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

With no annotations, the description carries the full burden of behavioral disclosure. It does reveal the return value (per-retailer offers and low/high range), but it omits any caveats such as region effects on results or what happens if both query and URL are provided. It gives basic output info but lacks depth.

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 primary purpose and output. Every sentence earns its place, and there is no filler or repetition of schema details.

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 complexity (3 optional params, no output schema), the description adequately covers the input mode (query or URL) and the return format. It could mention that region affects retailer selection, but the schema already documents the region parameter and its default, so the description does not need to repeat 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?

Schema coverage is 100%, so baseline is 3. The description adds important semantic context by stating 'query OR a product URL', clarifying that exactly one of these is expected despite no required parameters in the schema. This adds value beyond the individual field 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 specific verb 'Compare' and the resource ('one product's price across multiple retailers'), and distinguishes itself from siblings like get_product, search_products, and list_retailers by focusing on price comparison. It also specifies the output: 'per-retailer offers plus the low/high price range.'

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 clear context for when to use the tool (to compare prices across retailers) and states the input requirement ('Provide a query OR a product URL'). However, it does not explicitly name alternatives or exclusions, though the sibling tool names in context signals imply the intended use cases.

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 conveys that this is a read-only operation ('look up') and adds context about scope ('from hundreds of online stores') and output fields. However, it does not disclose potential limitations such as ambiguity in whether results are aggregated across stores, latency, or failure modes for invalid URLs, leaving some behavioral traits implicit.

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 front-loads the main action and lists key data points in a dash-separated list. Every word contributes meaning, with 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 tool with two parameters and no output schema, the description is adequately complete. It specifies the input (URL), the output categories (price, availability, images, specs, identifiers), and the scope (hundreds of stores). It doesn't cover edge cases like invalid URLs, but that's a minor gap for a lookup 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?

The input schema provides 100% coverage with descriptions for both parameters (url and fields). The description adds no additional parameter-level semantics, so the baseline score of 3 is appropriate since the schema already handles the burden.

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 purpose: 'Look up a product by its URL' with a specific verb and resource. It lists the exact data returned (price, availability, images, specs, identifiers), distinguishing it from sibling tools like search_products which likely search by query, and compare_prices which focuses on price 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 provides clear context: use this tool when you have a product URL and want detailed product information. It does not explicitly name alternatives or exclusions, so it falls short of a 5, but the condition ('by its URL') makes the usage context unambiguous.

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?

The description discloses a useful behavioral trait: the operation is free and does not consume quota. It also implies a read-only list operation with no destructive side effects. No annotations are provided, so the description carries this burden well.

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 that states the action and the optional filter. 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 simple list tool with one optional parameter and no output schema, the description covers the core purpose, the filter, and the quota impact. It does not describe the return format, but the simplicity makes this acceptable.

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 fully documents the region parameter with enum and description. The description's mention of optional region filtering repeats the schema without adding new meaning, so it meets the baseline for full 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 clearly states the tool lists the stores Silicon supports, optionally filtered by region. This is a specific verb+resource pair and distinguishes it from sibling tools like get_product, search_products, and compare_prices.

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 clear context: it lists supported stores and is free (does not consume quota). However, it does not explicitly mention when not to use it or name alternatives, so it lacks 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?

With no annotations provided, the description carries the full burden of disclosing behavior. It only states the basic search action and return of details, omitting important traits such as pagination, latency, error handling, or permissions required for a tool that searches across many stores.

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, compact sentence that is front-loaded with the primary action and scope. It is concise with no unnecessary words, though 'hundreds of stores' is slightly vague but still contributes context. It earns a 4 for efficiency and structure.

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?

This is a search tool with no output schema and no annotations, yet the description does not explain the structure of returned matches, potential errors, or the effect of parameters like max_results. Given the complexity of searching across many stores, the description is insufficient for full agent understanding.

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 describes all four parameters with adequate detail (100% coverage), so the baseline is 3. The description adds no additional semantic meaning beyond the schema; it only loosely references 'region' and 'details' without clarifying parameter behavior or interactions.

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: searching for a product across many stores in a region and returning details for each match. It uses a specific verb and resource, and 'across hundreds of stores' differentiates it from get_product (single product) and compare_prices (price comparison), though it does not explicitly name these alternatives.

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?

No guidance is provided on when to use this tool versus siblings like get_product or compare_prices. The description implies a broad search use case but does not state exclusions, prerequisites, or conditions where another tool would be more appropriate.

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