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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds context about the domain and return fields, but does not provide additional behavioral traits beyond what annotations already convey, so it does not significantly raise the transparency bar.

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 two sentences, front-loaded with the core purpose, and contains only useful details like domain scope, returned fields, and example queries. There is no fluff or redundancy.

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 simple tool with full schema coverage, rich annotations, and an output schema, the description adequately covers what the tool does, what it returns, and when to use it. It is complete enough for an agent to select and invoke the tool 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 descriptions cover 100% of the parameters, so the schema already documents from, size, and query. The description's example queries add no semantics beyond the schema's own examples, so it does not meaningfully compensate or augment parameter understanding.

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 clearly states a specific verb and resource: full-text search of PANGAEA earth/environmental science datasets, and lists the returned fields. It is specific and distinct from a generic search, but it does not explicitly distinguish itself from sibling tools like search_within or facets.

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 description provides concrete examples of when to use the tool ('Use for questions like "ocean temperature datasets"...') and defines the domain scope. It gives clear context for usage but does not mention exclusions or alternatives relative to sibling tools.

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