, validating it, generating artifacts, and reverse-engineering existing NodeSet files. There are no obvious dead ends or essential missing operations for the OPC UA modeling workflow.

Resources