get_codeReturns the full verified-code record for one brand: the code, the benefit, how to apply it, last-verified date, availability notes, and the guide/review URLs. Match by brand name or slug (e.g. 'kraken', 'Bybit Card').
| Name | Required | Description | Default |
|---|---|---|---|
| brand | Yes | Brand name or slug, e.g. 'kraken' or 'Bybit Card' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes what the tool returns in detail (code, benefit, apply, dates, URLs). No annotations provided, so description carries full burden; lacks mention of error handling or authentication, but adequate for the simple read operation.
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?
Single concise sentence that front-loads purpose, lists contents, and specifies matching method without 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?
For a simple tool with one parameter and no output schema, the description covers all essential information: what is returned and how to call it.
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?
Single parameter 'brand' is well-described with examples and matching strategy (name/slug). Schema coverage is 100%, and description adds practical context beyond the schema.
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?
Clearly states it returns the full verified-code record for one brand, listing specific fields (code, benefit, apply method, etc.) and distinguishes from siblings (list_brands, search_codes) by focusing on a single brand.
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?
Indicates matching by brand name or slug with examples. While it doesn't explicitly exclude when to use alternatives, the purpose is clear enough for an AI to decide.
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 serves a unique purpose: get_code retrieves a specific brand's code, list_brands enumerates all brands with optional filtering, and search_codes does free-text search. No overlap in functionality.
All tools follow a consistent verb_noun snake_case pattern (get_code, list_brands, search_codes), making them predictable and easy to understand.
With 3 tools, the server is concise and well-scoped for a code lookup service. It covers the essential query operations without excess, though adding a tool for direct code string lookup could be beneficial.
The tool set covers the main use cases: retrieving a specific code, browsing all codes, and searching. It lacks update/delete tools, but for a read-oriented API focused on finding codes, it is reasonably complete.