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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true and idempotentHint=false, so the safety profile is covered. The description adds useful behavior beyond that: repo type is auto-detected when repo_type is omitted, and preview requires prior discovery of config/split. It does not mention pagination, throttling, auth requirements, or what happens with unknown repo IDs, so it goes beyond annotations but not far.

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 capability and return contents in the first sentence, then spends the remainder on the dataset operations workflow, which is the non-obvious part. Every sentence carries information, though the trailing conditional clauses ('unless the dataset has a single config/split') make the last two sentences dense enough to require a second read.

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?

With seven parameters at full schema coverage, no output schema, and annotations carrying the safety profile, the description fills the remaining gaps: it lists what each operation returns, which substitutes for the missing output schema. It also covers the ordering constraint that would otherwise cause a failed dataset_preview call. Nothing essential to correct invocation is missing.

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 100%, so the baseline is 3 and every parameter is already documented. The description still adds meaning: it explains the auto-detect fallback for repo_type and the ordering dependency that makes config and split usable (discover via dataset_structure, or omit only for single-config/split datasets). That dependency relationship is the piece the schema alone underplays.

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 ('Get details') plus the resource ('one or more Hugging Face repos') and enumerates the payload (card metadata, license, downloads, files, Space hardware/status, dataset configs/splits/columns/preview rows). That enumeration makes the tool's scope unmistakable. It stops short of naming the sibling hub_repo_search as the alternative for discovery, so differentiation is inferable rather than explicit.

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?

Gives real operational guidance: 'Use dataset_structure first to discover configs, splits, sizes, and schema' and 'Use dataset_preview only when config and split are known, unless the dataset has a single config/split' — a clear when/when-not rule with an explicit exception. The alternative it routes against is an operations value rather than a sibling tool, and it never says when to prefer this over hub_repo_search or hf_fs.

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