verified-codes

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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Since no annotations are provided, the description carries the full burden. It transparently indicates the tool returns brands with category, offer, and code, implying a read operation. However, it does not explicitly state it is read-only or describe potential side effects, which is a minor gap.

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 sentences, no extraneous words. The purpose and filter options are stated upfront, making it easy to scan.

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 listing tool with one optional parameter and no output schema, the description provides all necessary information: what is returned (brand, category, offer, code) and the available filter values. No gaps.

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 schema covers 100% of the single optional parameter with a description and example. The tool description adds value by enumerating all allowed category values, which goes beyond the schema's brief description.

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 returns every brand with its category, current offer, and code, using a specific verb ('returns') and resource ('every brand'). This distinguishes it from siblings like get_code which retrieves a single code, and search_codes which performs broader searches.

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 implicitly suggests use for listing all brands with an optional category filter, but does not explicitly state when to use this tool over alternatives (e.g., get_code for a specific code). No when-not guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Discloses it returns 'matching verified-code records' but does not mention pagination, limits, or exact response structure.

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 sentences, front-loaded with purpose and examples, no redundant information. Every word contributes.

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 simple schema (one parameter, no output schema), description adequately explains input domain and output type. Sibling tools help contextualize, but missing details like result count or language support.

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 covers the single 'query' parameter with basic description. The tool description adds semantic context with examples and scope ('brands, categories, offers'), which is valuable beyond 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?

Description clearly states 'free-text search across brands, categories and offers', a specific verb+resource, and distinguishes from sibling tools 'get_code' (single record) and 'list_brands' (brands only).

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?

Provides explicit examples like 'stock perps discount', 'esim', '20% off exchange' illustrating use cases, but does not mention when not to use it or alternatives beyond siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.