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extract_structured_data

Read-only

Extract structured data from web pages using CSS selectors, LLM, or table extraction. Optionally save the full extraction to disk as JSON.

Instructions

Extract structured data using CSS selectors or LLM. Use output_path to persist the full extraction (including table_data) to disk as JSON and receive a slim response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesTarget URL
extraction_typeNo'css'|'llm'|'table'css
css_selectorsNoCSS selector mapping
extraction_schemaNoSchema definition
generate_markdownNoGenerate markdown
wait_for_jsNoWait for JavaScript
timeoutNoTimeout in seconds
use_llm_table_extractionNoUse LLM table extraction
table_chunking_strategyNo'intelligent'|'fixed'|'semantic'intelligent
output_pathNoAbsolute file path (auto .json extension) to persist the full extracted_data + table_data as JSON. When set, the response is slimmed (content, markdown, table_data, extracted_data.raw_content removed).
include_content_in_responseNoWhen True (with output_path set), also keep extracted_data/table_data/content in the response. Defaults to False.
overwriteNoOverwrite an existing output file at output_path. Defaults to False.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Annotations indicate readOnlyHint=true, which matches the extraction purpose. The description adds useful behavioral context about output_path persisting full data and slimming the response, but does not disclose other behaviors like rate limits or auth needs.

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 concise with two sentences, no wasted words. It front-loads the main purpose, but could be better structured by separating the core function from the output_path detail.

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?

Given the tool's complexity (12 parameters) and presence of an output schema, the description covers the key behavior of output_path but lacks context on when to use different extraction types or how they differ. This is adequate but not comprehensive.

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 the schema already documents parameters thoroughly. The description adds minimal value beyond restating extraction types and output_path behavior, meeting the baseline expectation.

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 the tool extracts structured data using CSS selectors or LLM, which is specific and clear. It distinguishes from sibling crawling tools but does not explicitly differentiate from other extraction tools like intelligent_extract or extract_entities.

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 provides no guidance on when to use this tool versus alternatives. It mentions the output_path behavior but lacks prerequisites or when-not-to-use instructions, which is a gap given the large set of sibling extraction tools.

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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