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

Annotations already convey readOnlyHint=true and idempotentHint=true, so the safe, non-mutating nature is covered. The description adds useful context that the tables are staged by paleobiology_search_occurrences and that this is a discovery helper, but it does not reveal additional behavioral details beyond that.

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 tight sentences: the first states the core function and provenance, the second gives actionable next-step guidance. No filler or redundant restatement of the schema or annotations.

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 a single parameter, a descriptive schema, an output schema, and strong annotations, the description fully covers what an agent needs: what is listed, where it comes from, and when to invoke it. Return-value details are already present in the output 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% and the canvas_id parameter is already well-described as the id returned by paleobiology_search_occurrences when results spill. The main description does not need to add parameter-level meaning, so the baseline of 3 applies.

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 specific verb ('List') and resource ('tables and their columns staged on a DataCanvas'), and it explicitly ties the data to paleobiology_search_occurrences. It also distinguishes itself from paleobiology_dataframe_query by framing itself as the discovery step before SQL queries.

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 explicitly instructs the agent to call this before paleobiology_dataframe_query to discover table and column names, giving clear contextual usage. It does not include when-not-to-use exclusions, but for a single-sibling workflow this is sufficient.

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