Current weather conditions for any city worldwide. Returns temperature, feels-like, humidity, precipitation, wind speed/direction, and weather condition description.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City name (e.g., Dubai, Abu Dhabi, London) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool 'Returns' data, which implies a non‑mutating read operation, and it does not claim any side effects. However, it does not explicitly state that it is read-only, nor does it disclose potential limitations (e.g., city not found, international coverage caveats). Thus, it gives some transparency but lacks explicit disclosure.
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 that provide the core purpose and the exact return fields. No unnecessary or redundant words. Front-loaded purpose and then scoped output. Every sentence contributes to understanding.
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 simple read-only weather tool with one parameter and no output schema, the description lists the expected return fields (temperature, feels-like, humidity, precipitation, wind speed/direction, weather condition). It also explicitly states that it covers any city, which addresses the geographic scope. Although it does not describe the concrete structure of the response (as no output schema exists), the described fields are sufficient for a user to know what data to expect.
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 has 100% description coverage with a clear city description including examples. The description adds no new semantic meaning beyond the schema's own parameter explanation. It does mention 'any city worldwide', but that is already a reasonable inference from the schema's 'City name' and examples. Since the description does not add extra context, it stays at the baseline per rubric for full 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 clearly states the tool provides 'Current weather conditions for any city worldwide' and lists the specific data fields to retrieve directly, using a specific verb 'Returns'. It naturally distinguishes from sibling data tools like currency_convert or gold_price, and there is no other weather tool in its sibship.
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 getting current weather conditions, which is clear from the title and content. It does not explicitly mention alternatives, but since no sibling tool overlaps with this function, the context is unambiguous. No explicit exclusions are provided, but the purpose is self-evident.
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.