check_submission

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

With no annotations supplied, the description carries the full burden. It does well on scope disclosure by listing every validation performed and implying a non-destructive read, but it never says what happens on failure (raises an error vs. returns a list of issues), whether it can modify or normalize the file, or how issues are reported.

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 core purpose and a compact enumeration of the checks, followed by a conventional Args block. The long comma-separated checklist sentence is dense but each item is informative; there is little filler.

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?

Parameter semantics are covered well, but with no annotations and no output schema, the description leaves the return contract entirely unexplained — an agent cannot tell whether a successful call yields a boolean, a report of issues, or an exception. For a validation tool whose whole value is the diagnostic result, that is a meaningful gap.

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 0%, so the inline Args section is doing real work: it identifies test_path as optional and explains its purpose, and gives id_col a meaningful default (first column of the sample submission) that the schema's bare 'null' default does not convey. The two path parameters are described only thinly ('the file to upload', 'the competition's sample submission'), which keeps this just short of a 5.

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 and resource ('Check a submission file against the sample submission') and enumerates exactly what is validated: columns, row count, duplicate/missing IDs, missing values, constant predictions, [0,1] range, per-row probability sums, and top-k label counts. This is clearly distinct from siblings like profile_dataset or compare_train_test, which operate on different objects.

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 explicit timing guidance ('before uploading it'), which frames the tool as a pre-submission gate, and explains the optional test_path as a way to verify every test ID is predicted. It does not state when to skip this check or what to do if the check fails, but the usage context is unambiguous.

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