opcua_model_validateValidate an OPC UA YAML model for correctness. Returns diagnostics with severity (error/warning/info), codes, messages, and line numbers. Works without an API key (limited to 50 calls/day). ALWAYS validate before generating.
| Name | Required | Description | Default |
|---|---|---|---|
| yaml | Yes | The full YAML model source to validate |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, openWorldHint, and idempotentHint, and the description adds valuable behavioral context beyond them: the exact diagnostic output shape (severity, codes, messages, line numbers) and the API-key-free rate limit. This fully covers what an agent needs to know about the call's behavior.
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?
Three concise sentences, each with purpose: what it does, what it returns, and when to use it. No filler or repetition of schema details.
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?
For a single-parameter validation tool with no output schema, the description adequately compensates by explaining the return structure (errors/warnings/info, codes, messages, line numbers) and the mandatory usage order relative to generation. An agent has enough context to invoke it correctly.
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?
The schema already documents the single 'yaml' parameter at 100% coverage ('The full YAML model source to validate'). The description adds no new parameter semantics, but none are needed; baseline 3 is appropriate.
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?
States a specific verb (validate), resource (OPC UA YAML model), and purpose (correctness), and clearly distinguishes from sibling generation/reversal tools. The 'ALWAYS validate before generating' instruction further clarifies its role in the workflow.
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?
Explicitly instructs when to use the tool ('ALWAYS validate before generating'), and provides operational context (works without an API key, 50 calls/day limit). This gives an agent clear selection and ordering guidance relative to generation siblings.
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.