List Core Lmi Datasets

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds real value beyond that with the account-scope constraint and the non-obvious pagination behavior ('metadata pagination is local after one upstream fetch'), which changes how an agent should loop over results.

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 with the core purpose front-loaded and no filler. The pagination caveat is appended efficiently rather than padded, though the phrasing is dense enough that the parameter mapping is easy to miss.

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?

With an output schema present and annotations covering the safety profile, the description does not need to explain return values. It covers the essential extras an agent needs — account scoping and pagination semantics — and only misses match semantics for the query filter (substring vs exact) and explicit limit/offset meaning.

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 0%, so the description must carry parameter meaning. It does explain that description inclusion is optional ('include descriptions') and that filtering is by 'name/text', mapping loosely to include_definitions and query, but limit and offset are never clarified (only vaguely implied by the pagination sentence), leaving half the parameters to guesswork.

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 concrete verb and resource ('Discover Core LMI datasets and releases') and bounds scope to the caller's account ('agnitio scope'), which lets an agent tell it apart from query and metadata siblings. It stops short of naming the specific sibling to use for closely related needs (e.g. get_core_lmi_metadata or get_core_lmi_versions), so differentiation is partial.

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 'Discover' framing implies this is the entry-point listing call and the filtering note hints at search use, but there is no explicit when-to-use or when-not-to-use guidance and no named alternative such as query_core_lmi for actual data retrieval. Usage must be inferred from the sibling names outward.

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