Official description10 sourcesMCP servers8d ago
JordiNei/mcp-databricks-server
๐ โ๏ธ - Run SQL queries, list jobs and get job status via the Databricks API
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claude mcp add mcp-databricks-server -- npx -y mcp-databricks-server
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- Star count
- 50 โ ยท JordiNeil/mcp-databricks-server ยท read from the GitHub search API ยท (checked 2026-10-16 20:00:20)
- Source
- collected from the punkpeye/awesome-mcp-servers list ยท type: MCP server
- Harvest history
- first seen 2026-10-09 ยท last seen 2026-10-16 ยท seen 32ร ยท listed by 10 sources
- Score
- score 1,003 ยท 50 โ ยท 10 sources ยท mcp
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JordiNeil/mcp-databricks-server๏ผfirst catalogued 2026-10-09๏ผlast seen 2026-10-16ใThere is no official certification or quality rating here โ we record sources and dates only.Third-party quality signals
Databricks MCP Server
ไปฅไธๆๅฝ่ช่ฏฅไปๅบ็ README๏ผ็ๆฎต๏ผ ยท ๅจ GitHub ่ฏปๅฎๆด README โ
A Model Context Protocol (MCP) server that connects to Databricks API, allowing LLMs to run SQL queries, list jobs, and get job status.
Features
- Run SQL queries on Databricks SQL warehouses
- List all Databricks jobs
- Get status of specific Databricks jobs
- Get detailed information about Databricks jobs
Prerequisites
- Python 3.7+
- Databricks workspace with:
- Personal access token
- SQL warehouse endpoint
- Permissions to run queries and access jobs
Setup
1. Clone this repository
2. Create and activate a virtual environment (recommended):
3. Install dependencies:
4. Create a .env file in the root directory with the following variables:
5. Test your connection (optional but recommended):
Obtaining Databricks Credentials
1. **Host**: Your Databricks instance URL (e.g., your-instance.cloud.databricks.com)
2. **Token**: Create a personal access token in Databricks:
- Go to User Settings (click your username in the top right)
- Select "Developer" tab
- Click "Manage" under "Access tokens"
- Generate a new token, and save it immediately
3. **HTTP Path**: For your SQL warehouse:
- Go to SQL Warehouses in Databricks
- Select your warehouse
- Find the connection details and copy the HTTP Path
Running the Server
Start the MCP server:
You can test the MCP server using the inspector by running
Available MCP Tools
The following MCP tools are available:
1. **run_sql_query(sql: str)** - Execute SQL queries on your Databricks SQL warehouse
2. **list_jobs()** - List all Databricks jobs in your workspace
3. **get_job_status(job_id: int)** - Get the status of a specific Databricks job by ID
4. **get_job_details(job_id: int)** - Get detailed information about a specific Databricks job
Example Usage with LLMs
When used with LLMs that support the MCP protocol, this server enables natural language interaction with your Databricks environment:
- "Show me all tables in the database"
- "Run a query to count records in the customer table"
- "List all my Databricks jobs"
- "Check the status of job #123"
- "Show me details about job #456"
Troubleshooting
Connection Issues
- Ensure your Databricks host is correct and doesn't include https:// prefix
- Check that your SQL warehouse is running and accessible
Features
- Run SQL queries on Databricks SQL warehouses
- List all Databricks jobs
- Get status of specific Databricks jobs
- Get detailed information about Databricks jobs
Prerequisites
- Python 3.7+
- Databricks workspace with:
- Personal access token
- SQL warehouse endpoint
- Permissions to run queries and access jobs
Setup
1. Clone this repository
2. Create and activate a virtual environment (recommended):
3. Install dependencies:
4. Create a .env file in the root directory with the following variables:
5. Test your connection (optional but recommended):
Obtaining Databricks Credentials
1. **Host**: Your Databricks instance URL (e.g., your-instance.cloud.databricks.com)
2. **Token**: Create a personal access token in Databricks:
- Go to User Settings (click your username in the top right)
- Select "Developer" tab
- Click "Manage" under "Access tokens"
- Generate a new token, and save it immediately
3. **HTTP Path**: For your SQL warehouse:
- Go to SQL Warehouses in Databricks
- Select your warehouse
- Find the connection details and copy the HTTP Path
Running the Server
Start the MCP server:
You can test the MCP server using the inspector by running
Available MCP Tools
The following MCP tools are available:
1. **run_sql_query(sql: str)** - Execute SQL queries on your Databricks SQL warehouse
2. **list_jobs()** - List all Databricks jobs in your workspace
3. **get_job_status(job_id: int)** - Get the status of a specific Databricks job by ID
4. **get_job_details(job_id: int)** - Get detailed information about a specific Databricks job
Example Usage with LLMs
When used with LLMs that support the MCP protocol, this server enables natural language interaction with your Databricks environment:
- "Show me all tables in the database"
- "Run a query to count records in the customer table"
- "List all my Databricks jobs"
- "Check the status of job #123"
- "Show me details about job #456"
Troubleshooting
Connection Issues
- Ensure your Databricks host is correct and doesn't include https:// prefix
- Check that your SQL warehouse is running and accessible
- Source
- awesome ยท https://github.com/JordiNeil/mcp-databricks-server
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