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intelligent_extract

Read-only

Extract specific data from web pages using AI. Define what to extract and optionally save full output as JSON.

Instructions

Extract specific data from web pages using LLM. Use output_path to persist the full extraction output to disk as JSON and receive a slim response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesTarget URL
extraction_goalYesData to extract
content_filterNo'bm25'|'pruning'|'llm'bm25
filter_queryNoBM25 filter keywords
chunk_contentNoSplit content
use_llmNoEnable LLM
llm_providerNoLLM provider
llm_modelNoLLM model
custom_instructionsNoLLM instructions
output_pathNoAbsolute file path (auto .json extension) to persist the full extracted data + content as JSON. When set, the response is slimmed to metadata+file path (extracted_data.raw_content, content, markdown, table_data removed).
include_content_in_responseNoWhen True (with output_path set), also keep extracted_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, so the tool is non-destructive. The description adds the behavior of output_path enabling slim responses. No further details on rate limits, error handling, or LLM behavior are provided. Does not contradict annotations.

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?

Two sentences: first states purpose, second explains key workflow parameter. No extraneous information. Efficient and front-loaded.

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?

Given the tool's complexity (12 parameters, output schema present), the description covers the main purpose and the critical output_path behavior. It could be improved by clarifying when to use this over crawling or batch tools, but overall it is sufficient for an agent to understand the tool's role.

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 most parameter meanings are already clear. The description adds context for output_path, explaining its effect on response structure. This provides marginal additional value beyond the schema.

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 clearly states the tool extracts specific data from web pages using LLM, distinguishing it from sibling tools like crawl_url which likely return full content. The verb 'extract' and resource 'web pages' are specific.

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

Usage Guidelines3/5

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

The description implies usage for extracting data from web pages with optional output persistence, but does not explicitly state when to use this tool versus alternatives like batch_crawl or search_and_crawl. No exclusions or prerequisites are mentioned.

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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