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awslabs

amazon-datazone-mcp-server

Official
by awslabs

accept_subscription_request

Accept a subscription request to an Amazon DataZone asset, specifying optional asset scopes and a decision comment.

Instructions

Accepts a subscription request to a specific asset in Amazon DataZone.

Args: domain_identifier (str): The ID of the domain where the subscription request exists identifier (str): The unique identifier of the subscription request to accept asset_scopes (List[Dict[str, Any]], optional): The asset scopes of the accept subscription request Example: [{"assetId": "asset-id", "filterIds": ["filter-id"]}] decision_comment (str, optional): A description that specifies the reason for accepting the request Length: 1-4096 characters

Returns: Any: The API response containing: - Subscription request ID and status - Creation and update timestamps - Domain ID - Decision comment - Subscribed listings and principals - Metadata forms - Reviewer information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
identifierYes
asset_scopesNo
decision_commentNo
domain_identifierYes
Behavior2/5

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

No annotations provided; description only states the action without disclosing implications like permissions, state changes, or failure conditions.

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?

Structured with Args and Returns sections. Sentences earn their place, though Returns section could be slightly more concise. Overall well-organized.

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?

Completes the picture with parameter details, example, constraints, and return fields. Adequate given no output schema and moderate complexity.

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 coverage is 0%, but description adds explanations for all parameters including examples for 'asset_scopes' and length constraint for 'decision_comment'.

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?

Clearly states the verb 'Accepts' and the resource 'subscription request to a specific asset in Amazon DataZone'. Distinguishes from sibling tool 'create_subscription_request'.

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?

No explicit guidance on when to use this tool versus alternatives. Does not mention prerequisites like an existing pending request.

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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