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kavingas

Catalog Services MCP Server

by kavingas

find_store_view_codes

Find store view codes by filtering with optional website and store codes to discover available options for a given environment.

Instructions

Find and filter store views by website and/or store code.

Useful for discovering available store view codes when you know the
website and store codes but need to find valid store view codes.

Args:
    environment_id: Adobe Commerce Cloud environment ID
    website_code: Optional website code to filter by
    store_code: Optional store code to filter by
    timeout: Request timeout in seconds (default: 15.0)
    
Returns:
    Dict containing:
    - status: Success or error status
    - environment_id: The queried environment ID
    - matching_store_views: List of matching store views
    - count: Number of matching store views
    
Example:
    >>> result = find_store_view_codes(
    ...     environment_id='7059bb71-341a-4ccb-b543-d7b2948b73e4',
    ...     website_code='base',
    ...     store_code='main_website_store'
    ... )
    >>> print([sv['storeViewCode'] for sv in result['matching_store_views']])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
timeoutNo
store_codeNo
website_codeNo
environment_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Describes return structure and includes an example, which is helpful. But with no annotations, it does not explicitly state read-only nature or potential side effects, leaving some ambiguity.

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

Conciseness4/5

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

Well-structured with sections and an example, but the first two sentences are slightly redundant. Overall, it is appropriately concise.

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

Completeness4/5

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

Provides purpose, parameters, return structure, and an example. For a query tool, this is adequate. Lacks error handling details but not critical.

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

Parameters5/5

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

Since schema coverage is 0%, the description fully compensates by clearly explaining each parameter's meaning, including defaults and role (filter by, timeout).

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?

Clearly states the tool finds and filters store views by website and/or store code, with a specific use case. However, it does not explicitly differentiate from sibling tool get_environment_store_views, which may also list store views.

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?

Provides a specific use case for discovering store view codes when website and store codes are known. However, it does not discuss when not to use this tool or mention alternatives explicitly.

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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