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extract_entities

Read-only

Extract emails, phone numbers, URLs, dates, IPs, and prices from web pages. Optionally save full entity data to a JSON file.

Instructions

Extract entities (emails, phones, etc.) from web pages. Use output_path to persist the full entity extraction output to disk as JSON and receive a slim response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesTarget URL
entity_typesYesTypes: email, phone, url, date, ip, price
custom_patternsNoCustom regex patterns
include_contextNoInclude context
deduplicateNoRemove duplicates
use_llmNoUse LLM for NER
llm_providerNoLLM provider
llm_modelNoLLM model
output_pathNoAbsolute file path (auto .json extension) to persist the full entity extraction as JSON. When set, the response is slimmed (content, markdown, extracted_data.raw_content removed).
include_content_in_responseNoWhen True (with output_path set), also keep the entity data in the response. Defaults to False.
overwriteNoOverwrite an existing output file at output_path. Defaults to False.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Description adds context beyond readOnlyHint annotation, detailing the output_path parameter behavior (persisting full output to disk and returning slim response). No contradictions with 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, front-loaded with purpose, no wasted words. Efficient and well-structured.

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?

Tool has output schema covering return values, and description explains key behavioral nuance. For a complex 11-parameter tool, description is brief but adequate given schema richness.

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?

Schema coverage is 100%, so baseline 3 is appropriate. Description enhances understanding of the output_path parameter, explaining its effect on response format, which exceeds baseline.

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?

Description clearly states 'Extract entities (emails, phones, etc.) from web pages,' providing a specific verb and resource with examples. This distinguishes it from siblings like extract_structured_data and intelligent_extract.

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?

Description does not provide explicit when-to-use or when-not-to-use guidance. No mention of alternatives or context for selecting this tool over similar siblings like extract_structured_data.

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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