argentina-price-index

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

With no annotations, the description carries the full burden. It adds useful behavioral context: data is free, source-cited, updated daily, and returns a value plus a 30-day rate. It also discloses method options. It stops short of explicitly stating read-only behavior or return format, but the retrieval nature is clear.

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, front-loaded with the core value proposition, then method details, then a pointer to the sibling tool. No wasted words; every sentence adds essential information.

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 annotations and no output schema, the description covers purpose, data freshness, and the method argument. However, the 'category' parameter is unmentioned, and the output format is only vaguely implied by 'value + 30-day rate'. These gaps prevent full completeness for an agent.

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 0%, so the description must compensate. It explains the 'method' parameter's enum values, including default and recommended, but completely omits the 'category' parameter. This leaves a significant semantic gap for a parameter that exists in 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?

The description clearly states the tool returns the latest Argentina food & beverage price index value and a 30-day rate, updated daily with source citations. It also differentiates itself from the sibling get_ar_food_price_index by explicitly pointing to that tool for the full daily series.

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 guidance to use get_ar_food_price_index for the full daily series, indicating when not to use this tool. It also explains the optional method parameter with default/recommended values, but doesn't mention get_ar_food_methods or when to use it.

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 provided, the description carries the transparency burden. It discloses that the tool is free and zero-cost, and that all estimates are computed from the same underlying data, which is useful behavioral context. It does not deeply cover data sourcing or limitations, but for a read-only comparison tool, this is adequate.

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 and front-loaded with the core purpose, using two sentences. However, 'Free' and 'Zero cost' are redundant, repeating the same information in both the label and the final phrase, which slightly detracts from conciseness.

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 tool with only one optional parameter and no output schema, the description covers the main purpose and adds context about cost and data consistency. Yet it does not describe the return format or mention the 'category' parameter, leaving some gaps for an agent to understand how to invoke it 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?

The schema declaration for the 'category' parameter has 0% coverage in the description; the description does not explain what this parameter does or its default value. Since the description does not compensate for the missing schema coverage, the parameter semantics are unclear, though the parameter is optional and has a sensible default.

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 identifies the tool's purpose as a methodology comparison for the Argentina food & beverage index, listing specific estimators (Jevons geometric mean, Carli arithmetic mean, trimmed mean). This distinguishes it from sibling tools like get_ar_food_headline and get_ar_food_price_index, which focus on headline numbers or price index values rather than cross-estimator comparison.

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 comparing estimation methodologies on the same underlying data, which is a clear context. However, it does not explicitly name alternatives or state when not to use the tool, though the distinct purpose of methodology comparison inherently sets it apart from the siblings.

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 full responsibility. It discloses key behavioral traits: source-cited from public retailer prices, updated daily, methodology published, base=100, and paid $0.02/call via x402 with mock in sandbox. This gives the agent important context about cost, data freshness, and provenance, though it does not describe return format details or error handling.

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 remarkably concise yet information-dense: five short sentences cover purpose, data source, update frequency, methodology, options, pricing, and sibling alternative. It front-loads the core purpose and contains no wasted words.

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 data retrieval tool with no output schema, the description sufficiently conveys return type (time series with base=100), update frequency, source, and cost. It lacks details like date range or pagination, but these are not implied by the parameters. It also ties in the free alternative, rounding out the operational context.

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 50%, and the description adds meaningful context for method and subcat by listing valid values and clarifying subcat is for sub-index. It also adds payment semantics ($0.02/call, sandbox mock) not present in the schema's _payment description. The `category` parameter is not mentioned, but it is a fixed default with a single possible value, reducing the gap.

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 uses a specific verb ('Returns') and clearly identifies the resource ('full daily Argentina food & beverage price index time series'), including base=100. It also distinguishes itself from siblings by explicitly mentioning 'Free headline via get_ar_food_headline' as a lighter alternative.

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 explicitly positions the tool as providing full daily series vs. the free headline alternative, giving clear context for when to use this tool. However, it does not mention the sibling get_ar_food_methods or provide explicit when-not guidance for method-related queries.

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