list_columns

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

Annotations already declare the operation as read-only, idempotent, and non-destructive. The description adds valuable behavioral context, noting that the main planet table has 300+ columns and suggesting the category filter to narrow results, which implies a potentially large output and gives the agent guidance on managing it. This goes beyond the bare 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?

The description is well-structured, leading with the core action, then usage guidance, then supplementary context about the large table and when the tool is unnecessary. It is slightly long but every sentence adds relevant information; no waste.

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?

Given the presence of an output schema and clear sibling differentiators, the description covers all necessary aspects: what the tool lists, when to use it, when not to, and how to handle its potentially large result set. It also properly references the default table and lists the category options implicitly via the schema.

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 100%; both parameters (table and category) already have detailed descriptions including examples and defaults. The tool description adds a rationale for using the category filter ('to narrow results to a manageable set') but does not meaningfully enhance parameter semantics beyond the schema.

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 opens with a specific action and object: 'List available columns for a table in the NASA Exoplanet Archive, with data types, descriptions, and units.' This clearly distinguishes the tool from siblings like search_planets (which searches data) and list_tables (which lists tables). The purpose is further reinforced by 'discover what data fields exist' and its relation to search_planets/run_query.

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?

The description provides explicit guidance on when to use the tool: 'Use this to discover what data fields exist before selecting specific columns in search_planets or writing ADQL queries with run_query.' It also states when it is not needed: 'You do not need this for basic queries — search_planets returns a useful default column set.' This directly addresses usage versus alternatives.

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