list_datasets

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

Annotations already establish read-only and open-world behavior. The description adds useful domain behavior: each dataset has an id, period granularity, tables, and SQL views named `<dataset>__<table>`, which goes beyond the annotations without contradicting them.

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 front-loaded, compact, and every sentence contributes: it states the catalog purpose, explains the entry-point role, and defines the table/view naming convention. There is no redundant wording.

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?

The tool has an output schema, complete input-schema descriptions, and annotations covering safety behavior. The description supplies the remaining conceptual context an agent needs: what a dataset contains and how table identifiers relate to sibling tools and SQL views.

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%, so the schema fully documents `detail` and `domain`, including domain values. The description does not add parameter-specific meaning beyond that, so baseline 3 is appropriate.

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 and resource: list every CVM dataset this server understands. It also frames the tool as the entry point and clarifies that table ids feed describe_dataset and run_query, which distinguishes it from those sibling tools.

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 phrase 'This is the entry point' gives clear usage context, and the mention that table ids are used by describe_dataset and run_query implies a workflow. It stops short of explicit when-not-to-use guidance or naming alternatives, so it is strong but not exhaustive.

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