exchange_rateLive currency exchange rate: one pair with conversion, or a major-currency table for a base currency.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | Target currency code; omit for a table | |
| from | No | Base currency code, default USD | |
| amount | No | Amount to convert, default 1 |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only and idempotent. The description adds behavioral context by explaining the two return modes (conversion vs. table) and that the data is 'live.' No contradiction with annotations is present.
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?
A single, neatly front-loaded sentence concisely conveys the tool's purpose and modes without waste or redundancy.
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?
Given the presence of full schema descriptions, output schema, and safety annotations, the description adequately covers the tool's behavior. It explains both operational modes, which is sufficient for an agent to select and invoke the tool correctly.
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%, so the schema already documents each parameter. The description reinforces the purpose of 'to' (omit for table) but adds no additional syntax, format, or edge-case information beyond what the schema provides.
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's function: providing live currency exchange rates with two modes—a single pair conversion or a table for a base currency. It distinguishes itself from sibling tools (weather, stock quotes, etc.) by its specific domain and scope.
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 for when to use the tool (for live currency rates) and the two usage scenarios (pair conversion or base-currency table). It doesn't explicitly exclude alternatives, but sibling tools are obviously different domains, making the intended use clear enough.
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.
Most tools are clearly separated by domain, but the cluster of domain-related tools (domain_check, ssl_check, china_reachability) could be confused by an agent looking for a generic 'check this domain' operation. The descriptions help clarify each one's specific focus, so overall ambiguity is low.
Naming conventions are mixed: some tools use a verb-first pattern (check_vulns, get_weather, transpile_sql), while others use a noun-first pattern (domain_check, stock_quote, ip_lookup). Verbs are also inconsistent (check, get, lookup, transpile), and some names are pure noun phrases (exchange_rate, package_info). This lack of a uniform pattern makes the set feel disjointed.
At 11 tools, the count is within a reasonable range and each tool has a distinct purpose. The broad scope makes the set feel somewhat unfocused, but there is no redundancy or excessive bloat.
The server intends to provide live data, but coverage is shallow within each category. For example, weather only gives current conditions and a short forecast, package tools only have info and vulnerabilities, and there is no historical data for stocks. The overall domain is vague, so significant gaps exist for a general-purpose live-data server.