Channel3 Shopping

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

Annotations already declare readOnly, openWorld, and idempotent behavior, and the description adds minimal extra context. It does not contradict annotations, and while it adds the UI scope, it doesn't disclose pagination mechanics or result ordering beyond what schema already provides.

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 entire description is a single concise sentence that communicates the tool's purpose efficiently, with no filler.

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?

The description is short, but combined with a rich schema and annotations, it covers the essentials. However, the lack of usage differentiation from sibling search_products leaves a gap in contextual 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 schema has solid descriptions for query, image_url, and page_token (75% coverage). The tool description adds no additional parameter semantics, so it relies on the schema. Given this coverage level, the description doesn't need to compensate, but it also doesn't add value.

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 searches and pages through products, with a storefront UI scope, which is specific and action-oriented. However, it does not explicitly differentiate from sibling search_products, so it stops short of a 5.

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 provides a context (storefront UI) but no explicit guidance on when to use this tool versus search_products or other siblings. Usage is implied rather than stated.

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 cover read-only, idempotent, and open-world behavior. The description adds the 'live offers' context, hinting at real-time data, but does not elaborate on behavioral traits like error handling, response shape, or any side effects. No contradiction 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 a single, information-dense sentence that front-loads the action and resource. Every word contributes purpose or context; no filler 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 one-parameter tool with full schema coverage, clear annotations, and an output schema present, the description is largely complete. The only minor gap is explicit sibling differentiation, but the purpose is unambiguous enough for selection and invocation.

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 fully describes product_id as 'Canonical product ID' (100% coverage), so the description does not need to add much. It adds no additional parameter-level meaning, matching the baseline 3 for high schema coverage.

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 ('Fetch') and resource ('a product') and adds meaningful scope: 'with live offers, for the storefront UI.' This clearly distinguishes it from siblings like get_products (plural/general) or get_price_history (time-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?

The phrase 'for the storefront UI' implies a use case but provides no explicit guidance on when to choose this tool versus alternatives such as get_products, get_similar, or browse_products. There are no stated exclusions or alternative references.

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, openWorldHint, and idempotentHint, so the description adds value by disclosing the 30-day lookback window and the UI-oriented output. This contextual detail goes beyond the structured annotations without contradicting them.

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, front-loaded sentence: 'Fetch 30-day price history for a product, for the storefront UI.' Every word earns its place, with no redundancy or filler.

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 one parameter, a documented schema, an output schema present, and strong annotations, this description provides all necessary context. It clearly states what data is returned (30-day history) and where it is used (storefront UI), making it fully sufficient for selecting and invoking the tool.

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%, with product_id described as 'Canonical product ID.' The tool description adds no additional parameter semantics beyond what the schema already provides, so a baseline score of 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 uses a specific verb ('Fetch') and clear resource ('30-day price history for a product'), and explicitly scopes the use case ('for the storefront UI'). This distinguishes it from sibling tools like get_details or browse_products, which target different data.

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 phrase 'for the storefront UI' gives clear context on when to use this tool—for displaying price history in the storefront. It does not explicitly name alternatives or exclusions, but the context is sufficient to select it over 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?

Annotations already provide readOnlyHint, openWorldHint, and idempotentHint, covering the safety profile. The description adds that it returns 'full product data' and details 'in the storefront UI,' but this is somewhat ambiguous and doesn't address error handling or unavailable fields. With annotations, this is acceptable but not rich.

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 with no fluff. The first sentence delivers the main purpose, the second provides a necessary prerequisite, and the third notes the return format. Content is front-loaded and each sentence earns its place.

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 single-parameter read-only tool with an output schema and strong annotations, the description covers the essential workflow: find products with search_products, then fetch full data by ID or URL, optionally multiple at once. It could be more explicit about how it differs from get_details, but 'full product data' implies comprehensiveness.

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 description for product_ids already fully documents the parameter (IDs or URLs, min/max, parallel fetch). The description adds only a comparison use case, which is marginal. Since schema coverage is 100%, a baseline of 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 tool's function: 'Get full product data... by product ID... or by retailer URL.' It specifies the resource and scope, and distinguishes from search_products by framing this as the retrieval step after searching.

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 says 'Use search_products to find products,' which tells the agent when to use this tool in a workflow. It also suggests passing several IDs to compare, a useful tip. However, it doesn't mention alternatives like get_details or get_price_history, so it lacks explicit exclusions.

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, openWorldHint, and idempotentHint, covering the safety profile. The description adds minimal behavioral context beyond the basic operation, such as how similarity is computed or whether results are ordered. No contradiction 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 a single, concise sentence of 10 words. It is front-loaded with the action and resource, with 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?

Given the tool's simplicity (2 params), the presence of an output schema, and rich annotations, the description is nearly complete. It lacks explicit alternative guidance, but for a straightforward read operation, the context provided by the description and structured data is sufficient.

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 describes product_id as 'Canonical product ID to find similar products for.' The limit parameter lacks a description but its meaning is inferable from name and constraints. The tool description itself adds no parameter details, leaving the undocumented limit partially unexplained.

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 'Find' and clearly states the resource 'products similar to a given product' plus the context 'for the storefront UI'. This distinguishes it from sibling tools like search_products and browse_products.

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 phrase 'for the storefront UI' provides some usage context, implying this is for the recommendation display in the storefront. However, it does not explicitly mention when to use this tool instead of alternatives like search_products or get_details, nor does it state any exclusions.

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, openWorldHint, and idempotentHint, establishing safety. The description adds behavioral context beyond this: the tool returns 'up to 8 product cards plus structured product data,' and notes that calls should be sent independently together. 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 compact and well-structured: first sentence states purpose, second gives usage instruction, third states output. Each sentence earns its place, with no fluff or repetition. It is appropriately sized for the tool's complexity.

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 annotations cover safety, the description provides key info: scope (one product type), return limit (up to 8 cards), and parallelization guidance. It is complete enough for correct invocation, though it could add details about error cases or pagination if any, but those are not necessary given other structured data.

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%, with rich descriptions for both parameters (query and image_url). The description's mention of 'Put relevant constraints in `query`' reinforces but does not add new meaning beyond what the schema already provides. 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 tool's function: 'Search 100M+ products across thousands of retailers for one product type.' It uses a specific verb (search), resource (products), and scope (100M+ products, one product type, returns up to 8 product cards). This distinguishes it from siblings like get_details or get_price_history, which imply more targeted lookups rather than broad search.

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 gives concrete usage guidance: 'Call once for each distinct product type, and send independent calls together,' and 'Put relevant constraints in `query`.' This implies when to use the tool and how to batch calls, but it does not explicitly state exclusions or name alternative tools. It provides clear context but lacks a direct when-not-to-use clause.

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