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awslabs

amazon-datazone-mcp-server

Official
by awslabs

search_listings

Search published data asset listings in Amazon DataZone using keywords, filters, and sorting options to find relevant assets.

Instructions

Search published data asset listings in Amazon DataZone using keyword, filter, and sort options.

Use it to search only within published data asset listings.

related tools:

  • search: Use only when the user needs general discovery across all entity types (e.g., glossary terms, data products).

Args: domain_identifier (str): The ID of the domain to search in search_text (str, optional): Text to search for max_results (int, optional): Maximum number of results to return (1-50, default: 50) next_token (str, optional): Token for pagination additional_attributes (List[str], optional): Additional attributes to include in search Valid values: ["FORMS", "TIME_SERIES_DATA_POINT_FORMS"] search_in (List[Dict[str, str]], optional): Attributes to search in Example: [{"attribute": "name"}, {"attribute": "description"}] sort (Dict[str, str], optional): Sorting criteria Example: {"attribute": "name", "order": "ASCENDING"}

Returns: Any: The API response containing search results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNo
search_inNo
next_tokenNo
max_resultsNo
search_textNo
domain_identifierYes
additional_attributesNo
Behavior4/5

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

Without annotations, the description carries the burden. It correctly implies a read-only operation through 'search' and describes input behavior but does not cover rate limits, authentication, or detailed side effects. However, for a search tool, this is sufficient.

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

Conciseness5/5

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

The description is well-organized: a single-sentence purpose, followed by usage guidance, related tools, and a clear list of arguments. Every sentence adds value without redundancy.

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?

Covers purpose, usage, and parameters comprehensively. However, the return value is only described as 'API response containing search results' without specific fields or example, which could be improved given the absence of an output schema.

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 description includes a detailed 'Args' section that explains each parameter's type, optionality, and purpose, plus valid values and examples for complex parameters (e.g., additional_attributes, search_in, sort). This compensates for the 0% schema description coverage.

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 the tool's purpose: 'Search published data asset listings' with keyword, filter, and sort options. It distinguishes itself from the sibling 'search' tool by specifying that it is limited to published listings.

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

Usage Guidelines5/5

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

Explicitly advises when to use this tool ('search only within published data asset listings') and contrasts with the 'search' tool for general discovery, providing clear guidance on alternatives.

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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