List Anti-Pattern Coffee Shops

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

While no annotations are provided, the description discloses that entries 'show why it's flagged,' revealing output structure. It also details the inclusion criteria (mass-market chains, flavor-led specialty, etc.), giving the agent understanding of what to expect. This is meaningful behavioral context beyond a simple list description.

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, with the primary action and purpose in the first sentence. It efficiently packs relevant detail (inclusion criteria, output note, use cases) without redundancy or fluff.

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?

As a simple list tool with two optional parameters and no output schema, the description provides sufficient information: it explains the tool's scope, the nature of list items (with reasons), and practical use cases. Combined with the schema's parameter coverage, this is complete for the task.

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 covers both parameters fully, but the description adds semantic meaning by defining the 'flavor-led specialty' category in prose ('display third-wave signage but lean heavily on flavored drinks') and providing examples like Starbucks, Dunkin', and Costa for mass-market chains. This enriches the enum values beyond their schema 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 uses a specific verb ('List') and defines the resource as anti-pattern coffee shops, explicitly stating they 'exemplify what to AVOID when looking for great espresso.' It further distinguishes from siblings by mentioning mass-market chains and flavor-led specialty shops, clearly contrasting with list_great_roasters. This provides a clear, unambiguous purpose.

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 states two explicit use cases: 'Useful as contrast when recommending real specialty cafes' and 'as regression fixtures for the scoring algorithm.' It gives clear context for when to leverage this tool, although it does not name alternative tools or explicitly state when not to use it.

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