data_list

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

Annotations already declare the tool read-only, idempotent, and non-destructive, so the description responsibly adds behavioral context: it lists what is returned and explains the empty-list meaning. This goes beyond re-stating annotations without overclaiming.

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 core definition and usage guidance are front-loaded in the first two sentences. The keyword list is slightly redundant but can aid retrieval and does not make the description bloated.

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 zero-parameter list tool, the description covers purpose, output fields, when to use it, empty-result behavior, and the next action to take. It is complete enough for an agent to select and invoke correctly.

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 parametersional signature, so there is nothing for the description to explain. Baseline for a no-parameter tool is 4, and the description adds no conflicting parameter claims.

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?

Description states a specific verb ('List'), resource ('registered datasets'), and the exact output fields (name, kind, row_count, column names). This clearly distinguishes it from sibling query, register, and describe 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?

Explicitly says to call this first when the user mentions a table/file and the dataset name is unknown. It also gives a concrete alternative: if empty, use data_register with the path, and instructs querying by `name` in SQL.

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