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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds useful context such as ODSQL capabilities and the Bahrain open-data source, but it does not disclose response format, rate limits, or how query and where interact; no contradiction with annotations exists.

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?

Two tight sentences: the first front-loads the action and resource, and the second maps operation types to parameter names. There is no filler or repetition of schema details.

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?

The schema covers parameter details and provides examples, annotations cover the safety profile, and the description covers the main operation categories. It is sufficient for selection and invocation, though the absence of an output schema and any note on return shape or errors keeps it from being fully complete.

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 has 100% parameter coverage, so the description does not carry the full burden. It adds value by grouping parameters according to ODSQL functions (filter, aggregate, sort, paginate), helping the agent map intent to concrete params.

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 names a concrete verb ('query'), a specific resource ('Bahrain government open data dataset'), and the ODSQL language, making the tool's job unmistakable. This clearly distinguishes it from dataset-discovery siblings like search_datasets and dataset_info.

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 clearly signals row-level querying on an existing dataset, which implies it is used after dataset discovery rather than for finding datasets. It does not explicitly name alternatives or when-not-to-use conditions, so it stops short of full routing guidance.

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