discover_datasets_v1_discover_get

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

With no annotations provided, the description carries full burden. It discloses that authentication is required, uses hybrid search with relevance sorting, has a smaller default limit (5), and has no pagination. It does not mention rate limits or potential side effects, but for a read-only discovery tool, these are less critical. Overall, it provides adequate behavioral expectations.

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. It opens with the general purpose, then provides a bulleted list of key differences. Every sentence adds value, and there is no redundancy or unnecessary detail.

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 tool has 7 parameters and no output schema, the description gives a good overview of what is returned (enriched metadata including columns, canonical questions, methodology, quality scores). It does not detail the exact response structure, but for a discovery tool, this level of completeness is sufficient. The lack of output schema is compensated by the description's summary.

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?

All 7 parameters have descriptions in the input schema (100% coverage), so the schema already defines the parameters. The description adds overall context (e.g., default limit of 5) but does not elaborate on individual parameter semantics beyond what the schema provides. Thus, a baseline score of 3 is appropriate.

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: 'Discover datasets optimized for LLM agents' and specifies the enriched metadata returned (columns, canonical questions, methodology, quality scores). It also explicitly distinguishes itself from the sibling tool search_datasets_v1_search_get, making its unique role evident.

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 explicit guidance on when to use this tool vs alternatives, including a bulleted list of key differences from /search, notes on authentication requirements, and a directive to 'use /search for browsing' when pagination is needed. This gives clear context for tool selection.

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