get_available_datasets

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

With no annotations, the description carries full burden. It mentions 'comprehensive information' but does not specify what that includes (e.g., dataset names, descriptions, metadata). The include_sample parameter hints at sample data but behavior is not fully disclosed (e.g., data volume, pagination, response format).

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 short sentence, which is functional but could be more informative. It is not excessively verbose but lacks some useful context. It is adequately concise but not exemplary.

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?

Given the simplicity (1 optional param, no output schema), the description is minimal. It does not explain what datasets are, what information is returned, or how it fits with sibling tools. More context about data discovery workflow would improve completeness.

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 100% with the boolean parameter already well-described. The description does not add significant extra meaning beyond the schema's 'Include sample data for each dataset'. The baseline is 3, and no additional value is provided.

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 'Get comprehensive information about all available PopHIVE datasets', specifying the verb (Get), resource (available datasets), and scope (all). It distinguishes well from sibling tools like compare_states and filter_data, which are more specific operations.

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?

No guidance is provided on when to use this tool versus alternatives. It does not mention that this tool should be used first to discover available datasets before applying filters or analyses. The description lacks any usage context or exclusion conditions.

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