cashbackpro

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

With no annotations, the description fully explains the calculation and output (estimates for each level). It discloses the formula and the revenue share percentages, which is sufficient for a non-destructive calculation tool.

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 concise (two sentences) and front-loaded with the purpose and formula. Every sentence is informative 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?

Given the tool's simplicity, two parameters, and no output schema, the description provides complete context: the formula, level shares, and that it returns estimates for every level.

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 both parameters described. The description adds value by explaining how they are used in the formula, going beyond the schema's basic descriptions.

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 estimates cashback for a purchase, provides the formula (commission = price × commission rate), and specifies the user's share per CashbackPro level. This distinguishes it from siblings like search_products or get_cashback_link.

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 tells when to use this tool (when you need cashback estimates for different levels) but does not explicitly mention when not to use it or alternatives. However, the context is clear enough for an agent 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?

Without annotations, the description fully covers behavioral traits: it states that the link works without an account but yields no cashback, and that the service is supported by affiliate commissions. It also instructs the agent to inform the user, showing transparency.

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 moderately concise and front-loaded with the core purpose. Every sentence adds value, though it is slightly longer than necessary. Structure is clear and logically organized.

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 no output schema, the description omits what the tool returns (the affiliate link URL). It covers behavioral context well but lacks return value specification, leaving some completeness gap.

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 a clear description for the 'product' parameter. The tool description does not add extra semantic meaning beyond the schema, so baseline 3 is appropriate.

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 action and resource: 'Get a CashbackPro affiliate link for an AliExpress product.' It effectively distinguishes this tool from siblings like get_product_details and calculate_cashback by focusing on affiliate link generation.

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 explains when to use the tool (to get an affiliate link) and provides context for two scenarios (with/without connected account), including explicit instructions to inform the user. However, it does not mention alternatives or when not to use this tool.

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 are provided, so the description bears full responsibility. It only says 'Get authoritative details,' which implies a read operation, but fails to mention rate limits, authentication, or any side effects. For a tool with no annotations, this is insufficient to fully inform the agent about behavior.

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 a single, well-structured sentence that packs the purpose, input, and output examples without any wasted words. It is front-loaded and easy to parse.

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 simplicity (one parameter, no output schema, no annotations), the description is adequate but not thorough. It lacks details on return format, error handling, or usage prerequisites. For a tool with no output schema, more context on what the response contains would improve 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?

The description adds meaning beyond the schema by specifying the input is a 'numeric AliExpress product ID' and listing the type of details returned. The schema already has a pattern for digits, but the description contextualizes the parameter and output, which is valuable.

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 it retrieves 'authoritative details' for a specific product by its numeric ID, listing examples like price, rating, sales volume, commission rate, category. This distinguishes it from sibling tools such as search_products (for discovery) and get_promo_codes (for promotions), making the purpose very clear.

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 when you have a specific numeric product ID and need detailed info. It does not explicitly exclude cases like needing to search first or requiring authentication, but the sibling context provides some guidance. A more explicit when-not would improve clarity.

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?

With no annotations, the description fully discloses behavioral traits: data refreshes daily, expired codes filtered out, returns specific fields, and notes that promo codes stack with cashback. No contradictions.

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?

Three sentences, each adding distinct value: purpose, output details, and sibling cross-reference. No filler, front-loaded with main action.

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?

No output schema, but description provides enough high-level detail on return contents (fields, filtering, refresh behavior). Sufficient for a zero-parameter tool, though exact structure is not specified.

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?

Input schema has zero parameters with 100% coverage, baseline is 4. Description does not need to add parameter info, and correctly focuses on purpose and output.

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 lists active AliExpress promo codes from CashbackPro, specifying scope (global, USD), types (platform-wide, product-specific), and included fields (minimum spend, remaining quantity, expiry). Distinguishes implicitly from sibling by referencing get_cashback_link.

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 clear context for when to use: when active promo codes are needed. Includes a cross-reference to sibling get_cashback_link for cashback earnings, but does not explicitly exclude other siblings or provide when-not-to-use guidance.

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 are provided, so the description carries the full burden. It mentions automatic catalog quality filters but does not disclose whether the tool is read-only, what errors may occur, authentication needs, rate limits, or any destructive potential. The lack of read-only assertion is a significant gap for a search 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?

Two sentences with no redundancy. First sentence delivers purpose and output; second adds context about filters. Every word earns its place. Highly efficient.

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?

With no output schema, the description partially covers return values ('compact product cards with prices, ratings and commission rates'), which is adequate but lacks details on pagination, total counts, or error conditions. For a search tool, more completeness would be beneficial.

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 description coverage is 100%, so the schema already documents the 'query', 'limit', and 'max_price_usd' parameters. The description adds no additional meaning beyond the schema, only noting automatic filters unrelated to parameter semantics. Baseline 3 is appropriate.

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 verb ('Search'), the resource ('AliExpress products with cashback via CashbackPro'), and the output ('compact product cards with prices, ratings and commission rates'). It distinguishes itself from sibling tools like get_product_details and get_cashback_link by specifying the compact card format and cashback focus.

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 searching products with cashback but does not explicitly state when to use this tool versus alternatives (e.g., get_product_details for full details, get_cashback_link for links). No guidelines on when not to use it.

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