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amazon-datazone-mcp-server

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by awslabs

list_data_sources

Browse and search data sources in an Amazon DataZone domain by project, type, status, or name. Use filters to find and select data sources without knowing their exact IDs.

Instructions

Retrieve a list of data sources in Datazone domain

Use this API when the user is browsing, searching, or filtering data sources — especially if they don't know the exact ID or want to find a list to choose from. This is not the correct API if the user asks for config details of a known data source — use get_data_source in that case.

related tools: get_data_source: Retrieves detailed information about a known data source. Use get_data_source when you want to fetch info about the connection details, authentication settings, or ingestion configuration of a particular data source.

Args: domainIdentifier (str): The identifier of the Amazon DataZone domain in which to list the data sources. Pattern: ^dzd[-][a-zA-Z0-9-]{1,36}$ Required: Yes projectIdentifier (str): The identifier of the project in which to list data sources. Required: Yes connectionIdentifier (str, optional): The ID of the connection used to filter the data sources. environmentIdentifier (str, optional): The identifier of the environment in which to list the data sources. maxResults (int, optional): The maximum number of data sources to return in one response. Valid Range: 1–50 name (str, optional): Filter by name of the data source. Length Constraints: 1–256 characters nextToken (str, optional): A pagination token for fetching the next set of results. Length Constraints: 1–8192 characters status (str, optional): Filter data sources by their current status. Valid values: - CREATING - FAILED_CREATION - READY - UPDATING - FAILED_UPDATE - RUNNING - DELETING - FAILED_DELETION type (str, optional): Filter by the type of data source (e.g., GLUE, REDSHIFT). Length Constraints: 1–256 characters

Returns: dict: A dictionary with the following keys: - items (List[dict]): A list of DataSourceSummary objects containing: - connectionId (str) - createdAt (str) - dataSourceId (str) - description (str) - domainId (str) - enableSetting (str) - environmentId (str) - lastRunAssetCount (int) - lastRunAt (str) - lastRunErrorMessage (dict): Contains "errorDetail" and "errorType" - lastRunStatus (str) - name (str) - schedule (dict): Contains "schedule" and "timezone" - status (str) - type (str) - updatedAt (str)

    - nextToken (str): Token to retrieve the next page of results, if any.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
statusNo
next_tokenNo
max_resultsNo
data_source_typeNo
domain_identifierYes
project_identifierYes
connection_identifierNo
environment_identifierNo
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 discloses return structure and pagination but does not explicitly state that it is read-only or mention any side effects, auth, or rate limits. For a list operation, this is adequate but not exhaustive.

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-structured: purpose sentence, usage guidelines, related tools, parameter descriptions in bullet style, and return info. It is front-loaded with key information and every section is relevant without redundancy.

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 9 parameters and 2 required, no annotations, and no output schema, the description fully covers all needed aspects: purpose, when to use, parameter semantics, and return structure including nested objects. It leaves no major 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%, but the description provides detailed parameter information including types, constraints, patterns, valid values, and required status for each parameter. This adds significant meaning beyond 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 starts with 'Retrieve a list of data sources in Datazone domain', clearly stating the verb and resource. It also distinguishes from the sibling tool 'get_data_source' by specifying when to use each. This meets the highest standard.

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?

The description explicitly states when to use this API ('browsing, searching, or filtering') and when not ('config details of a known data source'), and directs to an alternative tool. It also includes related tools section.

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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