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amazon-datazone-mcp-server

Official
by awslabs

create_data_source

Create a data source in Amazon DataZone and link it to a project for data ingestion, scheduling, and asset publishing.

Instructions

Creates a data source in Amazon DataZone and associates it with a project.

Args: domain_identifier (str): The ID of the domain where the data source is created project_identifier (str): The ID of the project to associate the data source with name (str): The name of the data source (1-256 characters) data_src_type (str): The type of data source (e.g., "S3", "GLUE", "REDSHIFT") description (str, optional): Description of the data source (0-2048 characters) enable_setting (str, optional): Whether the data source is enabled (ENABLED/DISABLED) environment_identifier (str, optional): ID of the environment to publish assets to connection_identifier (str, optional): ID of the connection to use configuration (Dict[str, Any], optional): Data source configuration Example for S3: { "s3Configuration": { "bucketName": "my-bucket", "prefix": "data/" } } asset_forms_input (List[Dict[str, str]], optional): Metadata forms for assets Example: [{ "content": "form-content", "formName": "form-name", "typeIdentifier": "type-id", "typeRevision": "type-rev" }] publish_on_import (bool, optional): Whether to automatically publish imported assets recommendation (Dict[str, bool], optional): Recommendation settings Example: {"enableBusinessNameGeneration": True} schedule (Dict[str, str], optional): Schedule configuration Example: { "schedule": "cron(0 12 * * ? *)", "timezone": "UTC" } client_token (str, optional): Token for idempotency

Returns: Any: The API response containing: - Data source ID and status - Creation and update timestamps - Domain and project IDs - Configuration details - Last run information - Error messages (if any)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
scheduleNo
descriptionNo
client_tokenNo
configurationNo
data_src_typeYes
enable_settingNoENABLED
recommendationNo
asset_forms_inputNo
domain_identifierYes
publish_on_importNo
project_identifierYes
connection_identifierNo
environment_identifierNo
Behavior4/5

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

With no annotations, the description carries the full burden. It details the creation action, required parameters, and return value including error messages. It mentions idempotency via client_token. However, it does not mention prerequisites like required permissions or domain/project existence.

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 long but well-structured with Args and Returns sections. Every sentence adds value, but it could be slightly more concise by condensing some examples. Overall, it is effective.

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?

Given the complexity (14 parameters, 0% schema coverage, no output schema), the description is highly complete. It covers all parameters, provides examples, and explains the return value. No critical gaps.

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%, so the description must compensate. It provides detailed explanations for all 14 parameters, including examples for complex types (configuration, asset_forms_input, recommendation, schedule). This goes beyond what the schema provides.

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 data source in Amazon DataZone and associates it with a project.' This is a specific verb and resource. There are no sibling tools that create a data source, so no confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly indicates when to use this tool (when a data source needs to be created). It does not explicitly state when not to use or alternatives, but given the context of sibling tools, the usage is straightforward.

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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