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.

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.

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.

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.

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.

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.