profile_dataset

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

With no annotations, the description carries the full burden, and it does disclose genuinely non-obvious behavior: max_rows takes a RANDOM sample rather than the head, and defaults to all rows. It also reveals that the tool emits diagnostic warnings (constant/ID-like columns, target leakage, date columns, repeated keys, free-text and file paths), which tells the agent the output is advisory and possibly noisy. It stops short of stating read-only/no side effects, memory or runtime behavior on large files, or how warnings are surfaced.

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: the first sentence delivers the core capability and output inventory, then a compact Args block maps each parameter to its meaning. The long parenthetical warning list is dense but each item is informative rather than filler; the docstring-style Args formatting is slightly verbose relative to the terseness of the rest.

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 four-parameter, read-only profiling tool with no annotations and no output schema, the description effectively serves as the return-value contract by naming every reported statistic. An agent knows what it will get and how sampling affects it. What is missing is the shape of the response (single report object? text?) and any limitations on file size or format.

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% and the titles are bare ('Path', 'Id Col', 'Target', 'Max Rows'), so the description must compensate, and it largely does: path is scoped to a CSV/Parquet table, id_col is defined by its effect (excluded from feature checks), and max_rows is defined by mechanism (random sample) and default (all rows). Remaining gaps are minor but real — no format/syntax guidance for path and no statement of what happens if target or id_col names don't exist.

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?

Opens with a specific verb+resource ('Profile a CSV/Parquet table') and then enumerates exactly what is computed: shape, column types, missing values, cardinality, duplicates, target balance and warnings. That enumeration implicitly separates it from siblings like compare_train_test or cv_baseline, but it never explicitly names which sibling to prefer for overlapping questions.

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: the Args section hints that target is used for balance/leakage checks and id_col is excluded from feature checks, so an agent can infer when these matter. There is no statement of when to call this versus inspect_competition or compare_train_test, and no prerequisites (e.g. file must be local, supported formats) are given.

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