Inspect Dataset

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

No annotations are provided, so the description carries the full burden and does well: it discloses that the backend 400s without subject, that a comma-list loads a cohort, the valid mode values, and a non-obvious units caveat (loader assumes volts; µV-native files read 1e6x too large, fix via unitsScale). It omits cost/runtime characteristics and any auth expectations, keeping it short of a 5.

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-loaded with the purpose before the guidance and caveats; every sentence carries actionable information (the units note is dense but materially useful). Slightly long, but no filler sentences.

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 values need no explanation; the description instead covers the operational gaps an agent needs — the required subject argument, the mode values, cohort syntax, and the units gotcha. Nothing essential is missing for correct invocation.

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 0%, so the description must compensate and largely does: subject is explained (required, format 'S01', comma-list cohort), mode is enumerated (training | evaluation | all), and the units caveat affects interpreting the data. The dataset parameter itself is left unexplained beyond its name.

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 ('Exploratory summary of a public EEG dataset (MOABB pack)') and enumerates exactly what is returned (channels, sampling rate, trial/class balance, per-channel µV stats, band powers, PSD). This clearly separates it from siblings like list_datasets (enumeration) and inspect_file (single file).

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?

Explicitly states when to use it ('Look at the data BEFORE building pipelines'), explains the downstream consequences that motivate it (class balance drives stratification; flatlined channels indicate a montage/reference problem), and names the alternative for obtaining subject IDs (list_datasets). No inference required.

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