Get live currency exchange rates between two currencies. Returns current mid-market rates. Cost: $0.01 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | Target currency code (e.g. JPY, GBP, EUR) | |
| from | Yes | Source currency code (e.g. USD, EUR, GBP) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate destructiveHint: true and readOnlyHint: false, which strongly suggest the tool modifies state. However, the description describes a read-only retrieval ('Get live currency exchange rates', 'Returns current mid-market rates'), creating a clear contradiction. The description fails to explain the potential destructive side effects or why these annotations exist.
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 (15 words) with zero redundancy. It efficiently conveys the core function, return value, and cost. Every word earns its place.
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?
The tool is simple with two string parameters and full schema coverage. The description mentions the return type ('current mid-market rates') but does not detail the output structure (e.g., JSON fields). Given no output schema, this is a minor gap. Annotations add contradictory context, reducing completeness.
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 adequate descriptions for both parameters (e.g., 'Source currency code (e.g. USD, EUR, GBP)'). The description does not add extra meaning beyond the schema, meeting the baseline for full 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 gets live currency exchange rates between two currencies and returns mid-market rates. The verb 'Get' and specific resource 'live currency exchange rates' are explicit. This tool is distinct from all sibling tools, which cover other data domains.
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 (when needing live exchange rates) but does not explicitly state when not to use or mention alternatives. Given the list of siblings, there is no other currency rate tool, so confusion is unlikely, but the description could be more precise.
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 target distinct data operations (matching, standardization, enrichment) with clear descriptions. Minor overlaps exist, e.g., address_match_advanced vs global_address_match, but descriptions differentiate them.
All tools follow a consistent 'interzoid_descriptive_function' pattern in snake_case, making it easy to predict purpose from the name.
58 tools is high for a single server, exceeding the typical 3-15 range. While each serves a specific data enrichment function, the quantity may overwhelm agents without clear categorization.
The tool surface covers a broad domain including address, company, person, and financial data. Minor gaps exist (e.g., no reverse IP lookup, limited social media coverage), but core data needs are well-addressed.
