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

Annotations already declare it readOnly, idempotent, non-destructive and closed-world, so the safety burden is covered. The description adds behavioral value by disclosing the returned facets (coverage, pricing model, live API sources) and the downstream integration with query_data.

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 sentences, fully front-loaded with the resource and its contents, and zero redundant text. Nothing is wasted.

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?

No output schema exists, so the description carries return-value burden, and it does summarize the returned fields reasonably well. It stops short of describing pagination or result limits, leaving a minor gap for a browse-style tool.

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?

With a single parameter at 100% schema description coverage, the schema already documents 'q' (word filter on title/description). The description adds no further parameter semantics, so the baseline 3 applies.

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 ('Lists') and resource ('datasets contributed by independent publishers') and enumerates what is included (indicators, coverage, pricing model, live API sources). It is distinguishable from generic search/data tools, though it does not explicitly contrast itself with siblings like find_indicators or search_data.

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?

Implies usage by noting the indicators 'can be used in query_data like any other', which hints at the workflow, but there is no explicit when-to-use/when-not guidance or named alternative (e.g., vs find_indicators or search_data).

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