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

With no annotations, the description carries the transparency burden. It discloses that the tool lists available datasets and notes the current state of not_ingested ('currently empty'). However, it does not explicitly state that the tool is read-only or side-effect-free, nor describe the return format. For a catalog tool, this is adequate but not rich.

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 one dense catalog sentence followed by a short usage directive, packing extensive category detail without filler. The em-dash list structure keeps the long enumeration scannable, and the actionable 'Call FIRST' instruction is clear. It is slightly long, but appropriate for listing 39 datasets.

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 zero-parameter discovery tool with no output schema, the description covers the essential ground: it names the dataset categories, ties them to the research-dataset reader, and instructs when to invoke the tool. It does not describe the exact return shape or explain how slugs map to tengu_v2_research_read, which would strengthen completeness. Overall it is adequate for its simple interface.

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 tool takes zero parameters, so the description cannot add parameter-level detail; the baseline for 0 parameters is 4. The mention of 'slug' and not_ingested adds useful context about how dataset identifiers are resolved and how to determine availability. No contradiction with the empty schema.

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 uses the specific verb 'Discover' and names the exact resource: the 39 licensed research datasets servable via the research-dataset reader. It enumerates categories, making it unmistakably a catalog/discovery tool rather than a data-read tool. The closing instruction 'Call FIRST when unsure of a slug' further distinguishes it from sibling tools like tengu_v2_research_read.

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?

It explicitly tells agents to 'Call FIRST when unsure of a slug,' giving a clear trigger condition for when to use this tool. It also mentions the not_ingested feature for checking pending slugs. It does not explicitly name alternative tools or state when not to use it, but for a zero-parameter discovery tool, the guidance is practical and sufficient.

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