detect_dataset

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds that results include confidence and evidence, which is genuinely useful since there is no output schema, but it says nothing about behavior on unrecognized or ambiguous formats (error vs. low-confidence result) or whether the path is deeply scanned.

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?

A single front-loaded sentence with zero filler: the action, the target, the supported format set, and the output characteristics all appear in one pass. Nothing to trim.

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 read-only detector with annotations covering safety, the definition is nearly sufficient, and mentioning confidence/evidence compensates for the absent output schema. The remaining gap is failure semantics for unknown or mixed-format datasets.

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?

There is a single parameter with 100% schema description coverage ('File or directory to inspect'), so the schema already carries the semantics and the baseline of 3 applies. The description adds no extra constraints such as whether the path must exist locally or how symlinks/remote paths are handled.

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 ('Identify') and resource ('on-disk format of an embodied-AI dataset') and enumerates the exact format families it recognizes (LeRobot, HDF5/robomimic, MCAP, rosbag1/2, RLDS, canonical JSONL). This clearly separates it from siblings like list_formats (which enumerates supported formats rather than inspecting a path) and validate_dataset.

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 first step before convert_dataset or validate_dataset, but the description never says when to call it versus those siblings or what to do when detection is ambiguous. No exclusions or prerequisites are given.

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