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Scrapling x402 Data Store

by io.github.bnth09productions-maker
OverviewDiscussionsAdmin

List datasets

list_datasets
Read-only

FREE. Returns every dataset for sale: id, title, category (crypto/DeFi token prices, volume, TVL and yields; e-commerce product prices and stock; custom scraped feeds), row count, field names, created_at (freshness), a few sample rows and the price in USDC (starter sets from $0.01, up to 100 rows $0.05, large sets max $4.99). JSON output.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_datasets"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already state readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful context beyond annotations: it declares JSON output, that the call is FREE, the pricing tiers, and what data categories and sample rows appear in the result.

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 front-loaded with the key fact ('FREE') and the operation's purpose, and every clause adds useful catalog detail. It is dense but not padded, though the long parenthetical enumeration makes it slightly less scannable.

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

Completeness5/5

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

With no parameters, no output schema, and simple read-only annotations, the description must carry the return-value context itself. It does so thoroughly by listing the returned fields, data categories, pricing structure, and output format.

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?

The tool has zero input parameters, so the baseline is 4. The description does not need to explain parameter semantics, and it correctly does not attempt to document nonexistent inputs.

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

Purpose5/5

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

The description uses a specific verb ('Returns') and resource ('every dataset for sale'), then enumerates the exact fields and categories returned. It implicitly distinguishes itself from buy_dataset and preview_dataset by being the free catalog listing of all datasets.

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?

Usage is implied: readers can infer this is the browse-before-buy tool because it is marked FREE and returns prices and sample rows. However, it never explicitly says when to use it versus buy_dataset or preview_dataset, and it provides no exclusions or prerequisites.

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

All Scrapling x402 Data Store toolsInspect
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