Current retail gold prices per gram in 24K/22K/21K/18K across 10 currencies (USD, AED, EUR, GBP, INR, SAR, PKR, PHP, BDT, EGP). Consumer retail pricing.
| Name | Required | Description | Default |
|---|---|---|---|
| karat | No | Gold karat: 24K, 22K, 21K, 18K, or 'all' (default: all) | |
| currency | No | Currency code: USD, AED, EUR, GBP, INR, SAR, PKR, PHP, BDT, EGP (default: USD) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds context that the data is consumer retail pricing and current, but it does not describe output format, caching, rate limits, or how the defaults of 'all' and 'USD' behave beyond the schema.
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 short sentences that convey all essential scope details without any filler. Every phrase contributes meaning, making it highly concise and front-loaded.
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 price lookup tool with two optional, well-described parameters and no output schema, the description covers the likely use case and scope in sufficient detail. A brief mention of the response structure would have made it fully 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?
Schema coverage is 100% with both parameters already described. The description adds modest context (per gram, retail pricing) but does not materially expand on the parameter semantics beyond what the schema already states.
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 identifies the resource as current retail gold prices per gram, listing specific karats and currencies, which distinguishes it from sibling tools like currency_convert and crypto_data. It lacks an explicit verb like 'get' or 'retrieve,' but the intent is 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 implies when to use the tool: when current retail gold prices are needed. However, it offers no explicit guidance on when not to use it or which alternative tool might be more appropriate for related financial data.
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.