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awslabs

amazon-datazone-mcp-server

Official
by awslabs

get_project

Retrieve detailed metadata, configuration, and status of an Amazon DataZone project to audit deployments, roles, or compliance.

Instructions

Retrieves detailed information, metadata and configuration, of a specific project in Amazon DataZone.

Use this API when the user is asking about a known project by name or context and wants to:

  • View deployment status, user roles, or configurations

  • Audit metadata for compliance or review

Returns: Any: The API response containing project details including: - Basic info (name, description, ID) - Timestamps (createdAt, lastUpdatedAt) - Domain IDs (domainId, domainUnitId) - Project status and profile - Environment deployment details - User parameters - Glossary terms - Failure reasons (if any)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domain_identifierYes
project_identifierYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes the return data but does not disclose side effects, rate limits, permissions, or idempotency. As a read operation, the lack of such details is acceptable but not exemplary.

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 front-loaded with purpose and use cases, but the list of return fields is somewhat verbose and could be trimmed. It is structured but not maximally concise, earning a middling score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema and annotations, the description should provide more context about parameters, error scenarios, and prerequisites. It covers return fields but leaves gaps in parameter semantics and behavioral context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It mentions 'known project by name or context' but the schema uses identifiers, not names. The description does not explain what the parameters represent or how to obtain them, leaving ambiguity.

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 uses a specific verb 'Retrieves' and resource 'project' in Amazon DataZone, clearly distinguishing it from sibling tools like 'list_projects' and other get_* tools. It also specifies the type of information returned, making the 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 Guidelines4/5

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

The description explicitly states when to use the tool: when the user is asking about a known project and wants to view deployment status, user roles, or audit metadata. It does not provide explicit 'when not to use' or name alternatives, but the context is clear enough.

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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