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

No annotations are provided, so the description must carry the transparency burden. It reveals that the tool scrapes live pages and that Best Buy blocks access, but it does not disclose other potential behavioral issues like rate limiting, IP blocks from other retailers, or terms-of-service risks. The description is partially transparent but lacks a fuller safety and reliability disclosure.

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 well-structured: three sentences, each adding meaningful information. It leads with the core action, then differentiates from siblings, and ends with an important caveat. 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?

Given the simplicity of the tool (3 parameters, no output schema), the description provides good context: it explains the geographic scope, the direct scraping behavior, and a known blocker. However, it does not describe the return value structure or failure handling, which for a scraping tool could be relevant. Still, the core context is sufficiently covered.

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 the baseline is 3. The description does not add detailed meaning beyond the schema parameters; it only reinforces that the retailers are Canadian. The schema already explains query, limit, and retailers, so the description adds little incremental 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's function: scraping Canadian retailer search pages to find the actual lowest price. It uses specific verbs ('scrape', 'find') and distinguishes itself from sibling product_search by noting it reads live retailer pages, making it more accurate.

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 contrasts with product_search ('More accurate than product_search because it reads live retailer pages'), providing clear guidance on when to prefer this tool. It also warns that Best Buy Canada blocks automated access, implying not to expect results from that retailer.

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 of behavioral disclosure. The description merely restates the tool's purpose ('Hunt for active deals') and adds synonyms like 'promo codes' and 'discounts,' but it does not disclose whether results are filtered by activeness automatically, whether coupon validity is verified, or what the return structure looks like. This is a significant transparency gap beyond what the name and schema already convey.

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 with no redundant or filler content. It is front-loaded with the core action and resource. Every phrase contributes meaning, and it is appropriately sized for a simple search tool.

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 tool has 4 parameters, no output schema, and no annotations. The description covers the core query intent but omits return-value details (e.g., does it return a list of deals with prices and coupon codes?), pagination/limit behavior, and regional defaults. Schema descriptions fill gaps on parameters, but the absence of output specification and usage context leaves the overall description only minimally complete for an agent to understand results and boundaries.

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 descriptions for all 4 parameters, covering 100% of them. The description itself adds no additional parameter-level detail beyond what the schema already contains. According to the rubric, when schema coverage is high (>80%), the baseline is 3, and there is no evidence of additional semantic value in the description.

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 clear action verb ('Hunt') and specifies the resource: active deals, promo codes, and discounts for a product or category. This distinguishes find_deals from sibling tools like product_search (searching products) and find_best_price (comparing prices). However, 'Hunt' is slightly informal, and the description does not explicitly contrast it with these siblings, so it falls short of a perfect 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 implies the tool is for finding deals and discounts, and the query parameter is described as 'Product, brand, or category to find deals for.' There is no explicit guidance on when to choose this tool over alternatives like find_best_price or price_history, nor any exclusionary statements. This meets the 'implied usage' level but lacks explicit when/when-not 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?

With no annotations available, the description carries the full burden of behavioral disclosure. It states the tool 'gets guidance' but does not describe the output format, whether it calls other tools, or any side effects. This lack of information leaves the agent uncertain about the tool's actual 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 front-loads the primary purpose with no redundant words. It efficiently communicates the tool's function without unnecessary elaboration.

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 gives the essential purpose but lacks details about what the guidance output looks like or how it interacts with sibling tools. Given the absence of an output schema and annotations, the description is minimally sufficient but leaves ambiguity about the tool's actual deliverable.

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 for the single 'scenario' parameter, including a description and example. The tool description itself adds no parameter-specific information, but since the schema is self-explanatory, the 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 clearly states the tool provides guidance on using Makiti tools for shopping decisions, with a specific verb ('Get guidance') and resource ('how to use Makiti tools'). This distinguishes it from sibling tools which directly perform shopping actions like search, compare, or find 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?

No guidance is provided on when to use this tool versus the sibling tools. There is no mention of alternatives, exclusions, or specific scenarios that would trigger its use. The description only implies a purpose without contextual usage direction.

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 must carry behavioral transparency. It mentions 'track or estimate' but doesn't explain what estimation entails, what data sources are used, or what the return format looks like, leaving significant gaps for an agent that needs to interpret results.

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 starts with the core action and includes a purpose clause. It is concise, front-loaded, and every word 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?

With no annotations and no output schema, the description should explain what the tool returns and how to interpret it. It only states the high-level purpose, leaving the agent uncertain about the result structure (e.g., time series, verdict, or estimate). This is incomplete for effective use.

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 has 100% description coverage for all three parameters (product, days_back, retailers). The tool description adds no extra meaning beyond the schema, so the baseline score 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 tracks or estimates price history to determine if a current price is a good deal, giving a specific verb and resource. However, it doesn't explicitly differentiate it from sibling tools like find_best_price or product_compare, which could also be used for deal evaluation.

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 'to determine if current price is a good deal' implies a clear context for when to use the tool. Yet there is no mention of when not to use it, and no alternatives are named among the sibling tools, leaving the guidance at an implied level.

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 is the sole source of behavioral information. It discloses the content of the comparison (specs, prices, pros/cons, verdict) which gives some insight into return format, but it does not mention side effects, data freshness, or any limitations such as whether it performs live lookups.

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, front-loaded sentence with no redundant words. Every element (compare, two products, output types) contributes to understanding.

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 tool is simple with 100% schema coverage and no output schema, but the description compensates by listing the returned comparison elements (specs, prices, pros/cons, verdict). It could be more complete by noting how optional parameters like budget affect the analysis, but it is adequate for a basic comparison 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?

All four parameters have descriptions in the schema, which covers 100% of parameter semantics. The description does not add extra meaning beyond the schema, as it only mentions 'two products' without detailing how budget or category influence the comparison.

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 specific verb 'Compare' and resource 'two products', and enumerates the output dimensions (specs, prices, pros/cons, verdict). This clearly differentiates it from sibling tools like product_search and find_best_price.

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 the use case of comparing exactly two products side-by-side, which is clear from the first phrase. However, it does not explicitly state when to prefer this over siblings like find_best_price or price_history, nor does it mention exclusions, so it lacks explicit usage boundaries.

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 carries the full burden of behavioral disclosure, but it only says it returns product data. It does not mention whether this is a read-only operation, any rate limits, pagination, or other behaviors. Minimal context beyond the core action.

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-loading the action and returning valuable result details without any redundancy. Every word 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?

Given the tool has 7 parameters but all are schema-documented, and there is no output schema, the description provides a basic understanding but lacks usage context and behavioral details. It is adequate but has clear gaps for an agent to fully understand when and how to invoke 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?

The schema provides full descriptions for all 7 parameters, so the baseline is 3. The description adds no additional parameter-specific meaning beyond the schema, though it implies filters like price and retailer are part of the search context.

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 for products across the web and lists the types of returned data (names, prices, retailers, links). It is specific with a verb and resource, but it does not explicitly differentiate from sibling tools like find_best_price or find_deals.

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 alternatives such as product_compare or price_history. There are no exclusions, prerequisites, or contextual hints about appropriate use cases.

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