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amazon-datazone-mcp-server

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by awslabs

get_environment

Retrieve details of an Amazon DataZone environment by specifying the domain ID and environment ID.

Instructions

Gets an Amazon DataZone environment.

Args: domain_identifier (str): The ID of the domain where the environment exists. Pattern: ^dzd[-][a-zA-Z0-9-]{1,36}$ identifier (str): The ID of the environment to retrieve. Length Constraints: Minimum length of 0. Maximum length of 128.

Returns: Any: The API response containing: - awsAccountId (str): The AWS account ID associated with the environment. - awsAccountRegion (str): The AWS region where the environment is located. - createdAt (str): Timestamp when the environment was created. - createdBy (str): The identifier of the user who created the environment. - deploymentProperties (dict): Properties related to deployment, including: - endTimeoutMinutes (int): Timeout in minutes for ending the deployment. - startTimeoutMinutes (int): Timeout in minutes for starting the deployment. - description (str): Description of the environment. - domainId (str): The domain ID associated with the environment. - environmentActions (list): A list of actions for the environment, each containing: - auth (str): Authorization type for the action. - parameters (list): Parameters for the action, each including: - key (str): Parameter key. - value (str): Parameter value. - type (str): The type of environment action. - environmentBlueprintId (str): ID of the blueprint used for the environment. - environmentConfigurationId (str): ID of the environment configuration. - environmentProfileId (str): ID of the environment profile. - glossaryTerms (list): List of glossary term strings associated with the environment. - id (str): The unique ID of the environment. - lastDeployment (dict): Information about the last deployment, including: - deploymentId (str): ID of the last deployment. - deploymentStatus (str): Status of the deployment. - deploymentType (str): Type of deployment. - failureReason (dict): Details of any failure, including: - code (str): Error code for the failure. - message (str): Human-readable error message. - isDeploymentComplete (bool): Whether the deployment is complete. - messages (list): List of messages related to the deployment. - name (str): Name of the environment. - projectId (str): The project ID associated with the environment. - provider (str): Provider responsible for provisioning the environment. - provisionedResources (list): List of provisioned resources, each including: - name (str): Name of the resource. - provider (str): Resource provider. - type (str): Type of the resource. - value (str): Value associated with the resource. - provisioningProperties (dict): Additional properties used during provisioning. - status (str): Current status of the environment. - updatedAt (str): Timestamp when the environment was last updated. - userParameters (list): Parameters provided by the user, each including: - defaultValue (str): Default value of the parameter. - description (str): Description of the parameter. - fieldType (str): Type of input field. - isEditable (bool): Whether the parameter is editable. - isOptional (bool): Whether the parameter is optional. - keyName (str): Key name for the parameter.

Example: >>> get_environment( ... domain_identifier="dzd_4p9n6sw4qt9xgn", identifier="conn_123456789" ... )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
identifierYes
domain_identifierYes
Behavior2/5

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

No annotations are provided, so the description must carry the burden for behavioral traits. It only states 'Gets' which implies read-only, but does not disclose idempotency, prerequisites, or any special behaviors beyond the return structure.

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 organized with bullet points and an example, making it easy to parse. While it is lengthy due to detailed return fields, it is appropriately structured for a complex output.

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?

With no output schema, the description fully documents the return object fields. Input parameters are thoroughly described, and an example is provided, making the tool complete for agent invocation.

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?

The input schema has 0% description coverage, but the description provides detailed parameter semantics including types, patterns, and constraints for both domain_identifier and identifier, significantly adding value.

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 an Amazon DataZone environment', which is a specific verb and resource. It distinguishes from sibling tools like get_asset, get_connection, and list_environments, making its purpose unambiguous.

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 explicitly state when to use this tool versus alternatives such as list_environments for multiple environments. While the context implies retrieval by ID, more explicit guidance would improve clarity.

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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