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 clearly enumerates the returned information (description, NGSI type, data type, units, constraints, model URL), indicating a read-only lookup. No side effects or error conditions are mentioned, but for a simple retrieval tool this is adequate.

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 sentences plus an example, with no fluff. It front-loads the purpose, then lists the exact fields returned, and includes a concrete usage example. Every sentence earns its place.

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 simple read tool with two fully documented parameters, no output schema, and no nested objects, the description is complete. It covers what the tool does, what it returns, and when to use it. The provided example further anchors the usage 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?

Schema description coverage is 100%, so the baseline is 3. The description adds an example call with concrete values, but the parameter meanings are already fully documented in the schema. The example is helpful but does not fundamentally expand on the schema definitions.

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 fetches full details for a single attribute within a Smart Data Model, using a specific verb ('Get') and resource. It differentiates from siblings like get_attributes_for_model, which lists attributes, by focusing on a single attribute's detailed fields (description, NGSI type, data type, units, constraints, model URL).

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 includes explicit usage guidance: 'Use this when the user asks what type a specific field is, what units it uses, or how to populate it.' It lacks explicit when-not-to-use or named alternatives, but the context and example make the intended scenario clear.

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