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awslabs

amazon-datazone-mcp-server

Official
by awslabs

get_subscription

Retrieve details of a subscription in Amazon DataZone, including its status, timestamps, and associated listing and principal information.

Instructions

Gets a subscription in Amazon DataZone.

Args: domain_identifier (str): The ID of the Amazon DataZone domain in which the subscription exists Pattern: ^dzd[-][a-zA-Z0-9-]{1,36}$ identifier (str): The ID of the subscription Pattern: ^[a-zA-Z0-9_-]{1,36}$

Returns: Any: The API response containing: - Subscription ID and status (APPROVED | REVOKED | CANCELLED) - Creation and update timestamps - Domain ID - Retain permissions flag - Subscribed listing details - Subscribed principal information - Subscription request ID - Creator and updater information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
identifierYes
domain_identifierYes
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses the return structure in detail (status, timestamps, listing details, etc.). However, it does not mention authentication, rate limits, or that it is a read-only operation, which is implicit.

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?

The description is well-structured with Args and Returns sections. While informative, it could be slightly more concise by moving patterns to the schema's description fields, but it remains focused.

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

Completeness5/5

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

For a simple get tool with two parameters and no output schema, the description covers input patterns comprehensively and details the return structure fully. It is contextually complete.

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

Parameters4/5

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

Schema description coverage is 0%, but the description adds pattern constraints for both parameters (domain_identifier and identifier) and explains the return structure. This adds meaningful value beyond the bare schema.

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 'Gets a subscription in Amazon DataZone,' using a specific verb and resource. It distinguishes from siblings like create_subscription_request or accept_subscription_request, which are mutation tools.

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 for retrieving a subscription's details but does not explicitly state when to use it versus alternatives or when not to use it. No callouts for 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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