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

With no annotations, the description fully carries the behavioral burden. It discloses the precise output contents, the payload difference between verbosity modes, the self-describing error behavior on unknown topics (including topic list and 'did you mean' hints), and the main_topic default behavior. No annotations are present to contradict it.

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 dense and every sentence earns its place, but it is one long unbroken paragraph that could be hard to scan. It is front-loaded with the core purpose and then layers detail, which is good, but formatting with paragraph breaks would improve readability.

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 description is complete for a tool of this complexity. It covers all parameters, defaults, verbosity modes, error behavior, usage before query_dataset, and important output fields like key_metric, recommended_query, and filter_hints. The output schema exists, but the description still adds interpretive context, making it self-sufficient.

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 only 33%, but the description compensates thoroughly. It explains the 'topic' parameter via the error/topic-list behavior, defines 'verbosity' values with concrete tradeoffs, and specifies 'main_topic' defaults and valid inputs ('education' default, 'census' alternative). This adds substantial meaning beyond the bare schema.

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 explicitly states the tool's job: returning the full schema for one topic, including columns, filters, foreign keys, example queries, and limitations. It is clearly distinguished from siblings like query_dataset by positioning itself as the schema-reading counterpart ('read this before query_dataset') and from list_datasets by focusing on a single topic rather than enumerating datasets.

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 gives explicit guidance on when to use the tool: read it before query_dataset, use verbosity='schema' when only filter keys/enums are needed, and prefer 'full' before reporting conclusions. It also tells the user to pass 'census' for Census topics, making the selection between default and alternative topics explicit.

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