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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable context beyond annotations: it states execution happens under the active API key, notes there is no separate per-route rate limit, and describes the return shape (request_id and results array with key, data envelope, metadata, or error). This is meaningful behavioral disclosure beyond the structured fields.

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 compact and front-loaded: it states the core action and use case in the first sentence, then covers defaults, safety, and return shape in three more sentences. Every sentence earns its place with no redundancy.

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 batched query tool with nested objects and no output schema, the description is fairly complete: it explains the batching model, the relationship to query_data, the defaults mechanism, the read-only nature, and the return structure. It could be slightly stronger by explicitly noting that per-item errors are captured in the results array, but the description already implies this by mentioning 'or error' in the return shape.

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 50%, and the description adds some meaning by explaining that defaults provide shared dataset_id, filter, limit, and order, and that each item reuses the query_data contract. However, the description does not deeply elaborate on the queries array structure beyond what the schema already states. The baseline of 3 is appropriate because the schema covers the core parameter semantics and the description adds moderate context.

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 states a specific verb ('Execute'), a resource ('several governed exact queries in one API call'), and the intended use case ('multi-metric prompts'). It also distinguishes itself from query_data by noting it reuses the same query contract, making it clear this is the batched variant.

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 explicitly says to use this for multi-metric prompts after choosing a dataset with list_data and get_data_summary. It also references query_data as the per-item contract, giving the agent a clear path to decide when to use this tool versus alternatives.

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