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AI Web Scraper — No-Code, Plain-English Extraction

ai_web_extractor
Read-only

A no-code AI web scraper: give a URL and a plain-English instruction, get clean…. A no-code AI web scraper: give a URL and a plain-English instruction, get clean structured JSON. No CSS selectors. For people and AI agents.

Input Schema

NameRequiredDescriptionDefault
inputNoInput for AI Web Scraper — No-Code, Plain-English Extraction. See https://apify.com/noniner/ai-web-extractor.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / input / description
      Previous value: -"Input for AI Web Extractor. See https://apify.com/noniner/ai-web-extractor."New value: +"Input for AI Web Scraper — No-Code, Plain-English Extraction. See https://apify.com/noniner/ai-web-extractor."
  2. First observed

TDQS

C2.9/5.0
Behavior3/5

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

The readOnlyHint and openWorldHint annotations already establish that this is a safe, open-web read operation, and the description adds useful context that extraction is instruction-based and returns structured JSON. It does not disclose limitations such as JavaScript-heavy pages, authentication, rate limits, or failure behavior, so it is only moderately transparent beyond the annotations.

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

Conciseness2/5

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

The content is short, but the description contains an apparent truncation and duplication: 'get clean…. A no-code AI web scraper...' repeated. This makes the structure sloppy and reduces clarity even though the core message is front-loaded.

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

Completeness2/5

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

For an open-world extraction tool with a loosely defined nested input object and no output schema, the description is not complete enough. It says the input is a URL plus an instruction and the output is structured JSON, but it omits input field structure, output shape, and practical scraping constraints.

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?

Schema description coverage is technically high, so the baseline is 3, but the only parameter is a nested object whose schema description merely points to an external URL. The description partially compensates by saying the input should include a URL and a plain-English instruction, but it does not specify field names or the expected nested structure.

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 clear verb and resource: give a URL plus a plain-English instruction and get structured JSON. The phrase 'No CSS selectors' usefully distinguishes it from selector-based scraping, but it does not explicitly differentiate it from nearby siblings like website_to_markdown or product_catalog_extractor.

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

Usage Guidelines2/5

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

The description implies usage by saying 'give a URL and a plain-English instruction,' but it gives no explicit when-to-use, when-not-to-use, or alternative-tool guidance. With many sibling extraction tools, an agent has to infer whether this is the right choice.

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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