dataset_finder

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

No annotations are provided, so the description carries the full burden, and it does disclose the cost ($0.004/call), the accepted payment rails (USDC on Arc/Base/Solana, XNT on X1), the x402 handshake, and where the licence verdict comes from. It still omits failure/refund behavior, rate limits, and whether unlicensed datasets are returned or filtered.

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?

Front-loaded with what the tool does and its differentiator (licence verdict), then the payment mechanics. Dense but no filler; the cost and rail list is arguably the least essential clause but still useful.

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?

With no output schema and no annotations, the description must cover the return shape, and it only says each dataset gets a licence verdict. It does not describe what fields come back per result, result count, sorting, or pagination, which is a notable gap for a search tool.

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 only two params, so the baseline is 3, but the description adds meaningful semantics beyond the schema: `payment` is explained as part of a two-call flow where the first call returns terms to sign, which the schema description alone does not convey.

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?

States a specific verb+resource (find open research datasets) and names the concrete source (Zenodo, 700k+ records) plus the distinguishing output (a plain licence verdict read from the depositor's structured licence). It is clear on its own, but it does not differentiate itself from near-name siblings like find_open_datasets or research_search, which an agent would also consider.

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?

Gives strong procedural guidance for invocation (call once without `payment` to get terms, sign with your own wallet, call again with `payment`), which is real usage information. However, it offers no when-to-use/when-not guidance relative to alternatives such as find_open_datasets or research_search, so the selection decision is left to inference.

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