list_tables

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

Beyond the annotations (readOnly, idempotent, not destructive), the description adds critical behavioral context: it returns a curated list of the most important tables, not all 40+ tables, and explains how to get the full list via run_query. It also clarifies the tool's relationship to other tools and the nature of the data returned, which is valuable beyond static hints.

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 with a clear opening, usage guidance, and important caveats. Each sentence provides distinct value—no fluff or repetition. The length is justified given the need to explain the curated nature and the distinction from sibling tools.

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?

With an output schema present and no parameters, the description fully covers what an agent needs to know: what the tool returns, how to use it, its limitations, and how to access the full table list. It also provides specific examples of common tables, making it complete for practical use.

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 tool has zero parameters, so there is nothing for the description to explain. Per the baseline rule for 0-param tools, a score of 4 is appropriate. The description does not need to add parameter semantics, and it doesn't.

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 clearly states the tool's purpose with a specific verb and resource: 'List all available tables in the NASA Exoplanet Archive with descriptions.' It also enumerates the return contents (names, descriptions, approximate row counts, guidance), making it immediately obvious what the tool does. It distinguishes itself from siblings by explicitly noting when search_planets or run_query are more appropriate.

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 when-to-use guidance: 'Use this to discover what data is available before writing queries with run_query.' It also states when NOT to use it ('You do not need this tool for basic planet searches') and offers an alternative for discovering all tables via run_query with a specific query. This is textbook usage guidance.

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