Returns live silver price per gram from zahabprice.com — updated every ~60 seconds. Infer country from context same as get_gold_price.
| Name | Required | Description | Default |
|---|---|---|---|
| purity | No | Silver purity. Default: 999 (fine silver). 925 = sterling silver. | |
| country | No | ISO country code. Default: sa (Saudi Arabia). | sa |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses the data source (zahabprice.com), the update frequency (~60 seconds), and a behavioral note about country inference. It doesn't detail return format or error behavior, but for a simple price getter this is sufficient.
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?
Two concise sentences front-load the core purpose and source, then add freshness and a cross-reference. No wasted words.
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 two-parameter tool with no output schema, the description provides purpose, source, freshness, and country inference. It doesn't explain the return format explicitly, but 'price per gram' gives a strong hint, and the reference to get_gold_price implies consistency with that tool.
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 has 100% coverage with descriptions for both parameters. The description adds no additional parameter details beyond referencing country inference context, which is not directly parameter-specific. Baseline 3 applies.
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 a specific action ('Returns live silver price per gram') and identifies the resource (zahabprice.com). It distinguishes itself from siblings by specifying 'silver' and referencing get_gold_price for context.
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 silver price lookups and provides context about country inference by referencing get_gold_price. It doesn't explicitly exclude alternatives or state when-not-to-use, but the tool's purpose is obvious from the name and description.
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.
Each tool has a clearly distinct purpose: retrieving gold price, retrieving silver price, listing countries, and calculating installments. No two tools overlap in function, so an agent can easily select the right one.
All tool names follow a consistent verb_noun pattern in lowercase with underscores: get_gold_price, get_silver_price, list_countries, calculate_installment. The naming is uniform and predictable.
With 4 tools, the server is well-scoped for its purpose of providing live gold/silver prices and related purchase calculations. The count is within the ideal range and each tool earns its place.
The tool surface covers the primary domain actions: fetching prices, listing countries, and calculating installments. Minor gaps like historical price data or unit conversions exist, but they are not essential to the core workflows.
