DataHub Agent Context
MCP tools for AI agents to search and query your DataHub metadata catalog — works with Claude, Cursor, Copilot, and any MCP-compatible AI assistant.
What you can do
- Search datasets, dashboards, pipelines, and other data assets by name or description
- Retrieve entity details — schema, lineage, ownership, tags, glossary terms, and more
- Trace lineage upstream and downstream across your data assets
- Mutate metadata — update descriptions, tags, owners, domains, and glossary terms
- Build LangChain or Google ADK agents with pre-built tool bindings
- Set up Snowflake AI agents with one CLI command
Installation
pip install datahub-agent-context
# With LangChain support
pip install "datahub-agent-context[langchain]"
Quickstart
LangChain agent
from datahub.sdk.main_client import DataHubClient
from datahub_agent_context.langchain_tools import build_langchain_tools
client = DataHubClient.from_env()
# Read-only tools (search, lineage, entity details)
tools = build_langchain_tools(client, include_mutations=False)
# Include write tools (tags, descriptions, owners, etc.)
tools = build_langchain_tools(client, include_mutations=True)
# DataHub Cloud: add Ask DataHub AI assistant
from datahub_agent_context.langchain_tools import build_langchain_cloud_tools
tools += build_langchain_cloud_tools(client, ask_datahub=True)
Snowflake AI agent setup
datahub agent create snowflake \
--datahub-url https://your-datahub-instance \
--datahub-token your-token
Available tools
Search — search(), search_documents(), grep_documents()
Entities — get_entities(), list_schema_fields()
Lineage — get_lineage(), get_lineage_paths_between()
Queries — get_dataset_queries()
Mutations — add_tags(), remove_tags(), update_description(), set_domains(), add_owners(), add_glossary_terms(), add_structured_properties(), save_document()
Cloud-only — ask_datahub_chat() (DataHub Cloud AI assistant)
Links
1 maintainer
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