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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds the key behavioral fact that invalid or unpublished ids return an empty upstream response, plus the no-auth requirement and the data source, which are useful beyond the annotations.

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 dense sentences: the purpose, the edge case, and the access context. Every sentence carries information, and the main verb and object are front-loaded with 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?

For a single-parameter read-only tool with a rich schema, an output schema, and clear annotations, the description covers all needed essentials: what it returns, where the id comes from, invalid-id behavior, data source, and authentication. Nothing an agent needs is missing.

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 itself already documents the datasource_id parameter with the same reference to statbel.list_datasources, an example UUID, and the empty-response behavior. With 100% schema coverage, the description adds no additional parameter meaning; 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?

The description uses a specific verb ('Get full metadata') and resource ('one Statbel datasource'), listing the exact fields (multilingual descriptions, supported locales, category, timestamps). It inherently distinguishes this detail endpoint from sibling list and data-query tools.

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 tells the agent to obtain the datasource_id from statbel.list_datasources, and notes the empty-response case for invalid ids. While it doesn't explicitly contrast with statbel.catalog.view_data, the reference to the list tool and the focus on metadata imply the appropriate use case.

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