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amazon-datazone-mcp-server

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by awslabs

create_connection

Create a connection in Amazon DataZone to link your domains, projects, and environments to external resources and services.

Instructions

Creates a new connection in Amazon DataZone. A connection enables you to connect your resources.

(domains, projects, and environments) to external resources and services.

This is specifically for creating DataZone connections and should be used in the DataZone MCP server.

Args: domain_identifier (str): The ID of the domain where the connection is created. Pattern: ^dzd[-][a-zA-Z0-9-]{1,36}$ name (str): The connection name. Length Constraints: Minimum length of 0. Maximum length of 64. environment_identifier (str, optional): The ID of the environment where the connection is created. Pattern: ^[a-zA-Z0-9_-]{1,36}$ aws_location (Dict[str, str], optional): The location where the connection is created. Contains: - accessRole (str): The access role for the connection - awsAccountId (str): The AWS account ID - awsRegion (str): The AWS region - iamConnectionId (str): The IAM connection ID description (str, optional): A connection description. Length Constraints: Minimum length of 0. Maximum length of 128. client_token (str, optional): A unique, case-sensitive identifier to ensure idempotency. props (Dict[str, Any], optional): The connection properties. Type: ConnectionPropertiesInput object (Union type)

Returns: Any: The API response containing: - connectionId (str): The ID of the created connection - description (str): The connection description - domainId (str): The domain ID - domainUnitId (str): The domain unit ID - environmentId (str): The environment ID - name (str): The connection name - physicalEndpoints (list): The physical endpoints of the connection - projectId (str): The project ID - props (dict): The connection properties - type (str): The connection type

Example: >>> create_connection( ... domain_identifier="dzd_4p9n6sw4qt9xgn", ... name="MyConnection", ... environment_identifier="env_123456789", ... aws_location={ ... "accessRole": "arn:aws:iam::123456789012:role/DataZoneAccessRole", ... "awsAccountId": "123456789012", ... "awsRegion": "us-east-1", ... "iamConnectionId": "iam-123456789", ... }, ... description="Connection to external service", ... )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
propsNo
descriptionNo
aws_locationNo
client_tokenNo
domain_identifierYes
environment_identifierNo
Behavior4/5

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

No annotations are provided, so the description carries full burden. It details parameters, return structure with example, and implies idempotency via client_token. However, it lacks explicit notes on error conditions, permissions, or side effects beyond creation.

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 well-structured with intro, args docstring, returns, and example. It is front-loaded with purpose. While slightly verbose, every section adds value and the structure aids readability.

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 7-parameter tool with no output schema, the description covers input, return values, and gives an example. Missing are error handling and potential side effects, but overall it is fairly complete for an agent to invoke correctly.

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 adds extensive parameter semantics: patterns, length constraints, dictionary structure for aws_location, and explanation of props type. This far exceeds 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 a new connection in Amazon DataZone' and specifies the context as the DataZone MCP server. Among siblings like get_connection and list_connections, the creation purpose is distinct.

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 indicates it's for creating DataZone connections within the DataZone MCP server, but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives like update or other creation tools.

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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