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amazon-datazone-mcp-server

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by awslabs

get_domain_unit

Fetch details of a domain unit in Amazon DataZone, including ownership, creation date, and parent unit.

Instructions

Retrieves detailed information about a specific domain unit in Amazon DataZone.

Args: domain_identifier (str): The ID of the domain where the domain unit exists Pattern: ^dzd[-][a-zA-Z0-9-]{1,36}$ identifier (str): The ID of the domain unit to retrieve Pattern: ^[a-z0-9_-]+$

Returns: Dict containing: - id: Domain unit identifier - name: Domain unit name - description: Domain unit description - domain_id: Domain ID - parent_domain_unit_id: Parent domain unit ID - created_at: Creation timestamp - created_by: Creator information - owners: List of domain unit owners - lastUpdatedAt: The timestamp at which the domain unit was last updated - lastUpdatedBy: The user who last updated the domain unit

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
identifierYes
domain_identifierYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are present, so the description must fully disclose behavioral traits. It states the tool retrieves info but does not mention permissions, error conditions, idempotency, or side effects. While 'get' implies safety, the lack of explicit behavioral context is a gap without annotations.

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 concise yet comprehensive: a one-line purpose followed by structured Args and Returns sections. Each sentence serves a purpose, and the format is easy to parse. No superfluous text exists.

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?

For a simple retrieval tool with two parameters, the description covers input (with patterns) and output (a detailed dict). It is nearly complete, though it lacks mention of potential errors or auth requirements. With output schema present (implied by the Returns section), the description is thorough enough for this complexity level.

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?

With 0% schema description coverage, the description adds significant value: it explains each parameter including regex patterns ('domain_identifier' pattern '^dzd[-_][a-zA-Z0-9_-]{1,36}$') and natural language descriptions. This compensates fully for the schema's lack of descriptions, though it could include examples or defaults.

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 opening line 'Retrieves detailed information about a specific domain unit in Amazon DataZone' clearly states the action and resource. This distinguishes it from siblings like 'list_domain_units' (which lists multiple) and 'create_domain_unit' (which creates), establishing a unique purpose.

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, such as 'list_domain_units' for multiple units or other retrieval tools. It lacks any context about prerequisites, fallbacks, or when not to use, leaving the agent without decision-making support.

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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