List TAP columns

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds the key constraint that the table must be an exact metadata-discovered table and clarifies that the operation returns column metadata instead of data. There is no contradiction with annotations.

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?

One dense sentence that front-loads the verb and object, states what is inspected, and situates the call in its workflow. There is no filler and no repetition of schema information.

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 read-only metadata-listing tool, the description, full parameter schema, output schema, and annotations together cover workflow context, safety, and expected inputs. The phrase 'exact metadata-discovered table before constructing ADQL' supplies the important contextual cue.

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?

The input schema has 100% description coverage, so all three parameters are already documented. The description reinforces that 'table' must be the exact name from prior metadata inspection, but it adds no new syntax or format details beyond what the schema provides.

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 names a specific verb (Inspect) and resource (columns for a table), lists the returned facets (names, datatypes, units, descriptions), and ties the action to a workflow stage (before constructing ADQL). This clearly separates it from sibling tools like starfetch_tap_tables, which list tables rather than one table's columns.

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?

It explicitly says when to use it: after metadata discovery, before constructing ADQL, on an exact table name. It does not explicitly enumerate alternatives or state when not to use it, but the workflow context is clear enough to guide tool selection among siblings.

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