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

Annotations already declare readOnly=true, idempotent=true, openWorld=true, destructive=false, so the safety profile is covered. The description adds a meaningful behavioral cue — that time-correctness rules should be consulted before backtesting and that the output supports cross-table reasoning — but does not disclose cost, latency, caching, or what happens with an invalid dataset_id.

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?

Front-loads the core value ('FULL semantics of one dataset') then enumerates returned components efficiently. The trailing 'Args: dataset_id.' is low-value filler, but overall the phrasing is tight and information-dense with little 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?

An output schema exists, so return-value explanation is largely redundant, and the description still maps the key sections an agent should expect. Annotations cover the safety profile, leaving only the dataset_id sourcing/format as an unaddressed gap for this single-parameter tool.

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 coverage is 0% and the only parameter note is 'Args: dataset_id.', which merely restates the property name. It does not state the expected format, where the id originates (e.g. from list_datasets), or how invalid ids behave, so the description fails to compensate for the coverage gap.

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?

States a specific verb+resource ('describe_dataset' → 'FULL semantics of one dataset') and enumerates exactly what those semantics comprise: grain, field meanings/units, time-correctness rules, relations, agent_hints, quant_use. An agent can tell this is metadata retrieval rather than data retrieval. It does not explicitly name the sibling it is not (e.g. query_dataset or list_datasets), which keeps it short of a 5.

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 'read before backtesting' note tied to TIME-CORRECTNESS hints at when this tool matters, and 'agent_hints (when to use)' and 'quant_use (which factors)' signal downstream use. However, there is no explicit contrast with alternatives like query_dataset or list_datasets, so usage is only implied.

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