yashshingvi/databricks-genie-MCP
by Various
๐ โ๏ธ - A server that connects to the Databricks Genie API, allowing LLMs to ask natural language questions, run SQL queries, and interact with Databricks conversational agents.
MCP
yashshingvi/databricks-genie-MCP
Added 1 June 2026
Overview
A Python server that connects to the Databricks Genie API, enabling LLMs to query data using natural language, run SQL queries, and interact with Databricks conversational agents. It implements the Model Context Protocol (MCP) to bridge AI models with Databricks analytics.
Best for
Best for
Developers building LLM-powered tools for querying and analyzing Databricks data
Use cases
- Querying Databricks data with natural language via an LLM
- Building AI-driven data analysis assistants for Databricks
- Integrating conversational agents with Databricks Genie spaces
Notes
A Python server that connects to the Databricks Genie API, enabling LLMs to query data using natural language, run SQL queries, and interact with Databricks conversational agents. It implements the Model Context Protocol (MCP) to bridge AI models with Databricks analytics.
16 stars on GitHub. Last updated 2025-04-18. Licensed MIT.
Use cases
- Querying Databricks data with natural language via an LLM
- Building AI-driven data analysis assistants for Databricks
- Integrating conversational agents with Databricks Genie spaces
Pros
- Direct natural language access to Databricks data without writing SQL
- Leverages the existing Databricks Genie API and MCP standard
- Python-based, easy to extend or deploy as a server
Cons
- Low community adoption (16 stars) may mean limited support or documentation
- Requires access to Databricks Genie API, which is not universally available
- Niche use case focused solely on Databricks conversational agents
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Direct natural language access to Databricks data without writing SQL
- Leverages the existing Databricks Genie API and MCP standard
- Python-based, easy to extend or deploy as a server
Cons
- Low community adoption (16 stars) may mean limited support or documentation
- Requires access to Databricks Genie API, which is not universally available
- Niche use case focused solely on Databricks conversational agents
Pairs with
Other entries in the index that connect to this one. Click through to see the chain.