🚀 MCP Databricks

Created By
leminkhoaa year ago
My Databricks MCP server to interact with Databricks through LLM models
Overview

What is MCP Databricks?

MCP Databricks is a powerful integration that connects AI assistants to Databricks workspaces using the Model Context Protocol (MCP). It allows for efficient management of Databricks environments through AI-driven commands.

How to use MCP Databricks?

To use MCP Databricks, clone the repository from GitHub, configure your environment variables with your Databricks credentials, and choose your installation method (Docker or local installation). Start the server and connect it to your preferred MCP client.

Key features of MCP Databricks?

  • Manage compute resources and clusters with precision.
  • Execute SQL queries and analyze results.
  • Organize and manipulate workspace objects.
  • Comprehensive toolkit for library and command execution management.

Use cases of MCP Databricks?

  1. Automating cluster management tasks in Databricks.
  2. Executing SQL commands for data analysis.
  3. Managing libraries and workspace objects efficiently.

FAQ from MCP Databricks?

  • What are the prerequisites for using MCP Databricks?

You need Python 3.11 or higher, a Databricks workspace, and a Databricks Personal Access Token (PAT).

  • Can I use MCP Databricks without Docker?

Yes, you can install it locally using the provided instructions.

  • What is the Model Context Protocol (MCP)?

MCP is a protocol that facilitates communication between AI assistants and Databricks workspaces.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
leminkhoa
Star
0
Language
Python
License
-

Recommend Servers

View All
//beforeyouship — LLM Cost Modeling From Your Editor
@Indiegoing

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

2 days ago
Numox

8 hours ago