ScorpTech x402 API

Html Tables

html_tables

Read-onlyIdempotent

Extract HTML tables from a public URL (SSRF-safe fetch) or an HTML string into clean JSON: header detection (thead or leading th rows, multi-row headers joined), rowspan / colspan expansion, captions, rows as objects keyed by header or as arrays, optional number parsing ("$1,234.50", "45%"), and table choice by index or CSS selector (e.g. table.wikitable). Layout tables with too few rows are skipped. Price: $0.003 USDC per successful call (x402, Solana). Same tool over HTTPS: POST https://scorptech.it.com/api/x402/v1/html-tables

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": "html_tables"
  }
}

Input Schema

Name	Required	Description	Default
`url`	No	Public http(s) page (or use html)
`html`	No	HTML source instead of url
`index`	No	Only the table at this 0-based position (after selector)
`format`	No		objects
`header`	No	auto = thead / all-th rows; first = first row is the header; none = no header	auto
`maxRows`	No	Rows returned per table
`minRows`	No	Skip tables with fewer data rows
`numbers`	No	Convert numeric-looking cells to numbers
`selector`	No	CSS selector choosing tables, e.g. table.wikitable or #results table
`maxTables`	No

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 23, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare read-only, idempotent, open-world, non-destructive behavior, but the description adds substantial context beyond them: SSRF-safe fetching, header detection rules, rowspan/colspan expansion, layout-table skipping, number parsing, and x402 pricing. It also discloses the HTTPS endpoint and payment model, which an agent needs before calling.

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 dense but front-loaded, leading with the core extract action and only then layering in behavior, examples, and pricing. Every clause adds information, though the long run-on enumeration could be more 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?

For a 10-parameter extraction tool with no output schema, the description is complete enough to call correctly: it explains inputs, output shape (clean JSON, objects or arrays), header handling, table selection, numeric conversion, table filtering, SSRF safety, and payment. Nothing an agent needs in order to use the tool correctly appears to be missing.

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 80%, so most parameters are already documented. The description still adds meaningful semantics for several parameters: examples of number parsing ('$1,234.50', '45%'), selector examples ('table.wikitable'), header detection behavior, and objects-vs-arrays formatting. It does not explain maxRows, minRows, or maxTables explicitly, but the added examples compensate for the fully documented parameters.

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?

States a specific verb and resource (extract HTML tables) plus the two input modes (public URL or HTML string) and the output format (clean JSON). It is immediately distinguishable from siblings like html_select or html_to_markdown because it is explicitly table-focused.

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

Usage Guidelines4/5

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

Gives clear context for when the tool applies: a public URL or an HTML string, with table choice by index or CSS selector. It does not explicitly name alternatives such as html_select or web_to_text, so the when-not guidance is absent, but the operational context is clear.

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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