Detects language, script type, and Arabic dialect from any text. Supports Arabic (MSA, Gulf, Egyptian, Levantine, Maghrebi), English, French, Spanish, Turkish, Hindi. Also detects Arabizi (Latin-script Arabic) and mixed-language text.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyze (max 10,000 chars) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosure. It explicitly mentions detection of language, script type, Arabic dialect, and highlights special handling of Arabizi and mixed-language text, which goes beyond a generic 'detects language' statement. It implies a read-only operation (detection) without explicit confirmation, but the scope of behavior is well-defined. No contradictions exist.
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 two sentences, front-loaded with the primary purpose and followed by a concise list of supported languages and special features. Every sentence earns its place—first states the core action, second provides essential scope details. No filler, no redundancy, and highly scannable.
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?
There is no output schema, so the description should ideally explain what the tool returns (e.g., a language code, dialect label, confidence score). It lists what it detects (language, script, dialect) but does not specify the response structure or format. Given the tool's complexity (multiple detection dimensions), the lack of return-value info leaves a gap for the agent. However, the description does provide enough to guess basic behavior, but not complete.
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 input schema already provides full coverage for the single parameter 'text' with a description ('Text to analyze (max 10,000 chars)'), so schema coverage is 100%. The tool description does not add any additional meaning about the parameter—it mentions 'any text' but that's redundant with the schema. Since the schema does the heavy lifting, the baseline of 3 is appropriate; the description contributes little beyond existing schema info.
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's function: 'Detects language, script type, and Arabic dialect from any text.' It specifies the exact resources (language, script, dialect) and lists supported languages and special cases (Arabizi, mixed-language), which distinguishes it from sibling tools like arabic_sentiment or text_summarize. The verb 'detects' is precise and unambiguous.
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 provides clear context on what the tool does and its supported inputs (languages, scripts, dialects), allowing an agent to infer when to use it. However, it does not explicitly state when not to use it or mention alternative tools, such as arabizi_translate for translation or arabic_sentiment for sentiment, so it lacks explicit exclusions. The context is strong enough for a typical use case but not fully explicit.
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.