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

With no annotations provided, the description carries the full burden of behavioral disclosure. It goes beyond a simple return statement by explaining the consequence of omitting the @context (invalid linked data, rejection by brokers). It does not explicitly state it's read-only, but 'Return' implies a non-mutating operation. This adds useful context beyond the bare functionality.

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 three sentences: the first states the operation, the second explains its importance, and the third provides a clear example. Every sentence earns its place, and the example is practical without unnecessary verbosity.

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 tool has one parameter, no output schema, and no annotations, the description sufficiently explains the tool's purpose, usage context, and return value. It could mention error handling for invalid model names, but that is not critical for such a straightforward lookup operation.

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 coverage is 100%, with the parameter 'model_name' already described with examples. The description reinforces this with a usage example but does not add significant new meaning beyond the schema. Baseline of 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?

The description clearly states the tool's function with a specific verb and resource: 'Return the NGSI-LD @context URL and context content for a specific data model.' This distinguishes it from sibling tools like get_data_model and get_attribute_details, which focus on other aspects of data models.

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 provides explicit usage context: 'required when building valid NGSI-LD linked-data payloads' and 'Use this before constructing or validating a normalized payload.' It also includes a concrete example, but does not explicitly mention when not to use it or name alternative tools, though the sibling list makes alternatives clear.

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