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get_warehouse_locations

Find nearby Costco warehouses by entering a ZIP code, city, or state to locate stores for shopping and membership services.

Instructions

Find nearby Costco warehouses by ZIP code or city

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zip_codeNoZIP code to search near
cityNoCity name to search near (alternative to zip_code)
stateNoState abbreviation (e.g., CA, WA)
limitNoMaximum number of warehouses to return (default: 5)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool's function but lacks critical behavioral details: it doesn't specify whether this requires authentication (unlike tools like 'login' or 'get_orders'), what the return format is (e.g., list of locations with addresses), distance radius for 'nearby', error handling, or rate limits. For a read operation with zero annotation coverage, this is a significant gap.

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, efficient sentence that front-loads the core purpose ('Find nearby Costco warehouses') and specifies the search method ('by ZIP code or city'). There is zero wasted verbiage, and every word contributes directly to understanding the tool's function.

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

Completeness2/5

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

Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is incomplete. It adequately states what the tool does but fails to provide necessary context: no information on authentication requirements, return format, error conditions, or how 'nearby' is defined. Without annotations or output schema, the agent lacks guidance on how to interpret results or handle failures.

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 fully documents all four parameters (zip_code, city, state, limit). The description adds minimal value beyond the schema by mentioning 'ZIP code or city' as search inputs, but doesn't explain parameter interactions (e.g., that city and state might be used together, or that zip_code alone may suffice). Baseline 3 is appropriate since the schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the action ('Find nearby') and resource ('Costco warehouses') with the search method ('by ZIP code or city'). It distinguishes from most siblings like 'check_warehouse_stock' or 'search_products' by focusing on location lookup rather than inventory or product search. However, it doesn't explicitly differentiate from potential location-related tools that might not exist in this set.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., whether authentication is needed), when not to use it, or how it compares to siblings like 'get_delivery_options' which might also involve location data. The agent must infer usage from the tool name and description alone.

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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