recommend_dataset

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

Without annotations, the description carries the full burden. It explains the tool finds datasets, processes metadata, and scores on completeness, popularity, and documentation. However, it does not mention whether the tool is read-only, any destructive actions, or behavioral details like result ordering or error handling.

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 concise and well-structured, with a clear first sentence stating the purpose, a brief explanation of functionality, and a neatly formatted parameter list. Every sentence adds value without 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?

The description is fairly complete for a recommendation tool with three simple parameters and an output schema. It mentions the return format (list of dict with matching reasons). However, it lacks edge-case handling or usage context relative to siblings.

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?

The input schema has 0% description coverage, so the description compensates well by explaining each parameter: query as a natural language prompt with example, page as result page, size as number of recommendations. This adds significant meaning beyond the schema alone.

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 the tool's purpose: providing ranked dataset recommendations based on a natural language request. It identifies the resource (datasets) and the action (recommend). While it is clear, it does not explicitly differentiate from siblings like find_training_datasets or search_records, missing a full 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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or comparison with sibling tools, leaving the agent to infer usage context.

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