detect_dataset

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

With no annotations, the description carries the full burden. It does add one valuable behavioral fact beyond the schema — the result includes confidence and evidence, not just a bare format label — and "on-disk" implies a read-only inspection. However, it says nothing about failure behavior for unrecognized formats, whether the whole tree is scanned (cost/latency on large datasets), or whether a path must exist locally.

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?

One front-loaded sentence with zero filler; the format enumeration and the confidence/evidence clause each carry information an agent needs.

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 one-parameter detection tool with no output schema, the description is nearly sufficient: it names inputs, candidate outputs, and that results come with confidence and evidence. The remaining gap is the unrecognized-format/failure path, which an agent would want before calling it on arbitrary paths.

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?

Single parameter at 100% schema description coverage ("File or directory to inspect"), so the schema already does the work and baseline 3 applies. The description adds no path semantics such as local-vs-remote, symlink handling, or depth of inspection.

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?

Specific verb ("Identify") plus resource ("on-disk format of an embodied-AI dataset"), with the candidate formats enumerated so an agent knows exactly what output to expect. It is distinguishable from validate_dataset/convert_dataset/summarize_dataset by the format-identification framing, but it never explicitly contrasts with list_formats, which is the nearest ambiguous sibling.

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?

Usage is only implied: an agent can infer this is the pre-flight step when the format of a given path is unknown. There is no explicit when/when-not, no mention of ordering relative to validate_dataset or convert_dataset, and no disambiguation from list_formats (supported formats) or catalog_search.

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