alation_data_product
ChatGPTRender an Alation data product spec as a branded card with contract status, owner, and recordsets. Optionally accepts include_citations=true to render a citation strip beneath the card.
Overview
Alation brings trusted enterprise data context into ChatGPT. Connect ChatGPT to Alation’s enterprise data catalog, governance, and trusted business context so users can discover, understand, and use data with confidence. The Alation Intelligence Operating System (AIOS) helps AI ground responses in trusted enterprise context, including catalog metadata, governance policies, semantic definitions, lineage, data quality, and documentation. The app is designed for enterprise teams that need AI-led data discovery without losing trust, context, or governance.
Tools
alation_document
ChatGPTRender an Alation document hub page (experiment plan, launch notes, incident, policy) as a branded citation card. Use when the model cites a document by id.
alation_glossary_term
ChatGPTRender an Alation glossary term as a branded citation card. Use whenever the model cites a metric definition (MRR, Activation, Churn, etc.) to give the analyst owner, status, canonical source column, and a deep link.
get_custom_fields_definitions_tool
ChatGPTGet Custom Fields Definitions: Get comprehensive custom field definitions for the Alation instance. This tool retrieves custom fields, built-in metadata fields, and built-in search facets with structured metadata and usage guidance. It's designed to help with data dictionary creation, metadata updates, and understanding field capabilities. Args: ctx (RunContext[AAIRunContext]): The run context containing dependencies. Automatically passed in. Returns: ToolReturn: JSON formatted response containing: - custom_fields: List of custom field definitions with essential properties - built_in_fields: List of built-in metadata fields (title, description, steward) - built_in_search_facets: List of built-in search facets (author, owner, etc.) - usage_guide: Comprehensive guidance for using fields in various contexts Raises: ModelRetry: When the API call fails or encounters recoverable errors.
get_data_product_spec_tool
ChatGPTGet Data Product Raw Specification: Get the raw data product specification in the exact format expected by the update API. This returns the complete JSON specification that can be modified and passed to the update_data_product_spec tool. This differs from the get_data_schema tool, because that adds sample values and simplifies the data product for use in question-answering and SQL-generation. This provides the raw JSON. Args: ctx (RunContext[AAIRunContext]): The run context containing dependencies. Automatically passed in. data_product_id: The ID of the data product to fetch. data_product_version: The version of the data product to fetch. If None, extracted from input_payload or uses the default version. Returns: ToolReturn with the complete raw data product specification as a JSON string.
get_data_schema_tool
ChatGPTGet Data Schema: Get the schema for the set of tables the user is asking about. The schema can be useful in a variety of downstream applications. Args: ctx (RunContext[AAIRunContext]): The run context containing dependencies. Automatically passed in. This contains the data product already. data_product_id: The ID of the data product to get the schema for. data_product_version: The version of the data product to get the schema for. If None, extracted from input_payload or uses the default version. pre_exec_sql: An arbitrary SQL query to run in the session before the standard queries. auth_id: ID of the authentication credentials to use. auth_mode: Authentication mode ('datasource_service_account' or None for user credentials). allow_fallback_auth: Whether to allow falling back to service account creds. This is a temporary workaround to avoid having to specify an auth_id and will be removed in the near future. query: An optional query to use for semantic search of sample values.
list_data_products_tool
ChatGPTData Product Search: All listed data products in the marketplace pertaining to the query. Only returns data products that are listed in the marketplace. Args: ctx (RunContext[AAIRunContext]): The run context containing dependencies. Automatically passed in. search_term: This can be the user question directly. It will be used to search for relevant data products. limit: Maximum number of results to return. Default is 5. marketplace_id: The marketplace ID to search in. If not provided, the default marketplace will be used. Returns: ToolReturn with the list of relevant data products.
run_catalog_context_search
ChatGPTCatalog Context Search: Execute the agent with the provided arguments.
run_execute_sql_on_dp
ChatGPTExecute SQL on DP: Execute the agent with the provided arguments.
run_generate_pdf_from_a_chat
ChatGPTGenerate PDF from a Chat: Execute the agent with the provided arguments.
sql_evaluation_tool
ChatGPTRun SQL Evaluation: Run SQL Evaluation on a Data Product's Evaluation Set. If an evaluation result matching the current data product's hash exists, we return that result. Otherwise, it starts an evaluation run if one is not currently running, then waits until that run completes and returns the result. Args: ctx: The run context (automatically injected) data_product_id: The data product ID to evaluate post_eval_results_back_to_chat: Whether to continue the chat asynchronously once evaluation is complete, posting the results back to the chat. ONLY for use by DP_SUGGEST_FIXES_JOB agent, since this will force the chat stream to end here. agent_config_id: The agent configuration ID (UUID) to use for evaluation. Hidden from the model, only for user overrides. auth_id: ID of the authentication credentials to use for the query. pre_exec_sql: An arbitrary SQL query to run in the session before the standard queries. data_product_version: The version of the data product to evaluate. If None, extracted from input_payload or uses the default version. Returns: ToolReturn with structured evaluation results showing passed and failed cases
sql_execution_tool
ChatGPTSQL Execution: Execute the given SQL query or return an error message. The resulting table will be serialized and returned. The serialization may drop rows and columns if it is determined the result is too large. To see more of the result, please adjust the query by looking at fewer columns and or rows. Or apply a limit/offset to the query. Args: ctx (RunContext[AAIRunContext]): The run context containing dependencies. Automatically passed in. data_product_id: The ID of the data product to execute the query on. sql: The SQL query to execute. result_table_name: The name of the result table of the sql query after execution. This is for understanding table semantics. data_product_version: The version of the data product to use. If None, extracted from input_payload or uses the default version. pre_exec_sql: An arbitrary SQL query to execute within the session before the normal query. auth_id: ID of the authentication credentials to use for the query. auth_mode: Authentication mode ('datasource_service_account' or None for user credentials). allow_fallback_auth: Whether to allow falling back to service account creds. This is a temporary workaround to avoid having to specify an auth_id and will be removed in the near future.
update_data_product_tool
ChatGPTUpdate an Existing Data Product: Update an existing data product using JSON Patch operations (RFC 6902). This tool fetches the current data product specification, applies your patches, validates the result, and saves it. Use JSON Patch operations to make targeted changes without regenerating the entire specification. Args: ctx (RunContext[AAIRunContext]): The run context containing dependencies. Automatically passed in. data_product_id: The ID of the data product to update. patches: A list of JSON Patch operations to apply. Each operation is a dict with: - op: The operation type ("add", "remove", "replace", "move", "copy", "test") - path: JSON Pointer to target location (e.g., "/product/en/description") - value: Value for add/replace/test operations - from: Source path for move/copy operations Common examples: - Update description: {"op": "replace", "path": "/product/en/description", "value": "New description"} - Add a metric: {"op": "add", "path": "/product/metadata/metrics/revenue", "value": {"displayName": "Revenue", "description": "...", "expression": "..."}} - Update column description: {"op": "replace", "path": "/product/recordSets/users/schema/0/description", "value": "User ID"} - Remove a field: {"op": "remove", "path": "/product/recordSets/orders/schema/2"} PROTECTED: Cannot modify /product/productId, /product/version, or /product/deliverySystems - these are managed by the system. data_product_version: The version of the data product to update. If None, extracted from input_payload or uses the default version. Returns: ToolReturn with the result of the update operation.
Capabilities
WritesInteractive
Links
App Stats
13
Tools
ChatGPT
Platforms
Works with
ChatGPT
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