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

With no annotations, the description carries the burden of behavioral disclosure. It transparently lists the exact output contents (NGSI type, data type, etc.) and gives an example invocation. The verb 'List' implies a read-only operation, adding clarity. It doesn't discuss errors or pagination, but for a single-parameter listing tool this is sufficient.

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 concise and well-structured: first sentence states the action and output, second provides usage context, third gives a concrete example. Every sentence earns its place with no redundancy or 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 one parameter, no output schema, and no annotations, the description is nearly complete. It explains the return content in detail and when to use the tool. It doesn't cover error cases (e.g., model not found), but that's a minor gap for a simple lookup tool. The inclusion of an example rounds out the context.

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?

Input schema has 100% coverage with a clear description of 'model_name' (exact name, examples). The tool description reinforces this with an example: get_attributes_for_model({"model_name": "WeatherObserved"}). This adds practical guidance beyond the schema's placeholder examples, earning a point above baseline.

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 clearly states the tool's purpose: 'List all attributes (properties) of a specific Smart Data Model' and details the scope (NGSI type, data type, description, units, reference URL). This distinguishes it from siblings like 'get_attribute_details' (single attribute) and 'get_data_model' (model itself).

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 explicitly says 'Use this after get_data_model when the user wants to understand what fields a model has...' providing clear context and sequencing. It doesn't mention when not to use it or alternatives, but the usage guidance is strong enough for an agent to choose this tool appropriately.

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