get_dataset_schema

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

With no annotations at all, the description carries the full burden of behavioral disclosure. It says 'Infer available columns and example values' but doesn't state whether this performs any sampling/mutation, what the returned structure looks like, or whether it's a safe read operation. For a schema-inference tool, transparency about whether it computes from actual data vs. metadata would be valuable context that's missing.

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?

Single sentence, no filler, gets to the point quickly. It's appropriately short, though arguably too terse given the missing context. No structural issues.

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?

Despite having an output schema, the description doesn't explain when this is needed, how it differs from related tools (find_cruises, resolve_variable, list_dataset_rows), or what the two required parameters should be. For a schema-inference tool operating on a two-parameter input where neither is documented, this is under-specified.

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%, yet the description adds no parameter-level detail. It doesn't explain what 'dataset' vs 'cruise_name' mean, their relationship, or how they constrain the schema returned. The description names neither parameter. With zero schema coverage and no compensating param info, the baseline is not met.

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?

The description states a clear verb+resource ('Infer available columns and example values') for a cruise dataset, which distinguishes it functionally from siblings like list_stations and get_cast_profile. However, it's somewhat generic—'cruise dataset' is vague and doesn't clarify what specific kind of schema is returned or what distinguishes it from other schema-related tools in the set. It lacks specificity about scope (a single cruise's data).

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?

No guidance on when to use this tool vs alternatives. The description doesn't mention when schema inference is needed, what prerequisite context is required (e.g., whether cruise_name must be resolved first via find_cruises), or when siblings like resolve_variable or list_dataset_rows might be more appropriate. This is a real gap given the large sibling set.

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