Databricks API에 연결하는 모델 Context Protocol (MCP) 서버는 SQL 쿼리, 목록 작업 및 작업 상태를 실행하는 LLM을 허용한다.

특징

• Databricks SQL 창고에서 SQL 쿼리 실행
• 모든 Databricks 일자리 목록
• 특정 Databricks 작업의 상태를 얻으십시오.
• Databricks 작업에 대한 자세한 정보 얻기

필수품

• 파이썬 3.7+
• Databricks 작업 공간:
• 개인 액세스 토큰
• SQL 창고 엔드포인트
• 쿼리 및 액세스 작업을 실행하는 권한

설치

1. Clone 이 저장소

2. 가상 환경(recommended) 만들기 및 활성화:

3. 의존성 설치:

4. 다음 변수와 루트 디렉토리에 .env 파일을 만듭니다.

5. 당신의 연결을 시험하십시오 (선택 그러나 추천되는):

Databricks Credentials 획득

1. Host: 데이터bricks 인스턴스 URL (예: your-instance.cloud.databricks.com)
2. Token: Databricks의 개인 액세스 토큰 만들기:

• 사용자 설정으로 이동 ( 상단의 사용자 이름을 클릭)
• "Developer"탭 선택
• "액세스 토큰"에서 "Manage"를 클릭하십시오
• 새로운 토큰 생성 및 즉시 저장

3. ** HTTP 경로 **: SQL 창고에 대 한:

• Databricks에서 SQL 창고로 이동

Databricks MCP Server

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):

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Create a .env file in the root directory with the following variables:

   DATABRICKS_HOST=your-databricks-instance.cloud.databricks.com
   DATABRICKS_TOKEN=your-personal-access-token
   DATABRICKS_HTTP_PATH=/sql/1.0/warehouses/your-warehouse-id
   

5. Test your connection (optional but recommended):

   python test_connection.py
   

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:

python main.py

You can test the MCP server using the inspector by running

npx @modelcontextprotocol/inspector python3 main.py

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
• Verify your personal access token has the necessary permissions
• Run the included test script: python test_connection.py

Security Considerations