Convert Arabizi (Latin-script Arabic like '7abibi', 'shlonk', '3aysh') to Arabic script (حبيبي، شلونك، عايش). Gulf dialect optimized. Handles number-to-letter mappings (3=ع, 7=ح, 5=خ, 8=ق, 9=ص).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Arabizi text to transliterate (max 5,000 characters) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden of disclosure. It explains the transformation rules, includes number-to-letter mappings (3=ع, 7=ح, etc.), and indicates dialect focus. This goes beyond a minimal description by explaining how the tool behaves, though it doesn't address edge cases like handling of proper nouns or mixed-script input.
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 exceptionally concise—two sentences—yet packs in the action, input/output examples, dialect note, and key mappings. Every phrase adds value with no redundancy or fluff. The structure front-loads the core purpose and follows with clarifying details.
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 tool with a single parameter and no output schema, this description is highly complete. It covers the tool's purpose, provides input/output examples, specifies dialect optimization, and lists common number mappings. An agent could confidently invoke this tool based on the description alone.
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 schema already provides 100% coverage by describing the 'text' parameter with examples and a 5,000-character limit. The description adds no additional parameter-specific information beyond what the schema provides, aligning with the baseline score of 3 for high schema coverage.
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 ('Convert'), identifies the exact resource (Arabizi to Arabic script), and includes concrete examples ('7abibi', 'حبيبي'). This clearly distinguishes the tool's function from any sibling tool, even without naming alternatives, making the purpose unmistakable.
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 by specifying 'Gulf dialect optimized' and gives examples of input types (numbers substituting Arabic letters). While it doesn't explicitly name alternatives or state when not to use the tool, the context is sufficient for an agent to infer appropriate usage. No exclusions are mentioned, aligning with a score of 4.
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.