Smart structured web scraper. Unlike basic scrapers that return raw text, this extracts and LABELS structured data: emails, phone numbers, prices, dates, tables, links, headings, and metadata. Returns agent-ready JSON — no further parsing needed.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Full URL to scrape and structure |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It clearly discloses the output behavior: returns agent-ready JSON, needs no further parsing, and extracts a defined set of structured data types. It doesn't mention failure modes or URL constraints, but the non-destructive read-only nature is evident.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences deliver the purpose, differentiation, output format, and examples of extracted data. There is no redundancy, and the information is front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description sufficiently covers behavior and return format. It could mention JavaScript rendering or failure behavior, but the gap is minor given the simplicity and the availability of sibling tools for broader scraping needs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter, 'url', has a complete schema description ('Full URL to scrape and structure'), and schema coverage is 100%. The tool description does not add much parameter-specific detail beyond what schema already says, earning the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb+resource combination: 'web scraper' that 'extracts and LABELS structured data', with explicit examples like emails, prices, and tables. It clearly distinguishes itself from 'basic scrapers that return raw text', which differentiates it from sibling web_scrape.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies clear when-to-use guidance: choose this tool when you need labeled structured JSON rather than raw text. It contrasts with 'basic scrapers' as an implicit alternative, though it does not explicitly name sibling web_scrape or state exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping functionality, such as domain_infra, company_report, and due_diligence all covering DNS/WHOIS/SSL checks. Similarly, web_scrape and scrape_structured both extract website content, and verify_email overlaps with email_audit on DNS-based email checks. This creates ambiguity in tool selection, especially for agents looking for a specific type of analysis.
All tool names use lowercase with underscores, which provides a consistent style. However, the grammatical pattern varies: some are verb-object (verify_email, currency_convert), some are noun-noun (domain_infra, site_audit), and others are adjective-noun (arabic_sentiment, brand_scout). This is not chaotic, but it lacks a rigid verb_noun convention, making it slightly less predictable.
With 25 tools, this sits at the upper boundary of what is considered 'heavy' but is still usable. The server covers a wide range of domains (Arabic NLP, web scraping, domain/email analysis, finance, faith), so the count is justified to a degree, but agents may be overwhelmed by choice. It is borderline appropriate for such a broad utility server.
The tool surface covers many common operations (scraping, DNS checks, email verification, financial data), but there are notable gaps. For example, no generic translation tool exists, only Arabizi-to-Arabic, and there is no text generation or embedding. While the set is extensive, it is not fully comprehensive for the diverse domains it touches, leaving some obvious missing operations.