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Dataset finder

dataset_finder

Find open research datasets you are allowed to use: searches Zenodo (700k+ records) and gives each dataset a plain licence verdict read from the depositor's structured licence. $0.004 per call over x402 (USDC on Arc, Base or Solana, or XNT on X1). Call once without payment for the terms, sign them with your own wallet, call again with payment.

Input Schema

NameRequiredDescriptionDefault
qYeswhat you are looking for, e.g. "electricity prices"
paymentNoa signed x402 payment (the base64 payload you would put in PAYMENT-SIGNATURE). Pass it and the purchase completes inside this tool call.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

There are no annotations, so the description carries the full behavioral burden. It discloses the paid nature ($0.004 per call), accepted payment assets and chains, the two-step x402 flow, and that results include licence verdicts read from structured depositor data. It does not cover pagination, rate limits, or error behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loads the core purpose before moving to cost and payment mechanics. Each sentence carries useful information, though the dense paragraph could be easier to scan if the payment workflow were separated from the search description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter paid search tool with no output schema, the definition covers purpose, source, result type, cost, and payment invocation. It lacks detail on result pagination or empty-result behavior, but the essentials for calling it correctly are present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already defines both parameters. The description still adds meaningful context by explaining that `payment` is a signed x402 payload and that omitting it returns payment terms, which clarifies the two-step invocation beyond the schema's wording.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: it searches Zenodo's 700k+ records for open research datasets and returns a plain licence verdict. This is clearer than a generic search tool. However, it never names the closely related sibling find_open_datasets or explains how this tool differs from it.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives detailed operational guidance for payment: call once without `payment` for terms, sign them, then call again with `payment`. It does not say when to choose this tool over alternatives such as find_open_datasets or research_search, so usage selection is left implied.

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

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