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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive behavior, so the safety profile is covered. The description adds substantive behavioral context beyond that: the three aggregation dimensions, concrete examples of outputs (Transit vs Radial Velocity vs Imaging counts, Kepler/TESS/Keck rankings), and attribution to the NASA Exoplanet Archive. 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?

Two sentences with zero waste. The first sentence front-loads the core function, and the second earns its place by clarifying all three group_by options with concrete examples. No redundant or filler content.

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 tool with two optional parameters (both fully schema-documented), a comprehensive annotation set, and an output schema covering return values, the description covers everything needed for correct invocation: aggregation dimensions, what each dimension reveals, the time range, and the data source. Nothing material is missing.

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 100%, so both parameters are fully documented mechanically, including defaults and bounds. The description adds genuine semantic enrichment by giving concrete examples of what each enum value yields (Transit vs Radial Velocity vs Imaging, annual trends since 1988, observatory rankings), which helps an agent pick a group_by value beyond the schema's terse wording.

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+resource ('Get discovery statistics for confirmed exoplanets aggregated by method, year, or facility'), which immediately distinguishes it from the sibling tools (planets.detail, planets.habitable, planets.search) that return individual planet records rather than aggregates. The tool's aggregation role is unambiguous and the data source is named.

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 gives clear context for when to call this tool by explaining what each group_by mode produces (method counts, annual trends since 1988, facility rankings). However, it does not explicitly name alternatives or state when NOT to use it (e.g., 'for individual planet records use planets.detail/search'), leaving exclusion guidance implicit.

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