product-intelligence

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

The description adds valuable behavioral context beyond the annotations: recording started 2026-07-27 so earlier days are empty, and prices are integer minor units. This complements the existing readOnlyHint and idempotentHint annotations without contradiction.

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 three sentences, front-loaded with the main purpose, then usage guidance, then a data caveat and format detail. Every sentence contributes meaning 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 output schema exists and the tool is read-only, the description covers all necessary caveats (recording date, price format) and positions the tool among siblings. It is complete for an agent to select and invoke 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?

Schema coverage is 100%, with both parameters fully described in the schema. The description does not add parameter-specific meaning beyond reinforcing the country scope, so the baseline of 3 applies.

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 daily lowest observed prices for a product over the last year in one country. This is a specific verb+resource combination that distinguishes it from siblings like search_products (which finds products) and get_product (which gets product details).

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 says 'Use this to judge whether a current price or a claimed discount is actually good', providing clear context for when to use the tool. However, it does not explicitly name alternatives or state when not to use it, stopping short of a 5.

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?

Annotations already declare this as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context beyond annotations by stating what is returned: 'Returns the product with its best offer in the given country.' No contradictions with annotations.

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 three purposeful sentences, front-loaded with the core action and resource, then usage context and return value. No unnecessary words or repetition.

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 lookup tool with an output schema and strong annotations, the description covers the core purpose, the exact identifiers to use, the use case, and the nature of the return value. No major gaps remain for an agent to select and 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 already describes the id and country parameters well, and the description reinforces that id can be a barcode or Ponito product id and that offers are country-specific. However, the optional language parameter is not described in the schema nor clarified in the description, leaving a small semantic 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 the specific verb 'Look up' with a clear resource: 'one product by GTIN (EAN/UPC barcode) or Ponito product id.' It distinguishes itself from siblings like search_products by emphasizing that it is for a known, exact product rather than discovery.

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?

It explicitly states when to use this tool: 'Use this when you already know exactly which product you mean — e.g. from a barcode, a previous search result, or a product page.' This implies search_products should be used when the product is not yet identified, and get_price_history for price history needs.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so no contradiction. The description adds useful behavioral context beyond the annotations: it specifies that results are GTIN-anchored, include the best offer (lowest price, integer minor units), and a shop count. This gives an agent a clearer picture of the response shape, though it does not mention pagination or other edge cases.

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 exceptionally concise: two sentences that front-load the core purpose, then the usage context, then a brief summary of the return value. Every sentence earns its place, with no fluff or 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 moderate complexity, the rich annotations (readOnly, idempotent), the fully described schema, and the presence of an output schema, the description provides sufficient context. It tells the agent what the tool does, when to call it, and what kind of data to expect. No critical information is missing.

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 input schema provides 100% coverage, with each parameter described clearly (e.g., 'country' has 'offers and prices are country-specific'). The description does not add parameter-level meaning beyond the schema; it only restates the country-specific aspect implicitly. Baseline 3 is appropriate because the schema already carries the parameter documentation.

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 opens with a clear verb+resource: 'Search Ponito's product catalogue.' It further specifies the exact use cases (finding products, comparing options, checking sellers) and the return value (GTIN-anchored products with best offer and shop count), which distinguishes it from sibling tools like get_product and get_price_history.

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 says 'Call this for any shopping query' and lists three concrete scenarios. It provides clear context for when to use this tool, though it does not explicitly state when not to use it or name the sibling alternatives. The exclusion guidance is implied by the siblings' names, but not stated.

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