find_training_datasets

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It explains the input and output but does not mention side effects, rate limits, authentication needs, or whether the tool is a read-only operation. Basic behavioral transparency.

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 concisely structured with a clear first sentence, followed by a well-organized parameter list and return type. Every section serves a purpose without unnecessary words, making it efficient for an agent to parse.

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 5 parameters and a specified output schema (list[dict]), the description covers all inputs and return format. However, it lacks details on the ranking or evaluation criteria, which would be helpful for understanding tool behavior. Since an output schema exists, the return type is adequately described.

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 description provides detailed parameter explanations beyond the schema, including examples for 'license_filter' (e.g., 'permissive (MIT, Apache, CC-BY, CC0)') and clarifies that 'modality' and size filters are optional. This adds meaning beyond the schema's type definitions, even though schema description coverage is 0%.

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 verb 'Find' and resource 'datasets' with a specific purpose 'suitable for training ML models'. It distinguishes from generic search tools like 'search_records' by specifying the ML training context, making the purpose unambiguous.

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 implies usage for finding ML training datasets but does not explicitly state when to use this tool versus siblings like 'recommend_dataset' or 'search_records'. No exclusions or alternatives are mentioned, leaving the agent to infer context.

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