Analyze sentiment in Arabic text. Supports Gulf/Khaleeji, Egyptian, Levantine, and MSA dialects. Returns sentiment (positive/negative/neutral), confidence score, detected dialect, and keywords. Global LLMs hit ~45% accuracy on Gulf dialect — this tool is purpose-built for it.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Arabic text to analyze (max 10,000 characters) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden, and it rises well above the minimum: it details what the tool returns (sentiment, confidence score, dialect, keywords) and the language support. It doesn't specify edge-case handling (e.g., non-Arabic input, long text errors), but it's transparent about its core behavior and limitations, such as the 10,000-character limit.
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 tightly-written sentences cover the what, why, and output format without any fluff or repetition. Every clause adds value, from the list of dialects to the differentiator about LLM accuracy. This is excellent use of space and very easy to parse quickly.
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 textual parameter and no output schema, the description is impressively complete—it covers the return values (sentiment, confidence, dialect, keywords) and sets expectations for the tool's specialization. It could add detail on error handling or non-Arabic input behavior, but given the low complexity, this is more than sufficient.
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 for the single parameter 'text' is 100%, and the parameter description ('Arabic text to analyze') aligns directly with the tool's purpose. The description adds value by specifying the max length and expected language, making the parameter's meaning even clearer than the schema alone.
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 concrete verbs and nouns — 'Analyze sentiment in Arabic text' — and clearly enumerates its supported dialects (Gulf/Khaleeji, Egyptian, Levantine, MSA). It distinguishes itself from generic LLMs by targeting a specific niche (45% accuracy gap on Gulf dialect), which sets it apart from sibling tools.
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 when to use it (any Arabic sentiment analysis, especially for dialects) and contrasts with generic models, but doesn't explicitly name alternative tools or say 'use X instead for language detection' (e.g., detect_language). The context is clear enough for an agent to make a good choice, but there's room to be more explicit about 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.