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GeorgesAdSim

Fetch Crawl MCP

by GeorgesAdSim

Extract With Schema

extract_with_schema
Read-only

Extract structured data from web pages using CSS selectors, custom schemas, or presets for products, articles, local businesses, and recipes. Fallback selectors ensure reliable extraction.

Instructions

Extract structured data from a web page using configurable CSS selectors. Supports custom schemas and built-in presets (ecommerce-product, article, local-business, recipe). Each field defines a CSS selector, optional attribute, multiple flag, and transform (text, html, number, trim, href). Fallback selectors can be provided for resilience.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL to extract data from
presetNoBuilt-in preset schema. If both preset and schema are provided, schema overrides preset fields
schemaNoExtraction schema: keys are field names, values define selector/attribute/multiple/transform
fallbackSelectorsNoFallback selectors used when primary selector finds nothing

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv4.2.0

TDQS

A3.9/5.0
Behavior3/5

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

The readOnlyHint annotation already establishes that this tool is safe and non-destructive. The description adds useful behavioral context about per-field transforms and fallback selectors, but it does not disclose the output format, error behavior, or whether dynamic/JavaScript-rendered pages are supported.

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

Conciseness5/5

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

Three dense, well-ordered sentences front-load the core purpose, then summarize presets and field configuration without redundancy. There is no filler and every sentence contributes useful information.

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

Completeness3/5

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

The input side is well covered, including nested schema objects and fallback selectors, but there is no output schema and the description does not state the shape of the returned data or behavior on empty/missing selectors. For a tool with this complexity, that is a notable gap.

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 100%, so all parameters are already documented in the structured schema. The description restates the field-level concepts and lists transform values, but it adds no additional meaning beyond what the schema already provides.

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 states a specific verb and resource: 'Extract structured data from a web page using configurable CSS selectors.' It clearly distinguishes itself from generic siblings like extract_content and extract_links by emphasizing custom schemas, built-in presets, and field-level selector configuration.

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 conveys a clear usage context: use this tool when you need structured, schema-driven extraction from a webpage with custom or preset selectors. It doesn't explicitly name alternatives or state when not to use it, but the intended use case is clear enough for an agent to route appropriately among siblings.

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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