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find_nearby_vending_machines

Locate Carvana vehicle pickup points near a specified city or zip code to facilitate customer vehicle collection.

Instructions

Find Carvana car vending machine locations near a given city or zip code where customers can pick up their purchased vehicles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zip_codeNoZip code to search near
cityNoCity name to search near
stateNoState abbreviation (e.g. CA, TX, FL)
radius_milesNoSearch radius in miles (default 50)
Behavior2/5

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. It describes the tool's function but does not mention behavioral traits such as whether it requires authentication, rate limits, error handling, or the format of returned results (e.g., list of locations with details). This leaves gaps in understanding how the tool behaves in practice.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently conveys the tool's purpose, resource, and context without unnecessary words. It is front-loaded with the core action and resource, making it easy to understand at a glance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and 4 parameters with full schema coverage, the description is adequate but incomplete. It explains what the tool does but lacks details on behavioral aspects (e.g., authentication, result format) and does not compensate for the absence of output schema, leaving uncertainty about the return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters (zip_code, city, state, radius_miles) with descriptions. The description adds no additional parameter semantics beyond implying location-based search, which is already covered by the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Find'), resource ('Carvana car vending machine locations'), and scope ('near a given city or zip code'), with explicit mention of the purpose ('where customers can pick up their purchased vehicles'). It distinguishes itself from sibling tools like 'check_delivery_availability' or 'search_vehicles' by focusing on physical pickup locations rather than delivery or vehicle listings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('near a given city or zip code') and the purpose ('pick up their purchased vehicles'), suggesting it's for post-purchase logistics. However, it lacks explicit guidance on when to use this tool versus alternatives like 'check_delivery_availability' for delivery options or 'search_vehicles' for vehicle discovery, and does not specify prerequisites or exclusions.

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

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