coffee-addict

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

With no annotations, the description carries the full burden, and it does well by disclosing two useful behaviors: every row carries the reason it is on the list, and an empty list carries the reason it is empty. It does not mention side effects, but this is clearly a read-style tool and the added output guarantees are genuinely informative.

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 only two sentences, with no filler. The core value proposition is front-loaded, and the second sentence adds a useful output guarantee that 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 low-complexity tool with two optional parameters and no output schema, the description is reasonably complete: it covers the listing categories, geographic scope, and important output behavior. Minor gaps like ordering and explicit parameter mapping exist, but they do not prevent correct 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?

Schema description coverage is 0%, so the description needs to compensate. It partially does by naturally naming the categories that map to price_cuts, just_dropped, and cheapest_per_100g. However, it never explicitly ties those terms to the kind parameter and says nothing about limit or the 'all' option.

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 as a source of today's price cuts, new arrivals, and cheapest-per-100g coffee listings across New Zealand specialty coffee. It does not use an explicit verb like 'list' or 'get,' but the intent is unmistakable and distinct from the sibling tools.

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 this tool is for surfacing deal-style listings, but it never explicitly states when to use best_deals versus roaster_info or search_beans. The sibling names provide clues, yet the description leaves the choice to inference rather than giving explicit 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 provided, the description carries the behavioral disclosure burden. It frames the tool as an informational lookup and lists the returned fields, implying a read-only operation. Still, it does not mention error behavior, data freshness, or what exactly happens when `roaster` is omitted versus provided.

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 one dense, front-loaded sentence: it names the resource, lists the key output attributes, and ends with the critical calling convention. Every phrase contributes value and there is 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?

For a read-only lookup with two optional parameters, the description adequately summarizes the main output. However, with no output schema and no annotations, an agent still lacks clarity on the exact response shape and failure behavior, so the definition is only minimally complete.

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%, so the baseline is 3. The description adds useful context around `roaster` by explaining the omit-for-all behavior and clarifies the per-100g metric, but it does not substantially expand on the schema's parameter 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 identifies the resource as the tracked New Zealand specialty roasters and enumerates the specific data returned: bag counts, cheapest per 100g, shipping terms, and roast location. It lacks an explicit verb and does not differentiate from siblings, but the purpose is unambiguous.

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 instruction to omit `roaster` for all roasters is a concrete usage rule. However, the description does not explicitly state when to choose this tool over `best_deals` or `search_beans`, leaving the routing decision mostly to inference from the tool name and scope.

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 disclosure burden. It usefully reveals the scope ('every bag... on sale in New Zealand') and the sort order ('cheapest per 100g first'), but it does not mention pagination, default stock filtering, or response shape.

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?

Two sentences with no filler. The search scope and ordering rule are front-loaded, and every clause adds information relevant to calling the tool correctly.

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 covers scope, filters, and ordering, which is good for a search tool. However, with no output schema and no mention of pagination, limit defaults, or result format, an agent cannot fully predict what the tool returns or how many results to expect.

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 low (38%), so the description adds some value by clarifying that the free-text query covers name, roaster, origin, and tasting notes, and by listing filterable dimensions. However, it does not clarify the difference between max_price_nzd and max_price_per_100g or the behavior of limit and in_stock_only.

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 states a specific verb ('Search'), a precise resource ('every bag of specialty coffee on sale in New Zealand'), the available filter dimensions, and the result ordering. This clearly distinguishes the tool from siblings like best_deals and roaster_info even without naming them.

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 use case is implied by the search-and-filter phrasing, but there is no explicit guidance on when to prefer this tool over best_deals or roaster_info, nor when not to use it. An agent would have to infer the boundary from tool names alone.

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