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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior, so the description only needs to add context beyond those. It adds useful behavioral detail: 'Field labels come back as label_en/label_ar — dataset_ids and field names stay in their native form,' which informs the agent about response shape. No contradictions with 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?

The description is three sentences with no fluff: purpose, capability summary, and return-format note. It front-loads the main action and keeps supporting details brief. Each sentence earns its place, though the capability list is somewhat redundant with the schema.

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 complex ODSQL tool with no output schema, the description covers key behavioral context (label localization) and the schema fully documents parameters and provides examples. Annotations handle safety traits. It is adequate, though a brief note on query complexity limits or using search_datasets first could make it more self-contained.

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% with detailed parameter descriptions and examples (e.g., 'ODSQL filter, e.g. year >= 2020 (overrides query)'). The description only lists parameter roles (filter, aggregate, sort, paginate) without adding new meaning beyond the schema. 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?

Description states a specific verb and resource: 'Query records from a Qatar government open data dataset with ODSQL.' It clearly distinguishes itself from siblings like search_datasets (finding datasets) and dataset_info (metadata) by focusing on record-level querying. No ambiguity about what the tool does.

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 implies usage for querying records within a dataset, but gives no explicit guidance on when to use this vs. alternatives such as search_datasets for finding datasets or search_within for other search operations. It does not state exclusions or direct the agent to prerequisites like obtaining dataset_id first.

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