Extract text content, links, title, and metadata from any URL. Returns cleaned text (scripts/styles removed), page title, meta description, and up to 50 links.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Full URL to scrape (e.g., https://example.com) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the output format (cleaned text, page title, meta description, up to 50 links) and notes that scripts/styles are removed. However, it does not mention potential side effects such as redirects, error handling, rate limits, or any other behavioral aspects that might affect the agent's decision, especially given the lack of annotations.
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?
The description is concise and free of redundancy. Every sentence adds value—stating the action and the specific output format—without unnecessary elaboration.
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?
Given that there is no output schema, the description adequately explains what the tool returns (text, title, meta description, links). It covers the core expected behavior but omits details about error scenarios, URL validation, or response formatting, which could be considered minor gaps but not detrimental.
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?
There is only one parameter (url) and its schema description fully covers it (100% coverage). The tool description adds no additional detail beyond the schema, so per the rubric, a baseline of 3 is appropriate.
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 clearly states the tool extracts text content, links, title, and metadata from a URL. It is specific about the verb and resource, but does not explicitly differentiate from sibling tools like screenshot or scrape_structured, though the name and description strongly imply raw text extraction.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, limitations, or typical use cases, leaving the agent to infer appropriateness from the name and description alone.
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.