Domain enrichment: returns company name, description, detected tech stack (React, WordPress, Shopify, etc.), social media links (Twitter, LinkedIn, GitHub), email addresses, and redirect info for any domain.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain name to enrich (e.g., google.com, aramco.com) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavior. It lists output fields but does not mention potential limitations (e.g., whether the domain must be publicly accessible, rate limits, or whether it performs a live scan or uses cached data). This is a significant gap for a tool that fetches external data.
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 a single concise sentence that lists the rich set of data returned. It front-loads the purpose and is efficient, though it could be slightly longer to include usage nuance.
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 the tool's moderate complexity (multiple data types returned) and no annotations, the description covers output types but misses behavioral aspects like data freshness, dependency on third-party sources, and potential errors. It is adequate for basic understanding but incomplete for nuanced usage.
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?
Schema coverage is 100% for the single parameter 'domain', which has a clear description. The description reinforces the parameter by giving examples (e.g., google.com, aramco.com) and connecting it to the enrichment output, but adds no new semantics beyond what the schema already provides.
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 that the tool enriches a domain with company info, tech stack, social links, emails, and redirect info. It is specific about the resource (domain) and the type of data returned, but doesn't explicitly distinguish it from siblings like domain_infra or site_audit, which might also relate to domains.
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 usage for enriching a domain with business information, but does not explicitly state when to use this tool versus domain_infra or site_audit. It lacks guidance on alternatives or when not to use it.
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.