Find datasets by tag

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower. The description still adds real behavioral context beyond them: the server never reads data, and results are scoped to 'only domains this caller may read' – a permission constraint not visible in the 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?

Front-loaded with the core question, and each sentence adds scope or a boundary. The fragmentary 'Answers which datasets hold a kind of data...' slightly restates the opening line, costing a point.

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?

An output schema exists, so return values need no prose. Between the purpose, the negative scope (no row-level data), and the permission scoping, an agent has everything needed to decide and call 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?

Schema coverage is 0% and the single parameter is a nested string map, so the description must carry the load. The example 'subject_type=customer' usefully illustrates the key=value shape, but it does not explain multi-tag semantics (AND vs OR, whether all tags must match).

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 ('which datasets carry these tag values') and clarifies the answer shape ('which datasets hold a kind of data, and in which columns'). It does not name any sibling (list_domains, get_dataset, resolve_lf_tag) to differentiate, so it falls short of a 5.

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?

Clear usage context is given: use it to map tag values to datasets/columns, and it explicitly excludes row-level queries ('it cannot say where one person's rows are'). However, no alternative tool is named for cases the agent might actually want, leaving routing to inference.

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