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awslabs

amazon-datazone-mcp-server

Official
by awslabs

create_asset

Creates a data asset in the Amazon DataZone catalog with metadata forms and glossary terms to organize and govern your data.

Instructions

Creates an asset in the Amazon DataZone catalog.

Args: domain_identifier (str): The ID of the domain where the asset is created name (str): The name of the asset (1-256 characters) type_identifier (str): The ID of the asset type (1-513 characters) owning_project_identifier (str): The ID of the project that owns this asset description (str, optional): Description of the asset (0-2048 characters) external_identifier (str, optional): External ID of the asset (1-600 characters) forms_input (List[Dict[str, str]], optional): Metadata forms for the asset Example: [{ "content": "form-content", "formName": "form-name", "typeIdentifier": "type-id", "typeRevision": "type-rev" }] glossary_terms (List[str], optional): Glossary terms to attach to the asset Example: ["term1", "term2"] prediction_configuration (Dict[str, Dict[str, bool]], optional): Configuration for business name generation Example: {"businessNameGeneration": {"enabled": True}} type_revision (str, optional): The revision of the asset type client_token (str, optional): Token for idempotency

Returns: Any: The API response containing: - Asset ID and revision - Creation timestamps - Domain and project IDs - Forms and metadata - Glossary terms - Listing status - Time series data points

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
descriptionNo
forms_inputNo
client_tokenNo
type_revisionNo
glossary_termsNo
type_identifierYes
domain_identifierYes
external_identifierNo
prediction_configurationNo
owning_project_identifierYes
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It describes the creation and return values but omits side effects (e.g., whether asset is immediately published), permission requirements, or error conditions. This is insufficient for a mutation tool.

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

Conciseness3/5

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

The description is structured with Args and Returns sections, but it is verbose (10+ lines). Some parameter details could be condensed without losing clarity. It is functional but not optimally 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?

Given the complexity (11 params, no output schema) and no annotations, the description covers creation details and return structure comprehensively. However, it lacks information on prerequisites or potential failures, which slightly reduces completeness.

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?

Schema description coverage is 0%, yet the description manually enumerates all 11 parameters with constraints (e.g., name length 1-256), types, and examples (e.g., forms_input, glossary_terms). This adds full semantic meaning 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 'Creates an asset in the Amazon DataZone catalog,' using a specific verb and resource. This distinguishes it from sibling tools like create_glossary or create_project, which target different entities.

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 does not provide explicit guidance on when to use this tool versus alternatives like publish_asset or search. Usage is implied only through the action of creating an asset, but no when-not-to or prerequisites are mentioned.

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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