opcua_model_reverseReverse-engineer a NodeSet2.xml file back into the YAML DSL format. Requires an API key (set OPCUA_MODELER_API_KEY env var). Optionally specify the target namespace URI to extract.
| Name | Required | Description | Default |
|---|---|---|---|
| xml | Yes | The NodeSet2.xml content to reverse-engineer | |
| namespace_uri | No | Target namespace URI to extract (auto-detected if omitted) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses an auth requirement not present in annotations: 'Requires an API key (set OPCUA_MODELER_API_KEY env var).' It also clarifies extraction behavior via the optional namespace URI. Since annotations already cover read-only/idempotent/open-world traits, the description adds valuable context beyond them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two tight sentences, front-loading the core purpose first. The second sentence combines the auth requirement and optional parameter without wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's purpose, input format, output format ('YAML DSL format'), auth prerequisite, and optional namespace filtering. With both parameters documented in the schema and read-only/idempotent annotations supplied, nothing essential for calling this tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds minor meaning by identifying the input as NodeSet2.xml content and mentioning the namespace URI as an extraction target, but it largely restates what the schema already says.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'Reverse-engineer a NodeSet2.xml file back into the YAML DSL format.' This clearly conveys the tool's core purpose and likely distinguishes it from siblings like opcua_model_generate, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used when converting NodeSet2 XML to YAML DSL and notes the API key prerequisite. However, it does not explicitly state when to prefer this over sibling tools like opcua_model_generate or opcua_model_create.
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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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.
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.
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.
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.