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awslabs

amazon-datazone-mcp-server

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by awslabs

add_entity_owner

Assign an owner to a domain unit or project in Amazon DataZone by specifying domain, entity, and owner identifiers.

Instructions

Adds an owner to an entity (domain unit or project) in Amazon DataZone.

Args: domain_identifier (str): The ID of the domain entity_identifier (str): The ID or name of the entity (domain unit or project) to add the owner to owner_identifier (str): The identifier of the owner to add (can be IAM ARN for users) entity_type (str, optional): The type of entity (DOMAIN_UNIT or PROJECT, default: DOMAIN_UNIT) owner_type (str, optional): The type of owner (default: "USER") client_token (str, optional): A unique token to ensure idempotency

Returns: Any: The API response

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
owner_typeNoUSER
entity_typeNoDOMAIN_UNIT
client_tokenNo
owner_identifierYes
domain_identifierYes
entity_identifierYes
Behavior2/5

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

Without annotations, the description carries the full burden but only mentions idempotency via client_token. It does not disclose permissions, side effects, rate limits, or what happens if the owner already exists. The return value is vaguely described as 'Any: The API response.'

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 concise and front-loaded with the main purpose, followed by a clear Args section. One point deducted because the Args section could be more structured (e.g., bullet points) but the content is efficient and necessary.

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?

For a tool with 6 parameters and no output schema, the description covers the parameters well but lacks details about behavior (e.g., error cases, what the API response contains). Some behavioral context, like whether ownership replaces or appends, is missing.

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?

With 0% schema description coverage, the description fully compensates by explaining each parameter's purpose (e.g., owner_identifier 'can be IAM ARN for users', entity_type defaults and options). This provides essential meaning beyond the schema's bare titles.

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 verb 'Adds an owner' and the resource 'entity (domain unit or project)' in Amazon DataZone, providing a specific and unambiguous purpose that distinguishes it from sibling tools like add_policy_grant or create_project.

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 guidance is provided on when to use this tool versus alternatives such as add_policy_grant or create_project_membership. The decision is left entirely to the agent without explicit context 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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