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JavaScript
Remote

scrape_structured

Read-onlyIdempotent

Extract structured data from web pages using CSS selectors. Specify row selectors to align records for reliable, consistent scraping of well-structured sites.

Instructions

Use this when you know the exact CSS selectors for the data you want - e.g. a pricing table or product list with consistent markup. More reliable than LLM extraction for well-structured pages. By default each selector is matched independently across the whole page, so the returned arrays are NOT row-aligned: data.price[0] need not belong to the same row as data.name[0]. Pass row_selector to get aligned records instead - one object per row, null for a field the row lacks. Not for pages whose markup varies or where you cannot name the selectors (extract_structured, LLM-driven). Cost: 2 credits. Example: scrape_structured({url: "https://shop.com/products", row_selector: ".product-card", selectors: {price: ".price", name: ".product-title"}})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL to scrape
selectorsYesCSS selectors mapping field names to selectors. Append @attr to extract an attribute instead of text (e.g. "a.link@href", "img@src")
user_agentNoOverride the outbound User-Agent. CrawlForge identifies itself honestly by default; use this only for targets you have your own agreement with.
max_resultsNoMaximum number of matches to return per field when a selector matches multiple elements, or the maximum number of rows when row_selector is set
row_selectorNoCSS selector for the repeating row/container element. When set, each field in selectors is matched inside each row and data is an array of row-aligned records ({field: value|null}) instead of parallel arrays
respect_robotsNoRespect the target site's robots.txt (default: true). Setting this to false is honoured, returns a warning in the response, and is recorded against your API key — it is your decision, not a silent default.

Schema Changelog

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

  1. Changed3 schema fields changedv6.0.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / max_results / maximum
      Added value: +9007199254740991
    • addedInput schema / properties / selectors / propertyNames
      Added value: +{
+  "type": "string"
+}
  2. Changed4 schema fields changedv5.4.0
    • changedInput schema / properties / max_results / description
      Previous value: -"Maximum number of matches to return per field when a selector matches multiple elements"New value: +"Maximum number of matches to return per field when a selector matches multiple elements, or the maximum number of rows when row_selector is set"
    • addedInput schema / properties / respect_robots
      Added value: +{
+  "description": "Respect the target site's robots.txt (default: true). Setting this to false is honoured, returns a warning in the response, and is recorded against your API key — it is your decision, not a silent default.",
+  "type": "boolean"
+}
    • addedInput schema / properties / row_selector
      Added value: +{
+  "description": "CSS selector for the repeating row/container element. When set, each field in selectors is matched inside each row and data is an array of row-aligned records ({field: value|null}) instead of parallel arrays",
+  "type": "string"
+}
    • addedInput schema / properties / user_agent
      Added value: +{
+  "description": "Override the outbound User-Agent. CrawlForge identifies itself honestly by default; use this only for targets you have your own agreement with.",
+  "type": "string"
+}
  3. Changed1 schema field changedv5.0.4
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  4. First observedv4.10.0

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, it discloses the critical row-alignment gotcha (data.price[0] need not belong to the same row as data.name[0]), the cost of 2 credits, and the meaning of row_selector. This is exactly the kind of non-obvious behavior an agent needs before invoking.

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?

The description leads with the decision rule, then covers exclusions, the key behavioral warning, cost, and an example with no filler. Every sentence contributes necessary operational information.

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?

Despite having no output schema, the description explains the shape of returned data (parallel arrays vs row-aligned objects) and gives enough context for an agent to invoke correctly. Together with the rich input schema, this is complete for a read-only extraction tool.

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 schema already covers all 6 parameters in detail, so the baseline is 3. The description adds value with a concrete usage example and clarifies how selectors and row_selector work together, but it does not substantially redefine the parameter meanings beyond the schema descriptions.

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 names a specific verb and resource (scrape structured data with exact CSS selectors) and differentiates it from LLM-driven extraction. It is immediately clear this tool is for well-structured pages with known markup.

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

Usage Guidelines5/5

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

It gives an explicit when-to-use condition (you know exact selectors, consistent markup) and states what it is not for (varying markup or unnamed selectors), pointing to extract_structured / LLM-driven extraction as the alternative. This lets an agent select correctly among many 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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