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extract_url

Extract structured data from public web pages by specifying a URL and the fields you need. Returns clean JSON with requested information, or Markdown or raw HTML as needed.

Instructions

Extract structured data from permitted public web pages by providing a URL and describing what you want. Returns clean JSON with exactly the fields you asked for by default. Can also return clean Markdown or raw HTML when response_format is set. Uses supported fetch paths for JavaScript-heavy pages and returns explicit error signals when blocked. It does not solve CAPTCHA, access login/paywall-only pages, or circumvent anti-bot controls. This is the general-purpose extraction tool. Use extract_markdown for LLM/RAG-ready Markdown, extract_article for full article content, or extract_metadata for page meta tags instead, they are optimised shortcuts. Read-only, makes no changes to any external system. Requires HAUNT_API_KEY environment variable. Free tier: 1,000 credits/month. Returns an error if rate limit, credit quota, or API key is invalid.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe full URL of the page to extract data from. Must be a valid HTTP or HTTPS URL. Supports permitted public pages, including some JavaScript-heavy SPAs. Human-verification, login-required, CAPTCHA-gated, paywalled, and blocked pages return explicit errors rather than fabricated data.
promptYesA plain-English description of what data to extract from the page. Be specific about which fields you want. Examples: 'product name, price, and availability', 'all email addresses and phone numbers', 'the main heading, first paragraph, and all image URLs'. The more specific, the more accurate the extraction.
response_formatNoOptional output mode. Leave blank or use json for structured extraction. Use markdown/md when you want clean page text for an agent, RAG pipeline, or .md file. Use raw_html/html only when you need the fetched HTML.
Behavior5/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It fully discloses that the tool reads from external systems (read-only), uses supported fetch paths for JavaScript-heavy pages, returns explicit error signals when blocked, does not solve CAPTCHA, requires HAUNT_API_KEY, and mentions free tier limits and error conditions. This is comprehensive and accurate behavioral transparency.

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 well-structured with purpose at the beginning, then behavior, alternatives, and constraints. While slightly lengthy, each sentence adds value. It could be tightened by removing redundancy in the alternatives section, but overall it's efficient and clear.

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 or annotations, the description covers all critical aspects: input format, usage, behavior, error handling, authentication (HAUNT_API_KEY), rate limits (free tier), and explicit error signals. No missing information for an agent to use the tool correctly.

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% as all parameters have descriptions. The description adds meaningful context beyond the schema: for 'url', it specifies allowed formats and types of pages; for 'prompt', it provides concrete examples; for 'response_format', it explains the modes. Since schema_coverage is high, baseline is 3, but the added details justify a 4.

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 the tool extracts structured data from public web pages using a URL and description. It specifies the verb (extract), resource (permitted public web pages), and method. Additionally, it distinguishes from sibling tools by naming them and stating they are optimised shortcuts, making it easy for an agent to select the correct tool.

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?

Description explicitly tells when to use this tool (general-purpose extraction) and names alternatives (extract_markdown, extract_article, extract_metadata) with their purposes. It also provides explicit limitations: does not solve CAPTCHA, access login/paywall-only pages, or circumvent anti-bot controls. This gives clear when-to-use and when-not-to-use guidance.

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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