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Structured Data Extractor

structured-extract

Read-only

Turn any URL into clean structured JSON — title, description, image, JSON-LD, headings, links, emails and prices — extracted deterministically via regex. Zero LLM calls, zero API keys. Built for AI agents that need one page turned into typed data, cheaply. — $0.01/call, x402 (USDC on base).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "urls": "<urls>"
    },
    "name": "structured-extract"
  }
}

Input Schema

Name	Required	Description
`urls`	Yes	List of URLs to extract structured data from. One row per URL.
`fields`	No	Optional subset of fields to return: title, description, image, siteName, canonical, jsonLd, headings, links, emails, prices. Leave empty to extract all of them.
`maxConcurrency`	No	How many URLs to process in parallel.

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 28, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already mark the tool as read-only and non-destructive. The description adds valuable behavioral context: extraction uses regex deterministically, makes zero LLM calls, requires no API keys, and incurs a fixed cost of $0.01/call via x402. This enriches the safety profile with cost and methodology details.

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 in the first clause, with supporting details on cost and method in subsequent clauses. It is slightly promotional but every piece of information (fields, regex, zero LLM, cost) is actionable.

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?

The description covers what fields are extracted, the extraction method, the cost model, and the target user. It does not elaborate on error handling or output structure beyond 'clean structured JSON', but given the tool's simplicity and the schema's parameter documentation, it is sufficiently complete for decision-making.

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

Parameters3/5

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

The schema provides complete descriptions for all three parameters, including the valid field names and concurrency range. The description adds no parameter-specific semantics beyond the schema, so the baseline of 3 applies.

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 'Turn any URL into clean structured JSON' and enumerates exact extracted fields (title, description, image, JSON-LD, headings, links, emails, prices). It distinguishes from siblings by noting deterministic regex extraction with zero LLM calls, which differentiates it from similar URL-processing tools.

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?

The description clearly states the use case ('Built for AI agents that need one page turned into typed data, cheaply') and highlights cost benefits ($0.01/call, zero LLM calls). However, it does not explicitly name alternative tools or state when not to use it, so it stops short of a 5.

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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