discover_datasets

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

With no annotations, the description carries the full burden. It clearly describes the behavioral output: list datasets and return schema details including sample values and column categories. The verbs 'list' and 'get' suggest a read-only operation, so no mutation traits need to be disclosed. It doesn't mention any limitations (e.g., num of datasets), but for a simple discovery tool this is adequate.

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 a single, well-structured sentence with the action verb frontloaded ('List') followed by a colon and a precise bullet-like list of outputs. It contains no filler or repetition and every item adds useful detail.

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 no parameters and no output schema, so the description must fully explain what an agent will get from invoking it. It does so specifically: column names, dtypes, sample values, and categorised column list. There is nothing critical missing for correct invocation and interpretation of results.

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 0 parameters, so there is no parameter space for the description to clarify. The baseline for parameterless tools is 4, and the description appropriately focuses on behavior rather than param references, which are absent.

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 uses a specific verb ('List') and a clear resource ('available datasets'), then enumerates exactly what is returned (schemas, column names, dtypes, sample values, categorised columns). This cleanly distinguishes it from the sibling chart/report/summary tools, which are all about producing outputs from datasets rather than discovering them.

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 usage context is implied rather than explicit: it's a discovery/inspection tool, and the sibling names make it easy to infer when to use it (before charting or summarising). However, the description does not state when to use it over alternatives, nor mention that it is a prerequisite for the other tools.

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